# Fact or Fiction: Verifying Scientific Claims

David Wadden† Kyle Lo‡ Lucy Lu Wang‡ Shanchuan Lin†
Madeleine van Zuylen‡ Arman Cohan‡ Hannaneh Hajishirzi†‡
† University of Washington, Seattle, WA, USA
‡ Allen Institute for Artificial Intelligence, Seattle, WA, USA
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>Work performed during internship
with the Allen Institute for Artificial Intelligence

###### Abstract

We introduce the task of scientific fact-checking. Given a corpus of
scientific articles and a claim about a scientific finding, a fact-checking
model must identify abstracts that support or refute the claim. In addition,
it must provide _rationales_ for its predictions in the form of evidentiary
sentences from the retrieved abstracts. For this task, we introduce SciFact, a
dataset of 1.4K expert-written scientific claims paired with evidence-
containing abstracts annotated with labels and rationales. We present a
baseline model and assess its performance on SciFact. We observe that, while
fact-checking models trained on Wikipedia articles or political news have
difficulty generalizing to our task, simple domain adaptation techniques
represent a promising avenue for improvement. Finally, we provide initial
results showing how our model can be used to verify claims relevant to
COVID-19 on the CORD-19 corpus. Our dataset will be made publicly available at
https://github.com/allenai/scifact.

##  1 Introduction

Figure 1: A SciFact claim refuted by evidence. To refute this claim, the
system must recognize that (1) “CSF” is an acronym for “cerebral spinal
fluid”, found in the brain, (2) “Citalopram” is a type of antidepressant, but
“placebo” is not, and (3) “Slowing by 37%” indicates a reversal in effect
relative to the claim.

_Fact-checking_ – a task in which the veracity of an input _claim_ is verified
against a corpus of documents that _support_ or _refute_ the claim – has seen
increased attention as an important research area. This attention is motivated
by the proliferation of misinformation in political news, social media, and on
the web. In turn, interest in fact-checking has spurred the creation of many
datasets across different domains to support research and development of
automated fact-checking systems. Yet, to our knowledge, no such dataset exists
to facilitate research on another important domain for fact-checking –
scientific literature. The ability to verify claims about scientific concepts,
especially those related to biomedicine, is an important application area for
fact-checking. Furthermore, this line of research also offers a unique
opportunity to explore the capabilities of modern neural models, since
successfully verifying most scientific claims requires expert background
knowledge, complex language understanding, and reasoning capability, as
demonstrated in Figure 1.

In this paper, we introduce the task of scientific fact-checking. To
facilitate research on this task, we construct SciFact, a dataset of 1,409
scientific claims accompanied by scientific abstracts that support or refute
each claim, and annotated with rationales justifying each support / refute
decision. To curate this dataset, we use a novel annotation protocol that
takes advantage of a plentiful source of naturally-occurring claims in the
scientific literature – _citation sentences_ , or _“citances”_ Nakov et al. .

To establish performance baselines on this new task, we develop a pipeline
model following the “BERT-to-BERT” approach from DeYoung et al. (2019), which
achieves strong performance on Fever. Our model, which we call VeriSci,
retrieves abstracts related to a given claim, uses a BERT-based Devlin et al.
(2019) sentence selector to identify rationale sentences, and then labels each
claim as Supports, Refutes, or NotEnoughInfo with respect to the claim. Our
system is able to identify correctly-labeled and rationalized evidence
abstracts with performance of 46.5 F1, indicating that the task is doable but
leaving ample room for improvement. Despite its small size, training VeriSci
on SciFact leads to better performance than training on fact-checking datasets
constructed from Wikipedia articles Thorne et al. (2018) and political news
Hanselowski et al. (2019). The strongest performance is achieved using a
simple domain adaptation strategy, pretraining on Fever and then finetuning on
SciFact.

To evaluate the real-world applicability of our dataset and approach, we
showcase the ability of our model to verify expert-written claims concerning
the novel coronavirus COVID-19 against the newly-released CORD-19 corpus Wang
et al. (2020). Medical student reviewers judge the retrieved evidence to be
plausible in 23 of the 36 claims.111While we find these results promising, we
emphasize that our model is a research prototype and should not be used to
make any medical decisions whatsoever. Our data and models will be released
publicly at https://github.com/allenai/scifact.

