#  InfoSync: Information Synchronization across Multilingual Semi-structured
Tables

Siddharth Khincha1, Chelsi Jain2, Vivek Gupta3†, Tushar Kataria3†, Shuo Zhang4
1IIT Guwahati, 2CTAE, Udaipur, 3University of Utah, 4Bloomberg,
<EMAIL_ADDRESS><EMAIL_ADDRESS>
{vgupta<EMAIL_ADDRESS><EMAIL_ADDRESS>
Corresponding Author Equal Contribution

###### Abstract

Information Synchronization of semi-structured data across languages is
challenging. For instance, Wikipedia tables in one language should be
synchronized across languages. To address this problem, we introduce a new
dataset InfoSync and a two-step method for tabular synchronization. InfoSync
contains 100K entity-centric tables (Wikipedia Infoboxes) across 14 languages,
of which a subset ($\sim$3.5K pairs) are manually annotated. The proposed
method includes 1) Information Alignment to map rows and 2) Information Update
for updating missing/outdated information for aligned tables across
multilingual tables. When evaluated on InfoSync, information alignment
achieves an F1 score of 87.91 (en $\leftrightarrow$ non-en). To evaluate
information updation, we perform human-assisted Wikipedia edits on Infoboxes
for 603 table pairs. Our approach obtains an acceptance rate of 77.28$\%$ on
Wikipedia, showing the effectiveness of the proposed method.

##  1 Introduction

English articles across the web are more timely updated than other languages
on particular subjects. Meanwhile, culture differences, topic preferences, and
editing inconsistency lead to information mismatch across multilingual data,
e.g., outdated information or missing information Jang et al. (2016); Nguyen
et al. (2018). Online encyclopedia, e.g., Wikipedia, contains millions of
articles that need to be updated constantly, involving expanding existing
articles, modifying content such as correcting facts in sentences (Shah et
al., 2019) and altering Wikipedia categories (Zhang et al., 2020b). However,
more than 40% of Wikipedia’s active editors are in English. At the same time,
only 15% of the world population speak English as their first language.
Therefore, information in languages other than English may not be as updated
Bao et al. (2012). See Figure 1 for an example of an information mismatch for
the same entity across different languages. In this work, we look at
synchronizing information across multilingual content.

Figure 1: Janaki Ammal Infoboxes in English (right) and Hindi (left). Hindi
Table lacks the "British Rule of India" as a cultural context. Two value
mismatches (a) The Hindi table doesn’t list Died key’s state (b) Institution
values differ. The Hindi table mentions "residence" while the English table
doesn’t. Hindi Table is missing Thesis, Awards, and Alma Mater keys. Both
don’t mention parents, early education, or honors.

To overcome the above-mentioned problem, we formally introduce the task of
Information Synchronization for multilingual articles, which includes
paragraphs, tables, lists, categories, and images. But due to its magnitude
and complexity, synchronizing all of the information across different
modalities on a webpage is daunting. Therefore, this work focuses on semi-
structured data, a.k.a. table synchronization in a few languages, as the first
step toward our mission.

We consider Infobox, a particular type of semi-structured Wikipedia tables
(Zhang and Balog, 2020a), which contain entity-centric information, where we
observe various information mismatches, e.g., missing rows (cf. Figure 1). One
intuitive idea to address them is translation-based. However, the Infoboxes
contain rows with implicit context; translating these short phrases is prone
to errors and leads to ineffective synchronization Minhas et al. (2022). To
systematically assess the challenge, we curate a dataset, namely InfoSync,
consisting of 100K multilingual Infobox tables across 14 languages and
covering 21 Wikipedia categories. $\sim$3.5K table pairs of English to non-
English or non-English to non-English are sampled and manually synchronized.

We propose a table synchronization approach that comprises two steps:  (1.)
Information Alignment:align table rows, and  (2.) Information Update:update
missing or outdated rows across language pairs to circumvent the
inconsistency.  The information alignment component aims to align the rows in
multilingual tables. The proposed method uses corpus statistics across
Wikipedia, such as key and value-based similarities.

The information update step relies on an effective rule-based approach. We
manually curate nine rules: row transfer, time-based, value trends, multi-key
matching, append value, high to low resource, number of row differences, and
rare keys. Both tasks are evaluated on InfoSync to demonstrate their
effectiveness. Apart from the automatic evaluation, we deploy an online
experiment that submits the detected mismatches by our method to Wikipedia
after strictly following Wikipedia editing guidelines. We monitor the number
of accepted and rejected edits by Wikipedia editors to demonstrate its
efficacy. All proposed edits are performed manually, in accordance with
Wikipedia’s editing policies and
guidelines111https://en.wikipedia.org/wiki/Wikipedia:List_of_policies_and_guidelines,
rule set222https://en.wikipedia.org/wiki/Wikipedia:Simplified_ruleset, and
policies333https://en.m.wikipedia.org/wiki/Wikipedia:Editing_policy. These
changes were subsequently accepted by Wikipedia editors, demonstrating the
efficacy of our methodology.

The contributions in this work are as follows: 1) We investigate the problem
of Information Synchronization across multilingual semi-structured data, i.e.,
tables, and construct a large-scale dataset InfoSync; 2) We propose a two-step
approach (alignment and updation) and demonstrate superiority over exiting
baselines; 3) The rule-based updation system achieves excellent acceptance
when utilized for human-assisted Wikipedia editing. Our InfoSync dataset and
method source code are available at https://info-sync.github.io/info-sync/.

##  2 Motivation

###  2.1 Challenges in Table Synchronization

We observe the following challenges when taking Wikipedia Infoboxes as a
running example. Note this is not an exhaustive list.

_MI: Missing Information_ represents the problem where information appears in
one language and is missing in others. This may be due to the fact that the
table is out-of-date or to cultural, social, or demographic preferences for
modification (cf. Figure 1).

_OI: Outdated Information_ denotes that information is updated in one language
but not others.

_IR: Information Representation_ varies across languages. For example, one
attribute about "parents" can be put in a single row or separate rows
("Father" and "Mother").

_UI: Unnormalized Information_ presents cases where table attributes can be
expressed differently. For example, "known for" and "major achievements" of a
person represent the same attribute (i.e., paraphrase).

_LV: Language Variation_ means that information is expressed in different
variants across languages. This problem is further exaggerated by the implicit
context in tables when translating. E.g., "Died" in English might be
translated to "Overleden" (Pass Away) or "overlijdensplaats" (Place of Death)
in Dutch due to missing context.

_SV: Schema Variation_ denotes that the schema (template structure) varies.
For example, extraction of "awards" in Musician tables can be harrowing due to
dynamic on-click lists (_Full Award Lists_).

_EEL: Erroneous Entity Linking_ is caused by mismatched linkages between table
entities among multiple languages, e.g., "ABV" and "Alcohol by Volume".

###  2.2 Wikipedian "Biases"

Wikipedia is a global resource across over 300 languages. However, the
information is skewed toward English-speaking countries Roy et al. (2020) as
English has the most significant Wikipedia covering 23% (11%) of total pages
(articles). Most users’ edits (76%) are also done in English Wikipedia.
English Wikipedia also has the highest number of page reads (49%) and page
edits (34%), followed by German (20% and 12%) and Spanish (12% and 6%),
respectively. Except for the top 25 languages, the total number of active
editors, pages, and edits is less than 1% Warncke-Wang et al. (2012); Alonso
and Robinson (2016).

Multilingual Wikipedia articles evolve separately due to cultural and
geographical bias Callahan and Herring (2011); REAGLE and RHUE (2011); Tinati
et al. (2014), which prevents information synchronization. For example,
information on "Narendra Modi" (India’s Prime Minister) is more likely to be
better reflected in Hindi Wikipedia than in other Wikipedias. This means that
in addition to the obvious fact that smaller Wikipedias can be expanded by
incorporating content from larger Wikipedias, larger Wikipedias can also be
augmented by incorporating information from smaller Wikipedias. Thus,
information synchronization could assist Wikipedia communities by ensuring
that information is consistent and of good quality across all language
versions.

##  3 The InfoSync Dataset

To systematically assess the challenge of information synchronization and
evaluate the methodologies, we aim to build a large-scale table
synchronization dataset InfoSync based on entity-centric Wikipedia Infoboxes.

###  3.1 Table Extraction

We extract Wikipedia Infoboxes from pages appearing in multiple languages on
the same date to simultaneously preserve Wikipedia’s original information and
potential discrepancies. These extracted tables are across 14 languages and
cover 21 Wikipedia categories.