##  2 Related work

We discuss SciFact in relation to existing fact-checking datasets and other
related scientific NLP tasks.

###  2.1 Fact checking

Fact-checking datasets include PolitiFact Vlachos and Riedel (2014), Emergent
Ferreira and Vlachos (2016), LIAR Wang (2017), SemEval 2017 Task 8 RumorEval
Derczynski et al. (2017), Snopes Popat et al. (2017), CLEF-2018 CheckThat!
Barrón-Cedeño et al. (2018), Verify ”Baly et al. (2018), Fever Thorne et al.
(2018), and UKP Snopes Hanselowski et al. (2019). Notably, the latter two
datasets are additionally annotated with sentence-level rationales; we refer
the reader to Hanselowski et al. (2019) for a thorough review. Yet, to our
knowledge, there is no prior work on scientific fact checking. We summarize
key characteristics of other fact-checking datasets and explain how they
differ from those in SciFact.

#### Natural vs synthetic claims

We distinguish between _synthetic_ and _natural_ claims. Fever uses synthetic
claims created by annotators by mutating Wikipedia sentences selected as
related evidence. Most other prior work uses natural claims curated from fact
checking sites, Twitter, debates, or news articles. The claims in SciFact
natural, since they are derived from citation sentences that occur naturally
in scientific articles, and annotators to not see the evidence at time of
claim writing. We discuss this claim-writing process further in §3.2.

#### Labeling claims vs claim-document pairs

In fact-checking, a claim is a statement of actuality whose veracity is a
fixed target for investigation. Therefore, claims can be assigned a global
_supported_ or _refuted_ label. For example in Fever, the claim “ _Barack
Obama was the $44^{th}$ President of the United States_” can be verified as
globally supported given sufficient evidence.

While SciFact claims are indeed factual assertions, we do not attempt to
assign them global labels because the asserted “fact” may still be under
active scientific research. Instead of labeling claims, we label claim-
document pairs with _support_ or _refute_ relations. This is similar to the
task in Perspectrum Chen et al. (2019), which identifies evidence-backed
“perspective” statements as agreeing or disagreeing with an opinion-based
claim, such as “ _Animals should have lawful rights_.” We discuss this claim-
document labeling process further in §3.3.

###  2.2 Related scientific NLP tasks

The SciFact task is closely related to two other scientific NLP tasks –
citation contextualization and evidence inference. The goal of _citation
contextualization_ is to identify all spans in a cited document that are
relevant to a particular citation in a citing document Cohan et al. (2015). A
dataset of 20 biomedical articles annotated with contextualized citations was
released at TAC 2014 for this
task.222https://tac.nist.gov/2014/BiomedSumm/index.html While the dataset was
annotated by domain experts, the average inter-annotator agreement rate on
annotated spans was only 21.7% 333Aggregating the dataset’s span-level
annotations to the sentence level gives an agreement of 0.27 Fleiss’
$\kappa$.. More recently, the SciSummNet dataset Yasunaga et al. (2019) was
released, focusing on NLP papers rather than biomedicine. Similar to these
datasets, the annotation in SciFact involves contextualizing citances in the
cited document, but in SciFact, citances are first converted into claims, and
evidence is restricted to the abstracts of the cited documents.

The _evidence inference_ task Lehman et al. (2019), involves predicting the
effect of a medical intervention on a specified outcome. Like SciFact, the
evidence inference task requires the model to identify evidence justifying its
label predictions. Unlike the full-sentence claims given as input to SciFact,
the inputs for evidence inference are individual text spans specifying an
intervention, comparator, and treatment outcome.

##  3 The SciFact dataset

For this task, we introduce SciFact, a dataset of 1,409 scientific claims
fact-checked against a corpus of 5,183 abstracts. Abstracts that support or
refute a claim are additionally annotated with rationales. We describe our
corpus creation and annotation protocol.

###  3.1 Data source

To construct SciFact, we use S2ORC Lo et al. (2020), a publicly-available
corpus of millions of scientific articles. We restrict articles to those with
at least 10 citations and with full text freely available444While we focus on
abstracts, this choice leaves open the opportunity to extend our work to full
text.. To ensure that documents in our dataset are of high quality, we
randomly sample articles from a manually curated collection of well-regarded
journals spanning domains from basic science (e.g., _Cell_ , _Nature_) to
clinical medicine (e.g., _JAMA_ , _BMJ_). The full list is in Appendix B. We
refer to the resulting collection of articles as our seed set.