Languages Selection. We consider the following languages English(en),
French(fr), German(de), Korean(ko), Russian(ru), Arabic(ar), Chinese(zh),
Hindi(hi), Cebuano(ceb), Spanish(es), Swedish(sv), Dutch(nl), Turkish(tr), and
Afrikaans(ak). We extracted tables across 14 languages and covered 21 diverse
Wikipedia categories. In these 14 languages, four are low resource (af, ceb,
hi, tr) < 6000, seven of them medium resource (ar, ko,nl, sv, zh,ru, de,es)
(6000–10000), and the remaining one are high resource (en, en, fr), w.r.t. to
the number of infobox total tables (see Table 1 in paper). Our choices were
motivated by the following factors:- a) Cover all the continents, thus
covering the majority and diverse population. Out of chosen languages, 7
(English, French, German, Spanish, Swedish, Dutch, and Turkish) are European.
b). They have sufficient pages with info boxes; each entity info box is
present in at least five languages, and c) an adequate number of rows (5 and
above) facilitates better data extraction.

Categories. Extracted tables cover twenty-one simple, diverse, and popular
topics: Airport, Album, Animal, Athlete, Book, City, College, Company,
Country, Food, Monument, Movie, Musician, Nobel, Painting, Person, Planet,
Shows, and Stadiums. We observe that Airport has the most number of entity
tables followed by Movie and Shows, as shown in Table 10. Other extraction
details are provided in Appendix A.1.

###  3.2 Tabular Information Mismatched

Ln | Average Table Transfer % | Language Statistics
---|---|---
C1 | C1 $\rightarrow$ $\sum_{L}ln$ |  $\sum_{L}ln$ $\rightarrow$ C1 |  $\\#$ Tables | AR
af | 17.46 | 400.5 | 1575 | 9.91
ar | 34.02 | 27.38 | 7648 | 13.01
ceb | 42.87 | 134.88 | 3870 | 7.82
de | 40.73 | 27.12 | 8215 | 7.88
en | 45.85 | 0.32 | 12431 | 12.60
es | 38.78 | 9.00 | 9920 | 12.59
fr | 41.25 | 4.73 | 10858 | 10.30
hi | 18.39 | 358.97 | 1724 | 10.91
ko | 31.13 | 40.51 | 6601 | 9.35
nl | 33.69 | 24.6 | 7837 | 10.46
ru | 36.98 | 14.54 | 9066 | 11.41
sv | 35.53 | 24.62 | 7985 | 9.89
tr | 28.99 | 59.33 | 5599 | 10.14
zh | 32.16 | 32.71 | 7140 | 12.43
Table 1:  Average Table Transfer:- Column 2 shows the average number of tables
missing in other languages which can be transferred from C1. Column 3 shows
the average number of tables missing in C1, which we can transfer from all
languages to C1. Here $L$ is the set of all languages ($ln$) except source or
transfer language. Language Statistics:- The number of tables and average rows
(AR) per table across different categories for each language.

We analyze the extracted tables in the context of the synchronization problem
and identify the information gap. The number of tables is biased across
languages, as shown in Table 1. We observe Afrikaans, Hindi, and Cebuano have
a significantly less number of tables. Similarly, the table size is biased
across several languages. Dutch and Cebuano have the last rows. In addition,
the number of tables across categories is uneven; refer to Table 2. Airport
and Movie have the highest number of tables. Table 2 also reports the average
number of rows for a category. Planet, Company, and Movie have the highest
average number of rows.

Topic |  $\\#$ Tables | AR | Topic |  $\\#$ Tables | AR
---|---|---|---|---|---
Airport | 18512 | 9.66 | Diseases | 3973 | 6.03
Food | 6184 | 7.93 | Monument | 1550 | 9.71
Album | 5833 | 7.58 | Medicine | 2516 | 15.20
Animal | 3304 | 8.27 | Movie | 12082 | 13.29
Athlete | 3209 | 9.09 | Musician | 2729 | 9.53
Book | 1550 | 9.99 | Nobel | 9522 | 9.84
Painting | 3542 | 7.05 | Country | 3338 | 22.85
City | 3088 | 14.45 | Person | 2252 | 11.87
College | 1857 | 11.01 | Planet | 1233 | 16.80
Company | 2225 | 13.85 | Shows | 5644 | 13.86
Stadium | 6326 | 10.94 |  |  |
Table 2: Category Statistics :- Number of tables in each category and average
number of rows (AR) across different languages.

When synchronizing a table from one language to another, we observe that the
maximum number of tables can be transferred from English, French, and Spanish
from Column 1 in Table 1. Afrikaans, Hindi, and Cebuano have the least
overlapping information (Column 3) with all other languages. The number of
rows (Column 5) varies substantially between languages, with Spanish and
Arabic having the highest number.

###  3.3 InfoSync Evaluation Benchmark

We construct the evaluation benchmark by manually mapping the table’s pairs in
two languages. The table pairs we consider can be broadly split into English
$\leftrightarrow$ Non-English and Non-English $\leftrightarrow$ Non-English.
The annotations are conducted as follows.

#### English $\leftrightarrow$ Non-English:

We sample 1964 table pairs, where a minimum of 50 pairs for each category and
language are guaranteed. We divide the annotated dataset, ratio of $1:2$, into
validation and test sets. The non-English tables are translated into English
first and then compared against the English version. Furthermore, native
speakers annotated 200 table pairs for English $\xleftrightarrow{*}$ Hindi and
English $\xleftrightarrow{*}$ Chinese to avoid minor machine translation
errors.

#### Non-English $\leftrightarrow$ Non-English:

We consider six non-English languages: two from each High resource (French,
Russian), Medium Resource (German, Korean), and Low Resource (Hindi, Arabic),
w.r.t. the number of tables in InfoSync. We sample and annotate 1589 table
pairs distributed equally among these languages, where we choose an average of
$\sim 50$ tables for all pairs of languages. Both are translated into English
before manually mapping them.

In addition, for more detailed analysis, we also annotate metadata around
table synchronization challenges such as MI, IR, LV, OI, UI, SV, and EEL, as
discussed in §2.1.

##  4 Table Synchronization Method

This section will explain our proposed table synchronization method for
addressing missing or outdated information. This method includes two steps:
information alignment and update. The former approach aims to align rows
across a pair of tables, and the latter helps to update missing or outdated
information. We further deploy our update process in a human-assisted
Wikipedia edit framework to test the efficacy in the real world.

###  4.1 Information Alignment

An Infobox consists of multiple rows where each row has a key and value pair.
Given a pair of tables $T_{x}=[...,(k_{x}^{i},v_{x}^{i}),...]$ and
$T_{y}=[...,(k_{y}^{j},v_{y}^{j}),...]$ in two languages, table alignment aims
to align all the possible pairs of rows, e.g., $(k_{x}^{i},v_{x}^{i})$ and
$(k_{y}^{j},v_{y}^{j})$ refer to the same information and should be aligned.

We propose a method that consists of five modules, each of which relaxes
matching requirements in order to create additional alignments.

_M1. Corpus-based._ The pair of rows $(k_{x},v_{y})$ in $T_{x}$ and
$(k_{y},v_{y})$ in $T_{y}$ are supposed to be aligned if
$cosine(em({tr_{x}^{en}(k_{x}})),em(tr_{y}^{en}({k_{y}})))>\theta_{1}$, where
$em$ is the embedding, $\theta_{1}$ is the threshold, and $tr_{y}^{en}()$
denotes the English translation of $k$ if $k$ is not in English. In order to
achieve accurate key translations, we adopt a majority voting approach,
considering multiple translations of the same key from different category
tables. We consider the key’s values and categories as additional context for
better translation during the voting process. To simplify the voting
procedure, we pre-compute mappings by selecting only the most frequent keys
for each category across all languages.

_M2. Key-only._ This module attempts to align the unaligned pairs in module 1.
Using their English translation, it first computes cosine similarity for all
possible key pairs. $k^{x}$ will be aligned to $k^{y}$ only if they are
mutually most similar key and the similarity is above a certain threshold
$\theta_{2}$.

This is similar to maximum bipartite matching, treating similarity scores as
edge weights followed by threshold-based pruning. And it ensures we are
capturing the highest similarity mapping from both language directions. Note
that here we use only keys as the text for similarity computation.

_M3. Key value bidirectional._ This module is similar to step 2, except it
uses the entire table row for computing similarities, i.e., key + value, using
threshold $\theta_{3}$.