We use the S2ORC citation graph to sample citances (from _citing_ articles)
that cite these seed articles. If a citance cites other articles not in the
seed set, we refer to these as _co-cited_ articles.

###  3.2 Claim writing

#### Definition

In SciFact, a scientific claim is an atomic factual statement expressing a
finding about one aspect of a scientific entity or process, which can be
verified from a single source.555Requiring annotators to search multiple
sources to verify a single claim increases cognitive burden and decreases the
quality of annotation. For instance, “ _The $R_{0}$ of the novel coronavirus
is 2.5_” is considered a valid scientific claim. Opinion-based statements like
“ _The government should require people to stand six feet apart to slow the
spread of coronavirus_ ” are not considered scientific claims. Compound claims
like “ _Aerosolized coronavirus droplets can travel at least 6 feet and can
remain in the air for 3 hours_ ” should be split into two atomic claims.

#### Annotation

Citances Nakov et al.  are an ideal source for claims since they contain
expert-written assertions about important findings reported in related
research articles, and, unlike claims found on the web, they specify the
documents where supporting evidence can be found.

Annotators are shown a citance – the source citance – in the context of its
source article, and are asked to write up to three claims based on the content
of the citance while ensuring the produced claims conform to our claim
definition. This results in _natural_ claims because the annotator does not
see the cited article’s abstract – the _cited abstract_ – at the time of claim
writing. Figure 2 shows an example. See Appendix C for screenshots of the
claim and evidence interfaces.

Figure 2: A claim written based on a citance. Material unrelated to the
citation is removed. The acronym “CVD” is expanded to “cardiovascular
disease”.

#### Annotators

The annotators include four experts with background in scientific NLP, fifteen
undergraduates studying life sciences, and four graduate students (doctoral or
medical) in the life sciences. Student claim writers attend an in-person
training session where they are introduced to the task and receive feedback
from the four experts. Following training, student annotators continue writing
claims remotely. The expert annotators monitor these claims for quality and
provide feedback when necessary. As a final check, all submitted claims are
proofread by an undergraduate whose claims are deemed especially high-quality
by the expert annotators.

#### Claim negation

Unless the authors of the source citance were mistaken, cited articles should
provide supporting evidence for the claims made in a citance. To obtain
examples where an abstract Refutes a claim, we create claim negations.
Performing this task improperly can introduce biases into the dataset; for
instance, a model could learn to associate the word “not” with a Refuted label
Schuster et al. (2019). To mitigate these effects, a scientific NLP expert
performed the negations, skipping claims that could not be negated without
introducing obvious dataset artifacts. The majority of claim negations
involved a reversal of effect direction; for instance “ _A high
microerythrocyte count protects against severe anemia_ ” can be negated as “
_A high microerythrocyte count raises vulnerability to severe anemia_ ”.

###  3.3 Claim verification

#### Annotation

Annotators are shown a claim, together with one of the claim’s cited
abstracts, and asked to label the claim-abstract pair as Supports, Refutes, or
NotEnoughInfo. If the abstract is not relevant to the claim, they are
instructed to label it NotEnoughInfo. If the annotator assigns a Supports or
Refutes label, they must also identify _all valid_ _rationales_ justifying the
label. A rationale is a minimal collection of sentences sufficient to justify
the label. An abstract may have multiple rationales,666This mirrors the setup
in Fever, where a single claim may be supported by multiple rationales. as in
Figure 3, but they must be _mutually exclusive_ – i.e. they may not share any
sentences.

Figure 3: A claim supported by two rationales from the same abstract. The text
of each rationale on its own provides sufficient evidence to verify the claim.

#### Annotators

The annotators include three NLP experts, five undergraduates studying life
sciences, and five graduate students studying life sciences. Annotations are
performed remotely through a web interface. Annotators are required to pass a
10-question “quiz” before annotating their own claims. After passing the quiz,
subsequent submissions are reviewed by an NLP expert until that expert deems
the annotator reliable. Approved annotators are then assigned to review each
others’ submissions. In general, graduate students are assigned to review
annotations from undergraduates.

#### Quality

We assign 232 claim-abstract pairs for independent re-annotation. The label
agreement is 0.75 Cohen’s $\kappa$, comparable with the 0.68 Fleiss’ $\kappa$
reported in Thorne et al. (2018), and 0.70 Cohen’s $\kappa$ reported in
Hanselowski et al. (2019). To measure rationale agreement, we treat each
sentence as either classified as “part of a rationale” or “not part of a
rationale” and compute sentence-level agreement on abstracts where annotators
agreed on the entailment label. The resulting Cohen’s $\kappa$ is 0.71.
Additional statistics on the dataset can be found in Appendix B.

###  3.4 Adding distractors to the corpus

Our initial corpus is defined as the union of the seed and co-cited abstract
sets from §3.1. To simulate a more realistic corpus for retrieval, we
introduce additional _distractor_ abstracts. In doing so, we observe a
tradeoff. Adding too many distractors (e.g., all biomedical papers in S2ORC)
increases the likelihood of false negatives – that is, when a distractor
actually contains evidence relevant to a written claim, but may have been
unknown to the authors who wrote the source citance. However, adding a small
number of uniformly-sampled distractors does not pose a retrieval challenge,
since these documents may not share much lexical overlap with the claims.

We address this problem as follows: for each citance, we sample articles that
are cited in the same document as the citance, but in a different paragraph
(see Figure 4). These articles should have cover topics related to the
evidence articles. At the same time, the citance authors were clearly aware of
these articles, and presumably would have mentioned them in the citance if
they were relevant. We add five distractor articles per citance.

Figure 4: Citance and abstract selection. Citing abstracts are identified for
each seed document. A claim is written based on the citation in the citing
abstract. Co-cited and distractor abstracts are added to the corpus.

##  4 The SciFact task

We formalize our definition of the SciFact task and define how we perform
evaluation.

###  4.1 Task Formulation

The inputs to our fact-checking task are a scientific claim $c$ and a corpus
of abstracts ${\mathcal{A}}$. All abstracts $a\in{\mathcal{A}}$ are labeled as
$y(c,a)\in\\{$Supports, Refutes, NotEnoughInfo $\\}$ with respect to a claim
$c$. The abstracts that either Support or Refute $c$ are referred to as
_evidence abstracts_ for $c$. We denote the set of evidence abstracts
${\mathcal{E}}(c)$. Each evidence abstract $a\in{\mathcal{E}}(c)$ is annotated
with rationales. A single rationale $R$ is a collection of sentences
$\\{r_{1}(c,a),\dots,r_{m}(c,a)\\}$ sufficient to justify the label $y(c,a)$,
where $m$ is the number of sentences in rationale $R$. We denote the set of
all rationales as ${\mathcal{R}}(c,a)=\\{R_{1}(c,a),\dots,R_{n}(c,a)\\}$,
where $n$ is the number of rationales.

Given a claim $c$ and a corpus ${\mathcal{A}}$, the system must predict a set
of evidence abstracts $\widehat{{\mathcal{E}}}(c)$. For each abstract
$a\in\widehat{{\mathcal{E}}}(c)$, it must predict a label $\widehat{y}(c,a)$,
and a collection of _rationale sentences_
$\widehat{S}(c,a)=\\{\widehat{s}_{1}(c,a),\dots,\widehat{s}_{m}(c,a)\\}$. Note
that although the gold annotations may contain multiple separate rationales,
to simplify the prediction task we simply require the model to predict a
single collection of _rationale sentences_ ; these sentences may come from
multiple gold rationales.

###  4.2 Task Evaluation

#### Abstract-level evaluation

is inspired by the Fever Score and measures the system’s ability to correctly
identify evidence abstracts. A predicted abstract
$a\in\widehat{{\mathcal{E}}}(c)$ is _correctly identified_ if (1) $a$ is a
gold evidence abstract for $c$, (2) The predicted label is correct:
$\widehat{y}(c,a)=y(c,a)$, (3) the predicted rationale sentences contain a
gold rationale, i.e., there exists some gold rationale
$R(c,a)\subseteq\widehat{S}(c,a)$. Like Fever, which limits the maximum number
of predicted rationale sentences to five, SciFact limits to three predicted
rationale sentences.777The longest rationale in SciFact is three sentences.
Overall performance is measured by the F1 of the precision and recall of
correctly-identified evidence abstracts, which we refer to as
$F1_{\textrm{abstract}}$.