_M4. Key value unidirectional._ This module further relaxes the bidirectional
mapping constraint in step 3, i.e., thus removing the requirement of the
highest similarity score matching from both sides. We shift to unidirectional
matching between row pairs, i.e., consider the highest similarity in either
direction. However, this may result in adding spurious alignments. To avoid
this, we have a higher threshold ($\theta_{4}$) than the prior step.

_M5. Multi-key._ Previous modules only take the most similar key for alignment
if exceeding the threshold. In this module, we further relax the constraint to
select multiple keys (maximum two), given exceeding a threshold
($\theta_{5}$). Multi-key mapping is sparse, but the above procedure will lead
to dense mapping. To avoid this, we introduce a soft constraint for value-
combination alignment, where multi-key values are merged. We consider valid
multi-key alignment when the merge value-combination similarity score exceeds
that of the most similar key.

The thresholds of five modules are tuned in the sequence as stated above.

Figure 2: Explanation of Alignment Performance Metrics:- $\mathbf{T}_{en}$ and
$\mathbf{T}_{hi}$ is a set of all rows in English and Hindi Table,
respectively. $\mathbf{R}_{x}^{n}$ represent $n^{th}$ row in the table of
language $x$. $\mathbf{R}_{x}(\mathbf{X})$ returns all the rows in language
$x$ using mapping $\mathbf{X}$. $|.|$ represents the cardinality of the set.
$\mathbf{G}$ is a set of all manual alignments. Each alignment is stored as a
tuple ${\mathbf{ga}}_{i}$ = $(\mathbf{R}_{x}^{m},\mathbf{R}_{x}^{n})$.
$\mathbf{P}$ is a set of predicted alignments (can see there are mistakes in
the alignment).

###  4.2 Information Updation

Information modification includes Row Append (adding missing rows), Row Update
(replacing or adding values), and Merge Rows. We propose a rule-based
heuristic approach for information updates. The rules are in form of logical
expression ($\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}})}$ L $\mapsto$ R)
applied on infobox tables, where, R${}_{T_{x}}$ and R${}_{T_{y}}$ represent
table rows for language $x$ and $y$ respectively. These rules are applied
sequentially according to their priority rank (P.R.). Rules explanations are
described below.

_R1. Row Transfer._ Following the logistic rule of

$\displaystyle\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}})}\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};\text{R}_{T_{y}})=0$
$\displaystyle\mapsto{T_{y}\cup
tr{{}_{x}^{y}}(\text{R}_{T_{x}})}\bigwedge\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};tr{{}_{x}^{y}}(\text{R}_{T_{x}}))=1$

, where $\text{Al}^{T_{y}}_{T_{x}}(.;.)$ represents the alignment mapping
between two tables ${T_{y}}$ and ${T_{x}}$. Unaligned rows are transferred
from one table to another.

_R2. Multi-Match._

We update the table by removing multi-alignments and replacing them with
merged information to handle multikey alignments.

_R3. Time-based._ We update aligned values using the latest timestamp.

_R4. Trends (positive/negative)._ This update applies to cases where the value
is highly likely to follow a monotonic pattern (increasing or decreasing)
w.r.t. time, e.g., athlete career statistics. The authors curated the
positive/negative trend lists.

_R5. Append Values._ Additional value information from an up-to-date row is
appended to the outdated row.

_R6. HR to LR._ This rule transfers information from high to low resource
language to update outdated information.

_R7. $\\#$Rows._ This rule transfers information from bigger (more rows) to
smaller (fewer rows) tables.

_R8. Rare Keys (Non Popular)._ We update information from the table where non-
popular keys are likely to be added recently to the outdated table. The
authors also curate non-popular keys.

Detailed formulation of logical rules and their priority ranking are listed in
Table 3. Figure 3 in Appendix shows an example of table update.

P.R. | Rule Name | Logical Rule $\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}})}$ L $\mapsto$ R | Update Type
---|---|---|---
1 | Row Transfer | $\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}})}\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};\text{R}_{T_{y}})=0$ | Row Addition
|  |  $\mapsto{T_{y}\cup tr{{}_{x}^{y}}(\text{R}_{T_{x}})}$ $\bigwedge$ $\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};tr{{}_{x}^{y}}(\text{R}_{T_{x}}))=1$ |
2 | Multi-Match | $\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}})}(\sum_{\text{R}_{T_{y}}}\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};\text{R}_{T_{y}}))>1$ | Row Delete
|  | $\mapsto\\{T_{y}\setminus\cup_{(\forall_{\text{R}_{T_{y}}}\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};\text{R}_{T_{y}})=1)}\text{R}_{T_{y}}\\}\bigcup tr{{}_{x}^{y}}(\text{R}_{T_{x}})\bigwedge\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};tr{{}_{x}^{y}}(\text{R}_{T_{x}}))=1$ |
3 | Time-based | $\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}})}\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};\text{R}_{T_{y}})=1\bigwedge(\text{isTime}(\text{R}_{T_{x}},\text{R}_{T_{y}})=1)$ | Value Substitute
|  | $\bigwedge(\text{exTime}(\text{R}_{T_{x}})>\text{exTime}(\text{R}_{T_{y}}))\mapsto\text{R}_{T_{y}}\leftarrow tr{{}_{x}^{y}}(\text{R}_{T_{x}})$ |
4 | Positive Trend | $\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}},\text{PosTrend})}\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};\text{R}_{T_{y}})=1\bigwedge\text{exKey}(\text{R}_{T_{x}})\in\text{PosTrend}$ | Value Substitute
| or | $\bigwedge\text{R}_{T_{x}}>\text{R}_{T_{y}}\mapsto\text{R}_{T_{y}}\leftarrow\text{R}_{T_{x}}$ |
| Negative Trend | $\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}},\text{NegTrend})}\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};\text{R}_{T_{y}})=1\bigwedge\text{exKey}(\text{R}_{T_{x}})\in\text{NegTrend}$ | Value Substitute
|  | $\bigwedge\text{R}_{T_{x}}<\text{R}_{T_{y}}\mapsto\text{R}_{T_{y}}\leftarrow\text{R}_{T_{x}}$ |
5 | Append Value | $\text{R}_{T_{x}}=\text{V}\bigwedge\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}})}\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};\text{R}_{T_{y}})=1\bigwedge|\text{R}_{T_{x}}[k]|>|\text{R}_{T_{y}}[k]|$ | Value Addition
|  | $\mapsto\forall_{(v\in\text{R}_{T_{x}}[k]~{}\wedge~{}\notin tr{{}_{x}^{y}}(\text{R}_{T_{x}}[k]))}\text{R}_{T_{y}}\leftarrow\text{R}_{T_{y}}\cup tr{{}_{x}^{y}}(v)$ |
6 | HR to LR | $({T_{x}},{T_{y}})\in(HR,LR)\bigwedge\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}})}\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};\text{R}_{T_{y}})=1$ | Value Substitute
|  | $\bigwedge tr{{}_{x}^{en}}(\text{R}_{T_{x}})\neq tr{{}_{y}^{en}}(\text{R}_{T_{y}})\mapsto\text{R}_{T_{y}}\leftarrow tr{{}_{x}^{y}}(\text{R}_{T_{x}})$ |
7 |  $\\#$ Rows | $|T_{x}|>>|T_{y}|\bigwedge\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}})}\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};\text{R}_{T_{y}})=1\bigwedge tr{{}_{x}^{en}}(\text{R}_{T_{x}})\neq tr{{}_{y}^{en}}(\text{R}_{T_{y}})$ | Value Substitute
|  | $\mapsto\text{R}_{T_{y}}\leftarrow tr{{}_{x}^{y}}(\text{R}_{T_{x}})$ |
8 | Rare Keys | $\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}},\text{RarKeys})}\text{Al}^{T_{y}}_{T_{x}}(\text{R}_{T_{x}};\text{R}_{T_{y}})=1\bigwedge\text{tr}^{en}_{x}(\text{R}_{t_{x}})\neq\text{tr}^{en}_{y}(\text{R}_{t_{y}})$ | Value Substitute
|  | $\bigwedge\forall_{(\text{R}_{T_{x}},\text{R}_{T_{y}}}|\text{exKey}(\text{R}_{T_{x}})\in\text{RarKey}|>|\text{exKey}(\text{R}_{T_{y}})\in\text{RarKey}|\mapsto\text{R}_{T_{y}}\leftarrow\text{R}_{T_{x}}$ |

Table 3: Logical Rules for Information Updation. Notation:- $T_{z}$ represents
a table in language $z$, $\text{R}_{T_{z}}$

represents a row of the table. In $\text{R}_{T_{z}}[k]=v$, $k$,$v$ represent
key and value pair. For $\text{R}_{T_{z}}[k]=V$, $V$ denotes value list mapped
to a key $k$. $\text{Al}^{T_{y}}_{T_{x}}(.;.)$ represents the alignment
mapping between two tables ${T_{y}}$ and ${T_{x}}$. Translation between two
languages($p$ and $q$) is represented by $tr^{p}_{q}(.)$. _exKey_ extract key
from a table row. _isTime_ is true if the row has time entry. _exTime_ extract
time from table row. _PosTrend/NegTrend_ represent list of keys whose value
always increase or decrease with time. _RarKey_ represent set of keys are
least frequent in the corpora.