#### Sentence-level evaluation

measures the system performance at identifying individual rationale sentences.
We consider this evaluation in addition to the abstract-level evaluation
because the abstract-level evaluation does not penalize the prediction of
extra rationale sentences. To address this, we define an additional evaluation
criterion at the level of individual rationale sentences. When the model
correctly identifies all the sentences in a gold rationale, it is rewarded for
_each_ sentence in that rationale, but it is also penalized for all other
sentences it predicts. More formally, a rationale sentence
$\widehat{s}(c,a)\in\widehat{S}(c,a)$ is correctly identified if (1) the
abstract $a$ is correctly labeled, (2) $\widehat{s}(c,a)$ is a member of a
gold rationale $R(c,a)$, and (3) all other members of $R(c,a)$ are among the
predicted $\widehat{S}(c,a)$.

Denote the set of correctly predicted rationale sentences for claim $c$ and
abstract $a$ as $S^{*}(c,a)$. We compute rationale sentence precision and
recall as

$\displaystyle
P_{\textrm{sentence}}=\frac{\sum_{c}\sum_{a\in\widehat{{\mathcal{E}}}(c)}|S^{*}(c,a)|}{\sum_{c}\sum_{a\in\widehat{{\mathcal{E}}}(c)}|\widehat{S}(c,a)|},$
$\displaystyle
R_{\textrm{sentence}}=\frac{\sum_{c}\sum_{a\in\widehat{{\mathcal{E}}}(c)}|S^{*}(c,a)|}{\sum_{c}\sum_{a\in{\mathcal{E}}(c)}\sum_{R\in{\mathcal{R}}(c,a)}|R(c,a)|}$

Overall performance is measured as the F1 of the precision and recall, denoted
as $F1_{\textrm{sentence}}$. For sentence-level evaluation, we do not limit
the number of predicted rationale sentences, since the evaluation penalizes
models that over-predict.

##  5 VeriSci: Baseline model

We develop a baseline for scientific fact checking by adapting the “BERT-to-
BERT” model for “hard” rationale selection presented in DeYoung et al. (2019)
for a number of rationalized NLP tasks including Fever; this approach is also
similar to the fact-checking model presented in Soleimani et al. (2019). Our
baseline (called VeriSci) takes a claim $c$ and corpus ${\mathcal{A}}$ as
input, identifies evidence abstracts $\widehat{{\mathcal{E}}}(c)$, and
predicts a label $\widehat{y}(c,a)$ and rationale sentences $\widehat{S}(c,a)$
for each $a\in\widehat{{\mathcal{E}}}(c)$. VeriSci is a pipeline of three
components:

  1. 1.

AbstractRetrieval, which retrieves $k$ abstracts with highest TF-IDF
similarity to the input claim.

  2. 2.

RationaleSelection, which identifies rationals $\widehat{S}(c,a)$ for each
candidate abstract (§5.1).

  3. 3.

LabelPrediction, which makes the final label prediction $\widehat{y}(c,a)$
(§5.2).

###  5.1 Rationale selection

Given a claim $c$ and candidate abstract $a$, we train a model to predict
$z_{i}$ for each abstract sentence $a_{i}$, where
$z_{i}=\mathds{1}[a_{i}\textrm{ is a rationale sentence}]$. For each sentence,
we encode the concatenated sequence $w_{i}=[a_{i},\textsc{SEP},c]$ using BERT
888We use BERT to refer to the class of models with the BERT architecture. Our
final system uses RoBERTa-large Liu et al. (2019). and predict a score
$\tilde{z}_{i}=\sigma[f(\textsc{CLS}(w_{i}))]$, where $\sigma$ is the sigmoid
function, $f$ is a linear layer and $\textsc{CLS}(w_{i})$ is the CLS token
from the BERT encoding of $w_{i}$. We minimize cross-entropy loss between
$z_{i}$ and $\tilde{z}_{i}$ during training. We train the model on pairs of
claims and their cited abstracts from our corpus. For each claim, we use cited
abstracts labeled NotEnoughInfo, as well as non-rationale sentences from
abstracts labeled Supports and Refutes as negative examples. We threshold the
sigmoid values when performing selection.

###  5.2 Label prediction

Sentences identified by the rationale selector are passed to a separate BERT
model to make the final labeling decision. Given a claim $c$ and abstract $a$,
we concatenate the claim and the rationale sentences $u=[s_{1}(c,a),\dots
s_{m}(c,a),\textsc{SEP},c]$,999We truncate the rationale input
$[s_{1},\dots,s_{m}]$ if it exceeds the BERT token limit. $c$ is never
truncated. and predict $\tilde{y}(c,a)=\phi[f(\textsc{CLS}(u))]$, where $\phi$
is the softmax function, and $f$ is a linear layer with three outputs
representing the {Supports, Refutes, NotEnoughInfo } labels. We minimize the
cross-entropy loss between $\tilde{y}(c,a)$ and the true label $y(c,a)$.

We train the model on pairs of claims and their cited abstracts using gold
rationales as input. For abstracts labeled NotEnoughInfo, we randomly choose
$k$ sentences from the cited abstract as input rationales.101010$k$ is chosen
from $\\{1,2\\}$ with 0.5 probability. When making predictions, we use the
predicted rationale sentences $\widehat{S}(c,a)$ as input and predict
$\hat{y}(c,a)=\textrm{argmax }\tilde{y}(c,a)$. The system predicts
NotEnoughInfo when given an abstract with no rationale sentences.

##  6 Experiments

In our experiments, we (1) establish a performance baseline on SciFact using
VeriSci, (2) analyze the performance of the three components of VeriSci, (3)
demonstrate the importance of in-domain training data, and (4) present
promising qualitative results on verifying claims about COVID-19 using
VeriSci.

###  6.1 Results

Table 1 shows the full-pipeline performance of VeriSci on the SciFact test
set, evaluated using the abstract-level and sentence-level metrics defined in
§4. The $F1_{\textrm{abstract}}$ value of 46.5 indicates that, for roughly
half of the claim-abstract pairs, VeriSci correctly identifies the Supports or
Refutes label _and_ provides reasonable evidence to justify the decision.
Given the difficulty of the task and limited in-domain training data, we
consider this a promising result, while leaving plenty of room for
improvement.

Oracle experiments |  | Sentence-level |  | Abstract-level
---|---|---|---|---
Abstract- | Rationale- | Label- |  |  |  |  |  |  |  |
Retrieval | Selection | Prediction |  | P | R | F1 |  | P | R | F1
VeriSci | Oracle | Oracle |  | 100.0 | 68.1 | 81.0 |  | 100.0 | 68.5 | 81.3
Oracle | VeriSci | Oracle |  | 75.1 | 75.7 | 75.4 |  | 100.0 | 84.2 | 91.4
Oracle | Oracle | VeriSci |  | 89.6 | 72.2 | 79.9 |  | 90.1 | 77.5 | 83.3
Oracle | VeriSci | VeriSci |  | 66.5 | 55.7 | 60.6 |  | 84.9 | 63.5 | 72.7
VeriSci | Oracle | VeriSci |  | 87.6 | 49.5 | 63.2 |  | 88.9 | 54.1 | 67.2
VeriSci | VeriSci | Oracle |  | 73.5 | 54.6 | 62.6 |  | 100.0 | 59.9 | 74.9
Final system |  |  |  |  |  |  |  |
VeriSci |  |  |  | 38.6 | 40.5 | 39.5 |  | 46.6 | 46.4 | 46.5
Table 1: Test set performance of our VeriSci system on SciFact, as measured by
the sentence-level and abstract-level performance evaluations defined in §4.2.
For the Oracle experiments, the first three columns in the table indicate
whether each module in the pipeline has been replaced by an oracle, or uses a
VeriSci system component. In Final system, we present the results of the full
VeriSci pipeline.

#### Oracle experiments

To examine the performance of each system component, we run the VeriSci
pipeline, replacing some components with “oracles” that always make correct
predictions when given correct inputs.111111For instance, the “Label” oracle
always predicts the correct label as long as it is provided with at least one
full gold rationale as input. The first three rows in Table 1 are _double-
oracle_ ; in these experiments, we isolate the performance of a single model
component together with two oracles. The next three rows are _single-oracle_ ,
and examine performance using two of the three model components combined with
one oracle.