#### Human-assisted Wikipedia Infobox Edits:

We apply the above rules to assist humans in updating Wikipedia infoboxes.
Following Wikipedia edit
guidelines444https://en.wikipedia.org/wiki/Wikipedia:List_of_policies_and_guidelines,
rule set555https://en.wikipedia.org/wiki/Wikipedia:Simplified_ruleset, and
policies666https://en.m.wikipedia.org/wiki/Wikipedia:Editing_policy, we append
our update request with a description to provide evidence, which contains  (a)
up-to-date entity page URL in the source language,  (b) exact table rows
information, the source language, and the details of the changes,  (c) and one
additional citation discovered by the editor for extra validation. 777We use a
search engine such as Google, Bing, you.com, perplexity.ai, find, etc. for
additional citation. We further update beyond our heuristic-based rules but
are aligned through our information alignment method.

##  5 Experiments

Our experiments assess the efficacy of our proposed two-stage approach by
investigating the following questions.

\- What is the efficacy of the unsupervised multilingual method for table
alignment? (§5.2)

\- How significant are the different modules of the alignment algorithm? (§5.2
and §A.6)

\- Does the rule-based updating approach effective for information
synchronization? (§5.3)

\- Can the two-step approach assist humans in updating Wikipedia Infoboxes?
(§5.3)

Method |  | Match |  |  | UnMatch |
---|---|---|---|---|---|---
|  $T_{en}$ $\leftrightarrow$ $T_{x}$ |  $T_{x}$ $\leftrightarrow$ $T_{y}$ |  $T_{en}$ $\xleftrightarrow{*}$ $T_{hi}$ |  $T_{en}$ $\xleftrightarrow{*}$ $T_{zh}$ |  $T_{en}$ $\leftrightarrow$ $T_{x}$ |  $T_{x}$ $\leftrightarrow$ $T_{y}$ |  $T_{en}$ $\xleftrightarrow{*}$ $T_{hi}$ |  $T_{en}$ $\xleftrightarrow{*}$ $T_{zh}$
SimCSE | 75.78 | 68.46 | 77.93 | 80.47 | 79.11 | 76.3 | 73.31 | 74.91
LaBSE | 85.25 | 78.44 | 88.98 | 89.1 | 87.03 | 81.7 | 88.98 | 85.06
mBERT-mp | 80.98 | 73.74 | 82.9 | 86.73 | 82.68 | 80.22 | 76.73 | 81.85
XLM-R | 83.38 | 75.02 | 86.85 | 88.08 | 85.42 | 80.65 | 83.14 | 83.1
MPNet | 82.85 | 78.63 | 86.08 | 87.58 | 84.2 | 83.45 | 83.14 | 83.76
distill mBERT | 84.55 | 77.45 | 87.64 | 88.7 | 86.3 | 82.28 | 83.14 | 84.3
Our Approach |  |  |  |  |  |  |  |
Corpus-based | 61.86 | 56.74 | 57.34 | 69.33 | 70.51 | 71.73 | 54.01 | 63.11
\+ Key Only | 70.41 | 62.14 | 73.4 | 74.67 | 73.85 | 73.52 | 62.49 | 66.23
\+ Key-Val-Bi | 87.71 | 84.2 | 90.07 | 93.04 | 89.51 | 85.52 | 85.06 | 89.2
\+ Key-Val-Uni | 87.89 | 84.33 | 90.34 | 93.12 | 89.52 | 85.42 | 85.16 | 88.62
\+ Multi-Key | 87.91 | 84.36 | 90.14 | 92.8 | 89.3 | 85.46 | 84.98 | 88.15
Table 4: Matched and UnMatch Score : F1-Score for all test sets of InfoSync. Method |  |  $T_{en}$ $\leftrightarrow$ $T_{x}$ |  |  |  $T_{x}$ $\leftrightarrow$ $T_{y}$ |
---|---|---|---|---|---|---
| OI | IR | SV | LV | UI | EL | OI | IR | SV | LV | UI | EL
w/o Align | 298 | 286 | 22 | 158 | 388 | 118 | 245 | 226 | 33 | 146 | 486 | 148
Corpus-based | 81 | 284 | 15 | 141 | 337 | 74 | 108 | 218 | 26 | 102 | 366 | 109
+Key Only | 110 | 281 | 7 | 120 | 262 | 48 | 77 | 212 | 19 | 94 | 284 | 97
+Key-Val-Bi | 75 | 232.33 | 6 | 35 | 108 | 8 | 44 | 197 | 15 | 28 | 60 | 18
+Key-Val-Uni | 74 | 206.67 | 6 | 30 | 99 | 8 | 43 | 188 | 15 | 28 | 59 | 17
+Multi-Key | 74 | 179.67 | 6 | 30 | 99 | 8 | 43 | 180.33 | 15 | 28 | 59 | 17
Table 5: Error Analysis for Matched Score : $T_{en}$ $\leftrightarrow$ $T_{x}$
and $T_{x}$ $\leftrightarrow$ $T_{y}$.

###  5.1 Experimental Setup

#### Baselines Models.

We compare our approach with LaBSE Feng et al. (2022), and SimCSE Gao et al.
(2021), multilingual sentence transformers embeddings Reimers and Gurevych
(2020a) in which we include mBERT (case2) with mean pooling (mp) Reimers and
Gurevych (2020b), and its distill versions (distill mBERT) Sanh et al. (2019)
all in base form. We also compared with XLM-RoBERTa (XLM-R) Conneau et al.
(2019) with mean pooling, and its distill version Reimers and Gurevych (2019a)
trained via MPNet-based teacher model (MPNet) Song et al. (2020). For all
baseline implementation, we use the Hugging Face transformers Wolf et al.
(2020) and sentence transformers Reimers and Gurevych (2019a) library for the
multilingual models’ implementation.

#### Hyper-parameter Tuning.

For our method, we embed translated English keys and values using MPNet model
Reimers and Gurevych (2019b). We tune the threshold hyper-parameters using the
validation set, $\frac{1}{3}$ of the total annotated set. We sequentially tune
the hyper-parameters thresholds ($\theta_{1}$ to $\theta_{5}$) in modules
training order. Optimal threshold after tuning are $\theta_{1}$ = (0.8,0.8);
$\theta_{2}$ = (0.64, 0.6); $\theta_{3}$= $\theta_{3}$ = (0.54, 0.54);
$\theta_{4}$ =(0.9, 0.54); $\theta_{5}$ = (0.88, 0.96) for $\text{T}_{en}$
$\leftarrow$ $\text{T}_{x}$ and $\text{T}_{x}$ $\leftarrow$ $\text{T}_{y}$
respectively. We retain the default setting for other models’ specific
hyperparameters.

#### Information Alignment.

We consider English as our reference language for alignment. Specifically, we
translate all multilingual tables to English using an effective table
translation approach of XInfoTabS Minhas et al. (2022). Then, we apply
incremental modules as discussed in §4.1.

We tune independently on the validation set for Non-English $\leftrightarrow$
Non-English and English $\leftrightarrow$ Non-English.

The method is assessed on two sets of metric  (a.) matched score: measure the
F1-score between ground truth matched row and predicted alignment, and  (b.)
unmatched score: measure the F1-score between independent (unmatched) rows in
ground truth with predicted unaligned rows. See Figure 2 for the explanations
of these metrics.

#### Information Updation.

We apply the heuristic-based approach and deploy the predicted updates for
human-assisted edits on Wikipedia Infoboxes. 532 table pairs are edited
distributed among $T_{en}$ $\rightarrow$ $T_{x}$, $T_{x}$ $\rightarrow$
$T_{y}$, and $T_{x}$ $\rightarrow$ $T_{en}$, where $x$ and $y$ are non-English
languages.