Interestingly, the three pipeline components share similar levels of
responsibility for model errors as measured by $F1_{\textrm{sentence}}$. The
double-oracle models all have $F1_{\textrm{sentence}}$ values around 80. The
single-oracle models have values around 60, and the final system
$F1_{\textrm{sentence}}$ is roughly 40. Thus, replacing a single oracle
component introduces a loss of roughly 20 $F1_{\textrm{sentence}}$. These
results suggest that no single module is serving as a performance
“bottleneck”; improvements at each stage of the pipeline are likely to improve
overall performance.

#### Training datasets

During model development, we train the RationaleSelection and LabelPrediction
modules on four different datasets: Fever, UKP Snopes, SciFact, and Fever
pretraining followed by SciFact fine-tuning. The RationaleSelection module is
evaluated on its ability to identify rationale sentences given gold
abstracts.121212Our Fever-trained RationaleSelection module achieves 79.9
sentence-level F1 on the Fever test set, virtually identical to the value of
79.6 reported in DeYoung et al. (2019) The LabelPrediction module is evaluated
on its classification accuracy given gold rationales from evidence abstracts
(including evidence documents labeled NotEnoughInfo). The results of these
experiments are shown in Table 2. For RationaleSelection, training on SciFact
alone produces good results, perhaps because domain-specific lexical cues are
sufficient in most cases for identifying rationale sentences. For the more
complex reasoning involved in LabelPrediction, domain adaptation was the most
effective approach, training first on the large Fever dataset and then the
smaller in-domain SciFact training set. Based on these results, we use the
RationaleSelection module trained on SciFact only, and the LabelPrediction
module trained on Fever \+ SciFact for our final end-to-end system VeriSci.
Additional implementation details can be found in Appendix A.

| Rationale- | Label-
---|---|---
| Selection | Prediction
| P | R | F1 | Accuracy
Fever | 41.5 | 57.9 | 48.4 | 67.6
UKP Snopes | 42.5 | 62.3 | 50.5 | 71.3
SciFact | 73.7 | 70.5 | 72.1 | 75.7
Fever \+ SciFact | 72.4 | 67.2 | 69.7 | 81.9
Table 2: Comparison of different training datasets for RationaleSelection and
LabelPrediction, evaluated on the SciFact dev set.

###  6.2 Verifying claims about COVID-19

We conduct exploratory experiments using our system to fact-check claims
concerning COVID-19. We task a medical student to write 36 COVID-related
claims. For each claim $c$, we use VeriSci to predict evidence abstracts
$\widehat{{\mathcal{E}}}(c)$. The same medical student annotator assigns a
label to each $(c,\widehat{{\mathcal{E}}}(c))$ pair. A pair is labeled
_plausible_ if at least half of the evidence abstracts in
$\widehat{{\mathcal{E}}}(c)$ are judged to have reasonable rationales and
labels. It is labeled _missed_ if $\widehat{{\mathcal{E}}}(c)=\emptyset$.
Finally, it is labeled _implausible_ if the majority of the abstracts in
$\widehat{{\mathcal{E}}}(c)$ have irrelevant rationales or incorrect labels.

Table 3 shows two example claims, both with supporting and refuting evidence
identified by VeriSci. For the majority of these COVID-related claims (23 out
of 36), the rationales produced by VeriSci was deemed _plausible_ by our
annotator, demonstrating that VeriSci is able to successfully retrieve and
classify evidence in many cases. An examination of errors reveals that the
system can be confused by context, where abstracts are labeled Supports or
Refutes even though the rationale sentences reference a different disease or
drug from the claim. An example of this is also provided in Table 3.