###  5.2 Information Alignment

Method |  $T_{en}$ $\leftrightarrow$ $T_{x}$ |  $T_{x}$ $\leftrightarrow$ $T_{y}$
---|---|---
Corpus-based | 157 | 245
+Key Only | 422 | 343
+Key-Value-Bi | 526 | 399
+Key-Value-Uni | 572 | 415
+Multi-Key | 619 | 437
Table 6: Error Analysis for UnMatch Score : Total Unaligned mistakes for
$T_{en}$ $\leftrightarrow$ $T_{x}$ and $T_{x}$ $\leftrightarrow$ $T_{y}$.

_Algorithm Efficacy._ Table 4 reports the matched and unmatched scores. For
match scores, we observe that the corpus-based module achieves an F1 score
exceeding 50 for all language pairs. Using a key-only module boosts the
performance by about 5-15 points. Taking the whole row context (key-value
pair) with strict constraints on bidirectional mapping, i.e., two-way
similarity, improves performance substantially (more than 16 points). Further
relaxing the bi-direction constraint to uni-directional matching (one-way
similarity), we improve our results marginally with less than 0.5 performance
points. Thus relaxation of the bi-direction mapping constraint doesn’t lead to
significantly better alignments. The multi-key module, which considers one-to-
many alignments, further improves the accuracy marginally. The reason for the
marginal improvements is very few instances of one-to-many mappings.

For unmatch scores, we see similar results to match scores. The only
significant difference is in key-only performance, where we observe a 0.5x
performance improvement compared to match scores. We also analyze the
precision-recall in Tables 17, 18, 19 and 20 of Appendix §A.3. We observe that
the precision reduces and recall increases for match scores with module
addition, whereas the reverse is true for unmatch scores. The number of
alignments increases as we add more modules with relaxed constraints. This
increases the number of incorrect alignments reducing the precision but
increasing the recall. 888There are more incorrect alignments ${}^{N}C_{2}$
compared to correct alignments which is $O(n)$. Similarly, we can note the
accuracy of unaligned rows increases because more incorrect alignments are
added with relaxed constraints. We also report each module coverage in
Appendix A.4. The performance of our proposed approach grouped by languages,
category, and rows keys are detailed in Appendix A.5.

_Error Analysis._ Error analysis (cf §2.1) for matched and unmatched are
reported in Table 5 and 6, respectively. Our proposed method works
sequentially, relaxing constraints, and the number of falsely aligned rows
increases with module addition (cf. Table 6). Different modules contribute
unequally to unaligned mistakes, (25%, 56%) of the mistakes come from corpus-
based module, (39%, 22%) from Key Only Module, (17%, 35%) from Key-Value-
Bidirectional module, (7%, 4%) from Key-Value-uni-directional module, and
(7.6%, 5%) from multi-key alignment module, for $T_{en}$ $\leftrightarrow$
$T_{x}$ and $T_{x}$ $\leftrightarrow$ $T_{y}$ respectively. The corpus-based
module is worst performing in $T_{x}$ $\leftrightarrow$ $T_{y}$ because of
difficulty in multilingual mapping. The key-only module is the worst
performing in $T_{en}$ $\leftrightarrow$ $T_{x}$ because it’s the first
relaxation in the algorithm. Further analysis of the error cases is in
Appendix (§A.7).

###  5.3 Information Updation

Rules |  | Gold |  | Predicted
---|---|---|---|---
|  $T_{en}$ $\rightarrow$ $T_{x}$ |  $T_{x}$ $\rightarrow$ $T_{y}$ | Live Set |  $T_{en}$ $\rightarrow$ $T_{x}$ |  $T_{x}$ $\rightarrow$ $T_{y}$
R1 | 20320 | 18055 | 4213 | 21246 | 17675
R2 | 648 | 502 | 207 | 1395 | 1852
R3 | 546 | 399 | 75 | 443 | 347
R4 | 142 | 151 | 4 | 120 | 147
R5 | 3507 | 2116 | 784 | 3193 | 1960
R6 | 5237 | 3047 | 332 | 5062 | 2891
R7 | 2748 | 1899 | 990 | 2732 | 1855
R8 | 25 | 77 | 5 | 29 | 82
$Al$ | 14967 | 9715 | 2851 | 14864 | 10657
Table 7: Updates on Test Corpora: Count of the number of updates done by different rules listed in §4.2.$Al$ is the number of Alignments. R1-R8 are the rules listed in the same sequential manner as listed in §4.2. Type | Total | Accept | Reject
---|---|---|---
Row Transfer | 461 | 368(79.82%) | 93(20.17%)
Value Substitution | 70 | 52(74.28%) | 18(25.72%)
Append Value | 72 | 46(63.88%) | 26(36.12%)
Total | 603 | 466 (77.28%) | 136(22.72%)
Table 8: Analysis of Human-Assisted Updates: Accept/Reject rate of different types of edits for human-assisted Wikipedia infobox updates. Ln Pairs | Total | Accept | Reject
---|---|---|---
$T_{en}$ $\rightarrow$ $T_{x}$ | 204 | 161(78.92%) | 43(21.07%)
$T_{x}$ $\rightarrow$ $T_{y}$ | 216 | 169(78.25%) | 47(21.75%)
$T_{x}$ $\rightarrow$ $T_{en}$ | 183 | 136(74.31%) | 47(25.68%)
Total | 603 | 466(77.28%) | 137(22.71%)
Table 9: Human-Assisted Wikipedia infobox updates: Accept/Reject rate for
different flows of information.

Table 7 reports the results of different updation types of rules explained in
§4.2. We observe that the row addition rule accounts for the most updated,
$\sim$64% of total updates for gold and predicted aligned table pairs. The
flow of information from high resource to low resource accounts for $\sim$13%
of the remaining updates, whereas a high number of rows too low adds another
8% of the updates. $\sim$9% of the updates are done by the value updates rule.
All the other rules combined give 8% of the remaining suggested updates. From
the above results, most information gaps can be resolved by row transfer. The
magnitude of rules like value updates and multi-key shows that table
information needs to be synchronized regularly. Examples of edited infoboxes
using the proposed algorithm are shown in Appendix Figures 4 and 5.

Table 8 reports a similar analysis for human-assisted Wikipedia infobox edits.
We also report Wikipedia editors’ accept/reject rate for the above-deployed
system in Table 9. We obtained an acceptance rate of 77.28% (as of May 2023),
with the highest performance obtained when information flows across non-
English languages. The lowest performance is obtained when the information
flows from non-English to an English info box. This highlights that our two-
step procedure is effective in a real-world scenario. Examples of live updates
are shown in Appendix Figures 6 and 7.

##  6 Related Works

#### Information Alignment.

Multilingual Table attribute alignment has been previously addressed via
supervised Adar et al. (2009); Zhang et al. (2017); Ta and Anutariya (2015)
and unsupervised methods Bouma et al. (2009); Nguyen et al. (2011). Supervised
methods trained classifiers on features extracted from multilingual tables.
These features include cross-language links, text similarity, and schema
features. Unsupervised methods made use of corpus statistics and
template/schema matching for alignments. Other techniques by Jang et al.
(2016); Nguyen et al. (2018) focus on using external knowledge graphs such as
DBpedia for the updation of Infoboxes or vice versa. In their experiments,
most of these methods use less than three languages, and machine translation
is rarely used. Additionally, we don’t require manual feature curation for
strong supervision. We study the problem more thoroughly with grouped analysis
along languages, categories, and keys direction. The works closest to our
approach are Nguyen et al. (2011); Rinser et al. (2013), both of which use
cross-language hyperlinks for feature or entity matching. Nguyen et al. (2011)
uses translations before calculating text similarity. Utilizing cross-language
links can provide a robust alignment supervision signal. In contrast to our
approach, we do not use external knowledge or cross-language links for
alignments. This additional information is rarely available for languages
other than English.

#### Information Updation.

Prior work for information updates Iv et al. (2022); Spangher et al. (2022);
Panthaplackel et al. (2022); Zhang et al. (2020b, d) covers Wikipedia or news
articles than semi-structured data like tables. Spangher et al. (2022) studies
the problem of updating multilingual news articles across different languages
over 15 years. They classify the edits as addition, deletion, updates, and
retraction. These were the primary intuitions behind our challenge classified
in §2.1. Iv et al. (2022) focused on automating article updates with new facts
using large language models. Panthaplackel et al. (2022) focused on generating
updated headlines when presented with new information. Some prior works also
focus on the automatic classification of edits on Wikipedia for content
moderation and review Sarkar et al. (2019); Daxenberger and Gurevych (2013).
Evening modeling editor’s behavior for gauging collaborative editing and
development of Wikipedia pages has been studied Jaidka et al. (2021); Yang et
al. (2017). Other related works include automated sentence updation based on
information arrival Shah et al. (2020); Dwivedi-Yu et al. (2022). None of
these works focus on tables, especially Wikipedia Infoboxes. Also, they fail
to address multilingual aspects of information updation.

##  7 Conclusion and Future Work

Information synchronization is a common issue for semi-structured data across
languages. Taking Wikipedia Infoboxes as our case study, we created InfoSync
and proposed a two-step procedure that consists of alignment and updation. The
alignment method outperforms baseline approaches with an F1-score greater than
85; the rule-based method received a 77.28 percent approval rate when
suggesting updates to Wikipedia.

We identify the following future directions.  (a) Beyond Infobox
Synchronization.While our technique is relatively broad, it is optimized for
Wikipedia Infoboxes. We want to test whether the strategy applies to
technical, scientific, legal, and medical domain tables Wang et al. (2013);
Gottschalk and Demidova (2017). It will also be intriguing to widen the
updating rules to include social, economic, and cultural aspects.  (b) Beyond
Pairwise Alignment.Currently, independent language pairs are considered for
(bi) alignment. However, multiple languages can be utilized jointly for
(multi) alignment.  (c) Beyond Pairwise Updates.Similar to (multi) alignment,
one can jointly update all language variants simultaneously. This can be done
in two ways: (1.) With English as pivot language : To update across all
languages. Here, English act as a central server with message passing. (2.)
Round-Robin Fashion: where pairwise language updates between language pairs
are transferred in a round-robin ring across all language pairs. In every
update, we selected a leader similar to a leader election in distributed
systems.  (d) Joint Alignment and Updation.Even while our current approach is
accurate, it employs a two-step process for synchronization, namely alignment
followed by updating. We want to create rapid approaches aligning and updating
in a single step.  (e) Text for Updation: Our method doesn’t consider
Wikipedia articles for updating tables Lange et al. (2010); Sáez and Hogan
(2018); Sultana et al. (2012).

## Limitations

We only consider 14 languages and 21 categories, whereas Wikipedia has pages
in more than 300 languages and 200 broad categories. Increasing the scale and
diversity will further improve method generalization. Our proposed method
relies on the good multilingual translation of key and value from table pairs.
Although we use key, value, and category together for better context,
enhancement in table translation Minhas et al. (2022) will benefit our
approach. Because our rule-based system requires manual intervention, it has
automation limits. Upgrading to completely automated methods based on a large
language model may be advantageous. We are only considering updates for semi-
structured tables. However, updating other page elements, such as images and
article text, could also be considered. Although a direct expansion of our
method to a multi-modal setting is complex Suzuki et al. (2012).

## Ethics Statement

We aimed to create a balanced, bias-free dataset regarding demographic and
socioeconomic factors. We picked a wide range of languages, even those with
limited resources, and we also ensured that the categories were diversified.
Humans curate the majority of information on Wikipedia. Using unrestricted
automated tools for edits might result in biased information. For this reason,
we adhere to the "human in the loop" methodology Smith et al. (2020) for
editing Wikipedia. Additionally, we follow Wikipedia editing
guidelines999https://en.wikipedia.org/wiki/Wikipedia:List_of_policies_and_guidelines,
rule set101010https://en.wikipedia.org/wiki/Wikipedia:Simplified_ruleset, and
policies111111https://en.m.wikipedia.org/wiki/Wikipedia:Editing_policy for all
manual edits. Therefore, we ask the community to use our method only as a
recommendation tool for revising Wikipedia. As a result, we ask that the
community utilize InfoSync strictly for scientific and non-commercial purposes
from this point forward.

## Acknowledgements

We thank members of the Utah NLP group for their valuable insights and
suggestions at various stages of the project, and reviewers for their helpful
comments. Additionally, we appreciate the inputs provided by Vivek Srikumar
and Ellen Riloff. Vivek Gupta acknowledges support from Bloomberg’s Data
Science Ph.D. Fellowship.

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##  Appendix A Appendix

###  A.1 Table Extraction Details

Category | Entities | $std\\_dev$ | Category | Entities | $std\\_dev$
---|---|---|---|---|---
Airport | 2563 | 5.03 | Country | 259 | 10.28
Album | 840 | 3.81 | Diseases | 462 | 4.20
Animal | 368 | 3.37 | Food | 692 | 4.34
Athlete | 369 | 5.80 | Medicine | 334 | 9.58
Book | 218 | 5.24 | Monument | 203 | 5.23
City | 262 | 7.95 | Movie | 1524 | 6.75
College | 202 | 5.83 | Musician | 284 | 5.09
Company | 267 | 6.87 | Nobel | 967 | 5.29
Painting | 743 | 3.51 | Stadium | 742 | 5.86
Person | 198 | 6.32 | Shows | 1044 | 6.83
Planet | 188 | 8.46 |  |  |
Table 10: Missing information Analysis in Categories:- For each category unique number of entities and their average standard deviation across languages. C1 | Row Diff | C1 | Row Diff
---|---|---|---
af | 5.28 | hi | 5.06
ar | 5.84 | ko | 4.30
ceb | 3.33 | nl | 3.86
de | 5.96 | ru | 4.1
en | 4.80 | sv | 3.92
es | 5.17 | tr | 4.23
fr | 4.42 | zh | 4.76
Table 11: Row Difference Across Paired Languages:- Column 2 shows average row
count difference between languages for all entities.

Table formats and HTML code styles differ from one language to another and
even across categories in the same language. Extraction is modified to handle
these variations, which requires the following steps:  (a) _Detecting
Infoboxes:_ We locate Wikipedia infoboxes that appear in at least five
languages.  (b) _Extracting HTML:_ After detection, we extract HTML and
preprocess to remove images, links, and signatures.  (c) _Table
Representation:_ we convert the extracted table and store them in JSON.

Row Difference Across Paired Languages: There is substantial variation in the
number of rows for infobox across different languages, i.e., rows difference =
$\frac{1}{|L|}$$\sum_{ln\in L\setminus{C1}}||R_{c1}|-|R_{ln}||$, where $L$ is
set of all 14 languages under consideration. Table 11 shows that German
followed by Arabic and Afrikaans, has the highest row difference. This
indicates that tables in these languages are incomplete (with missing rows).

###  A.2 Table Updation Examples

An example of table updation is shown in the Figure 3.

Figure 3: Update Example:- "Shirley Strickland de la Hunty " Infoboxes for two
languages, i.e., English and Spanish. Shows rows transfer for missing
information. Value substitution because "Aged 78" is absent in Died. One medal
information (Bronze,1952, 100m) is added to the medal tally.

###  A.3 Precision and Recall

We also evaluated precision-recall values in information alignment for matched
and unmatched scores (§5.2). Precision recall values for $T_{en}$
$\leftrightarrow$ $T_{x}$, $T_{x}$ $\leftrightarrow$ $T_{y}$, $T_{en}$
$\xleftrightarrow{*}$ $T_{hi}$ and $T_{en}$ $\xleftrightarrow{*}$ $T_{zh}$ are
reported in Tables 17, 18, 19, and 20, respectively.

###  A.4 Algorithm Coverage

Ablation |  |  $T_{en}$ $\leftrightarrow$ $T_{x}$ |  |  |  $T_{x}$ $\leftrightarrow$ $T_{y}$ |
---|---|---|---|---|---|---
| Corpus | Key | K-V-Bi | K-V-Uni | Multi | Corpus | Key | K-V-Bi | K-V-Uni | Multi
w/o | 16.28 | 40.83 | 58.17 | 71.95 | 72.54 | 17.15 | 39.78 | 57.53 | 67.58 | 67.96
Corpus | - | 33.15 | 50.17 | 74.69 | 75.3 | - | 25.04 | 49.82 | 64.98 | 65.41
Key | 16.28 | - | 57.88 | 71.14 | 71.6 | 17.15 | - | 55.05 | 64.8 | 65.1
K-V-Bi | 16.28 | 38.88 | - | 71.9 | 72.3 | 17.15 | 37.83 | - | 70.32 | 70.58
K-V-Uni | 16.28 | 46.19 | 21.34 | - | 67.53 | 17.15 | 40.19 | 62.91 | - | 63.59
Multi | 16.28 | 40.96 | 58.4 | 72.23 | - | 17.15 | 36.36 | 55.03 | 67.13 | -
Table 12: Coverage Ablation: $T_{en}$ $\leftrightarrow$ $T_{x}$ and $T_{x}$
$\leftrightarrow$ $T_{y}$.

We measure the coverage on the entire corpus, the rate of rows aligned w.r.t.
the smaller table in a table pair. Table 12 reports ablations results of
coverage for various modules. Our proposed method aligns 72.54% and 67.96% of
rows for $T_{en}$ $\leftrightarrow$ $T_{x}$ and $T_{x}$ $\leftrightarrow$
$T_{y}$, respectively. Corpus-based is the most constrained module, focusing
more on precision; hence removing corpus-based gives better coverage for both
cases. Key-Only-Unidirectional is the most important module for coverage,
followed by the Key-Only module for both cases.

###  A.5 Domain and Language Wise Analysis

Table 13, 14, and 15 show the performance of our proposed method grouped by
languages, domains, and keys, respectively.

$T_{en}$ $\leftrightarrow$ $T_{x}$ | Match | UnMatch |  $T_{x}$ $\leftrightarrow$ $T_{y}$ | Match | UnMatch
---|---|---|---|---|---
af | 88.08 | 89.48 | de $\leftrightarrow$ hi | 88.85 | 90.4
ar | 85.24 | 88.77 | de $\leftrightarrow$ ko | 85.27 | 88.7
ceb | 85.17 | 91.07 | fr $\leftrightarrow$ ar | 85.35 | 87.21
de | 85.41 | 86.65 | fr $\leftrightarrow$ de | 84.97 | 88.94
es | 89.83 | 89.7 | fr $\leftrightarrow$ hi | 83.95 | 84.58
fr | 89.41 | 89.8 | fr $\leftrightarrow$ ko | 83.59 | 84.36
hi | 90.56 | 87.07 | fr $\leftrightarrow$ ru | 87.63 | 88.83
ko | 85.69 | 86.22 | hi $\leftrightarrow$ ar | 84.33 | 89.38
nl | 86.4 | 90.28 | ko $\leftrightarrow$ ar | 82.18 | 89.08
ru | 87.46 | 88.54 | ko $\leftrightarrow$ hi | 78.8 | 83.03
sv | 84.89 | 86.76 | ru $\leftrightarrow$ ar | 82.18 | 86.96
tr | 92.07 | 91.3 | ru $\leftrightarrow$ de | 89.93 | 91.92
zh | 91.61 | 89.31 | ru $\leftrightarrow$ hi | 82.38 | 87.78
|  |  | ru $\leftrightarrow$ ko | 81.62 | 84.47
|  |  | de $\leftrightarrow$ ar | 78.05 | 87.23
Table 13: Language Wise Analysis :-Alignment F1-score reported for same language for $T_{en}$ $\leftrightarrow$ $T_{x}$ and $T_{x}$ $\leftrightarrow$ $T_{y}$ averaged over all entities.  |  $T_{en}$ $\leftrightarrow$ $T_{x}$ |  $T_{x}$ $\leftrightarrow$ $T_{y}$
---|---|---
Category | Match | UnMatch | Match | UnMatch
Airport | 79.77 | 82.64 | 85.79 | 90.9
Album | 93.9 | 91.33 | 88.6 | 85.01
Animal | 93.79 | 94.2 | 90 | 96.24
Athlete | 86.6 | 90.21 | 83.75 | 88.81
Book | 86.48 | 90.96 | 81.29 | 83.13
City | 86.14 | 93.67 | 77.4 | 86.6
College | 82.47 | 87.53 | 81.05 | 86.24
Company | 87.49 | 85.15 | 85.5 | 86.7
Country | 86.38 | 92.47 | 86.53 | 92.32
Food | 88.58 | 90.04 | 85.65 | 91.67
Monument | 84.86 | 86.14 | 87.66 | 89.6
Movie | 91.2 | 85.7 | 74.33 | 76.19
Musician | 89.47 | 85.62 | 89.04 | 93.27
Nobel | 88.2 | 91.08 | 88.84 | 87.1
Painting | 90.27 | 82.35 | 86.52 | 89.72
Person | 87.37 | 87.79 | 79.85 | 87.74
Planet | 90.93 | 85.77 | 85.01 | 87.18
Shows | 91.23 | 88.89 | 83.65 | 78.84
Stadium | 88.59 | 87.72 | 83.2 | 77.38
Table 14: Category Wise Analysis :- Alignment F1-score reported for same group entities average over all languages. Key Freq | Range | # of Keys (all) | Avg Score
---|---|---|---
High | $100\leq x$ | 33 | 90.71
Mid | $50\leq x\leq 100$ | 49 | 89.33
Low | $x\leq 50$ | 700 | 81.82
Table 15: Key Wise Analysis:- F1-Score report for grouped keys.

Group-wise Analysis. From Table 13, for $T_{en}$ $\leftrightarrow$ $T_{x}$,
Cebuano, Arabic, German, and Dutch are the worst performing languages with
F1-score close to 85 for alignment. Whereas Turkish, Chinese, and Hindi have
F1-score greater than 90. Korean, German, and Swedish are the lowest-
performing language groups, with an F1-Score close to 86 for unaligned
settings. Cebuano, Turkish, and Dutch get the highest score for unaligned
metrics (greater than 90). For non-English language pairs, the lowest F1-score
for match table pairs is observed for German-Arabic and Hindi-Korean pairs
with an F1-score close to 78, as shown in Table 13. The highest F1-score is
observed for Russian-German and Hindi-German, with F1-scores exceeding 88.8.
For unmatched data, Korean-Hindi, French-Hindi, French-Korean, and Russian-
Korean pairs have the lowest F1 scores, less than 85. In contrast, German-
Hindi and Russion-German have exceeded the unaligned F1-Score of 90.

Category-wise Analysis. As reported in Table 14, our method performs worst in
Airport and College categories for match settings when one of the languages is
English. For non-English match settings, Movie and City are the worst-
performing categories. For unmatch setting with English as one of the
languages, Airport and Painting have the lowest F1-score, whereas Movie and
Stadium have the most inferior performance for non-English languages.

Key-wise Analysis. Table 15 shows the average F1-scores across tables for
frequent and non-frequent keys. We observed an F1-score degradation of 10
points for rare keys with a low occurrence compared to frequent keys.

###  A.6 Ablation Study

Ablation |  | Match |  |  | UnMatch |
---|---|---|---|---|---|---
|  $T_{en}$ $\leftrightarrow$ $T_{x}$ |  $T_{x}$ $\leftrightarrow$ $T_{y}$ |  $T_{en}$ $\xleftrightarrow{*}$ $T_{hi}$ |  $T_{en}$ $\xleftrightarrow{*}$ $T_{zh}$ |  $T_{en}$ $\leftrightarrow$ $T_{x}$ |  $T_{x}$ $\leftrightarrow$ $T_{y}$ |  $T_{en}$ $\xleftrightarrow{*}$ $T_{hi}$ |  $T_{en}$ $\xleftrightarrow{*}$ $T_{zh}$
Corpus-based | 86.67 | 82.3 | 89.13 | 92.33 | 87.95 | 87.03 | 83.11 | 87.38
Key Only | 89 | 80.09 | 87.35 | 91.49 | 89.42 | 85.88 | 79.83 | 87.13
Key-Val-Bi | 84.98 | 75.39 | 86.95 | 90.41 | 86.39 | 82.06 | 80.48 | 84.4
Key-Val-Uni | 87.73 | 79.35 | 90 | 92.67 | 89.03 | 85.35 | 84.83 | 88.74
Multi-Key | 87.89 | 84.33 | - | - | 89.52 | 85.42 | - | -
w/o | 87.91 | 84.36 | 90.14 | 92.8 | 89.03 | 85.46 | 84.98 | 88.17
Table 16: Ablation Study of Matched and UnMatch Score : i.e. F1-Score for all
test sets of InfoSync.

We report ablation performance to highlight the significance of each module in
Table 16. Key-Value-Bidirectional mapping (two-way) is the most critical
module, followed by Key Only corpus-based modules. We also observe Uni-
directional mapping being the second most important for non-English
alignments. The multi-key module was consistently was least significant for
the same reason as the discussion above (very few instances). Similar
observations were valid for unmatching scores.

###  A.7 Further Details: Error Analysis

We discussed challenges to table information synchronization across languages
in §2.1. Table 5 (main paper) shows the number of instances of these
challenges in evaluation for matched cases after applying various modules of
the alignment algorithm.

  * •

Corpus-based module solves approximately (40%, 56%) of outdated
information,(31%, 21%) of schema variation, (10%, 30%) of language variation,
(13%, 25%) of unnormalized information and (37%, 26%) of erroneous entity
linking challenges in $T_{en}$ $\leftrightarrow$ $T_{x}$ and $T_{x}$
$\leftrightarrow$ $T_{y}$, respectively.

  * •

Further adding of key only similarity module resolves extra (24%, 13%) of
outdated information, (36%, 21%) of schema variation, (13%, 5%) of language
variation, (19%, 17%) of unnormalized information and (22%, 8%) of erroneous
entity linking challenges in $T_{en}$ $\leftrightarrow$ $T_{x}$ and $T_{x}$
$\leftrightarrow$ $T_{y}$, respectively.

  * •

Applying key-value-bidirectional module resolves another (12%, 13.5%) of
outdated information, (18%, 6.6%) of information representation, 54%, 45% of
language variation, (40%, 46%) of unnormalized information and (34%, 53%) of
erroneous entity linking challenges in $T_{en}$ $\leftrightarrow$ $T_{x}$ and
$T_{x}$ $\leftrightarrow$ $T_{y}$, respectively.

  * •

Key-Val-Unidirectional and Multi-key together solves another (18.5%, 7.5%) of
the information representation in $T_{en}$ $\leftrightarrow$ $T_{x}$ and
$T_{x}$ $\leftrightarrow$ $T_{y}$, respectively, but are not effective against
other challenges.

|  | Match |  |  | UnMatch |
---|---|---|---|---|---|---
Alignment | Precision | Recall | F1 | Precision | Recall | F1
Corpus-based | 93.51 | 46.22 | 61.86 | 55.66 | 96.17 | 70.51
\+ Key Only | 88.09 | 58.62 | 70.4 | 60.75 | 94.16 | 73.85
\+ Key-Value-Bi | 89.6 | 85.89 | 87.71 | 85.87 | 93.47 | 89.51
\+ Key-Value-Uni | 89.3 | 86.52 | 87.89 | 86.24 | 93.07 | 89.52
\+ Multi-Key | 88.85 | 86.99 | 87.91 | 86.51 | 92.27 | 89.3
Table 17: $T_{en}$ $\leftrightarrow$ $T_{x}$ alignment performance on Human-Annotated Test Data |  | Match |  |  | UnMatch |
---|---|---|---|---|---|---
Alignment | Precision | Recall | F1 | Precision | Recall | F1
Corpus-based | 75.68 | 45.38 | 56.74 | 58.9 | 91.71 | 71.73
\+ Key Only | 74.45 | 58.62 | 62.14 | 62.44 | 89.37 | 73.52
\+ Key-Value-Bi | 82.78 | 85.66 | 84.2 | 82.53 | 88.73 | 85.52
\+ Key-Value-Uni | 82.2 | 86.58 | 84.33 | 82.94 | 88.05 | 85.42
\+ Multi-Key | 82.16 | 86.68 | 84.36 | 83.05 | 88.01 | 85.46
Table 18: $T_{x}$ $\leftrightarrow$ $T_{y}$ alignment performance on Human-Annotated Test Data. |  | Match |  |  | UnMatch |
---|---|---|---|---|---|---
Alignment | Precision | Recall | F1 | Precision | Recall | F1
Corpus-based | 94.81 | 41.1 | 57.34 | 37.55 | 96.19 | 71.73
\+ Key Only | 92.04 | 61.04 | 73.4 | 46.6 | 94.81 | 73.52
\+ Key-Value-Bi | 87.65 | 85.89 | 90.07 | 77.37 | 88.73 | 85.52
\+ Key-Value-Uni | 88.59 | 86.52 | 90.34 | 78.53 | 88.05 | 85.42
\+ Multi-Key | 91.15 | 88.59 | 90.14 | 78.52 | 88.01 | 85.46
Table 19: $T_{en}$ $\xleftrightarrow{*}$ $T_{hi}$ alignment performance on Human-Annotated Test Data. |  | Match |  |  | UnMatch |
---|---|---|---|---|---|---
Alignment | Precision | Recall | F1 | Precision | Recall | F1
Corpus-based | 89.94 | 56.41 | 69.33 | 47.19 | 95.26 | 63.11
\+ Key Only | 88.78 | 64.43 | 74.67 | 51.74 | 91.99 | 66.23
\+ Key-Value-Bi | 92.38 | 93.7 | 93.04 | 86.73 | 91.81 | 89.2
\+ Key-Value-Uni | 92.13 | 94.13 | 93.12 | 86.75 | 90.58 | 88.62
\+ Multi-Key | 91.51 | 94.13 | 92.8 | 86.73 | 89.66 | 88.17
Table 20: $T_{en}$ $\xleftrightarrow{*}$ $T_{zh}$ alignment performance on
Human-Annotated Test Data.

###  A.8 Other Related Work

#### Tabular Reasoning.

Addressing NLP tasks on semi-structured tabular data has received substantial
attention. There is work on tabular NLI (Gupta et al., 2020; Chen et al.,
2020a; Gupta et al., 2022b), question-answering task (Zhang and Balog, 2020b;
Zhu et al., 2021; Pasupat and Liang, 2015a; Abbas et al., 2016; Sun et al.,
2016; Chen et al., 2021a, 2020b; Lin et al., 2020; Zayats et al., 2021; Oguz
et al., 2022, and others) and table-to-text generation (Zhang et al., 2020c;
Parikh et al., 2020; Nan et al., 2021; Yoran et al., 2022; Chen et al.,
2021b).

#### Tabular Representation and Learning.

There are also several works representing Wikipedia tables, such papers are
TAPAS (Herzig et al., 2020), StrucBERT (Trabelsi et al., 2022), Table2vec
(Zhang et al., 2019), TaBERT (Yin et al., 2020), TABBIE (Iida et al., 2021),
TabStruc (Zhang et al., 2020a), TabGCN (Pramanick and Bhattacharya, 2021), RCI
(Glass et al., 2021), TURL (Deng et al., 2022), and TableFormer (Yang et al.,
2022). Some papers such as (Yu et al., 2018, 2021; Eisenschlos et al., 2020;
Neeraja et al., 2021; Müller et al., 2021; Somepalli et al., 2021;
Shankarampeta et al., 2022; Dong et al., 2022, and others) study pre-training
for tabular tasks. Paper related to tabular probing includes (Koleva et al., ;
Gupta et al., 2022a).

#### Tabular Datasets.

There are several tabular task datasets on (a.) tabular NLI: (Gupta et al.,
2020; Rozen et al., 2019; Müller et al., 2021; Kaushik et al., 2020; Xiong et
al., 2020; Chen et al., 2020a; Eisenschlos et al., 2020; Chen et al., 2020c,
and others); (b.) Tabular QA: WikiTableQA Pasupat and Liang (2015b), HybridQA
Chen et al. (2020b); Zayats et al. (2021); Oguz et al. (2022),WikiSQL Iyyer et
al. (2017), SQUALL Ferré (2012); Shi et al. (2020), OpenTableQA Chen et al.
(2021b), FinQA Chen et al. (2021c), FeTaQA Nan et al. (2022), TAT-QA Zhu et
al. (2021), SQA Iyyer et al. (2017), NQ-Tables Herzig et al. (2021); (c.) and
Table Generation: ToTTo Parikh et al. (2020), Turing Tables Yoran et al.
(2022), LogicNLG Chen et al. (2020c).

Furthermore, there are also several works discussed on web table extraction,
retrieval, and augmentation Zhang and Balog (2020a), and utilizing the
transformers model for table representation Badaro and Papotti (2022).

Figure 4: Example From Update Algorithm Proposed: Update English Infobox is
obtained by using Spanish Infobox as a reference and vice versa."Country of
origin" is updated in English infobox and "website" is updated in Spanish
infobox. Figure 5: Example From Update Algorithm Proposed: Update English
Infobox is obtained by using French Infobox as a reference and vice versa.
Multiple keys are updated in both infoboxes "Opened," "Location," "Owner,"
"Coordinates," "Operating Season," and "Slogan" in French, and "number of
visitors," "surface," "Type of park," number of attractions" are updated in
English infobox. Figure 6: Example From Live Updates: In the above figure, the
Target infobox needs to be updated using Reference infobox(available in
English version) as extra/grounding information. The updated infobox is shown
in column 3, where the key ’job’ is updated. This is an example of "Value
substitution," as in Table 8. The red box highlights the updated information.
Figure 7: Example From Live Updates: In the above figure, the target infobox
needs to be updated using a reference infobox as extra/grounding information.
The updated Infobox is shown in column 3, where the ’Load/Cargo Traffic’ key
is updated. This is an example of Row Addition, as referred to in Table 8. The
red box highlights the updated information.