Claim: Lopinavir / ritonavir have exhibited favorable clinical responses when
used as a treatment for coronavirus.
---
Supports: The 54-year old male is the third patient diagnosed with COVID-19 in
Korea … Interestingly, after lopinavir/ritonavir (Kaletra, AbbVie) was
administered, $\beta$-coronavirus viral loads significantly decreased and no
or little coronavirus titers were observed.
Refutes:  The focused drug repurposing of known approved drugs (such as
lopinavir/ritonavir) has been reported failed for curing SARS-CoV-2 infected
patients.. It is urgent to generate new chemical entities against this virus …
Wrong context: There are no approved treatments for MERS-CoV infection
although a combination of lopinavir, ritonavir and interferon beta … In mice,
both prophylactic and therapeutic RDV improve pulmonary function and reduce
lung viral loads and severe lung pathology.
Claim: The coronavirus cannot thrive in warmer climates.
Supports:  …most outbreaks display a pattern of clustering in relatively cool
and dry areas…This is because the environment can mediate human-to-human
transmission of SARS-CoV-2, and unsuitable climates can cause the virus to
destabilize quickly…
Refutes:  …significant cases in the coming months are likely to occur in more
humid (warmer) climates, irrespective of the climate-dependence of
transmission and that summer temperatures will not substrantially limit
pandemic growth.
Table 3: Results of our system on several claims concerning COVID-19. In some
cases, the label is predicted given the wrong context, e.g. the third evidence
sentence for the first claim is a finding about Lopinavir, but for the wrong
disease (MERS-CoV).

##  7 Discussion and Future Directions

Though SciFact represents progress in scientific fact-checking, we look
forward to making further improvements. In several cases described below, we
attempt to collect more fine-grained data for certain subtasks, but are
impeded by annotation challenges. We also discuss how the task of scientific
fact-checking can be naturally extended to involve evidence synthesis.

###  7.1 Partial evidence

During pilot annotations for entailment labeling, annotators are instructed to
label abstracts as one of Supports, PartiallySupports, NotEnoughInfo,
PartiallyRefutes, or Refutes. The Perspectrum dataset Chen et al. (2019)
features a similar annotation scheme for annotating evidence in online
debates. The Partial categorization is useful in cases like the one shown in
Figure 5, where the abstract contains relevant evidence, but the context is
different (mouse vs. human). When an annotator selects a Partial label, they
are also instructed to _edit_ the claim being verified, making as few changes
as possible, such that the evidence would provide full support / contradiction
for the edited claim.

Figure 5: An abstract that partially supports a claim. The edited claim is
fully supported.

Unfortunately, inter-annotator label agreement is only 0.48 Cohen’s $\kappa$
on this more granular annotation task, largely due to disagreement over the
Partial label. This is unsurprising given the subjectivity of the task, and is
consistent with the findings from Chen et al. (2019). Based on this low
agreement, we completely remove partially-supported claims from the task
dataset.131313Though we make these claims and their edits available as a
supplement to the dataset. Improving agreement on partial labels is part of
ongoing work.

###  7.2 Modeling contextual information

Similarly, for claim verification, we initially instruct annotators to
identify _primary_ and _supplemental_ rationale sentences for each rationale.
Primary sentences are those that are needed to verify the claim, while
supplemental sentences provide important context missing from primary
sentences that are still necessary for appropriately selecting the Supports or
Refutes label. For example, in Figure 1, the claim specifies “in experimental
animals,” yet no part of the rationale sentence indicates that its content
applies to experimental animals. In this case, another sentence in the
rationale abstract supplying information that the experiment was conducted in
mice would qualify as a supplemental sentence for this rationale.

We provide some guidance on when and how to select supplemental sentences,
such as defining context to be aspects of the claim such as country or
population, or instructing annotators to select the first sentence in an
abstract that provides the supplementary information. However, agreement on
supplemental rationale sentences is low among annotators (Cohen’s $\kappa$ =
0.45). Consequently, we remove supplemental rationale sentences from the task
dataset, though we continue to work with annotators on improving agreement.

###  7.3 Evidence synthesis

Evidence synthesis Marshall et al. (2017) is the task of combining relevant
information across different sources to inform decision making. Evaluating the
veracity of a scientific statement is challenging, even for human experts. It
requires assessing the strength of conflicting evidence from documents of
varying degrees of support, credibility, and recency, and synthesizing the
results in a meaningful and actionable way.

Evidence synthesis is not a current part of our task definition. Though we do
not ask our system to make corpus-level decisions about a claim’s veracity,
the extracted evidence and entailment labels produced by VeriSci can naturally
be extended for evidence synthesis. However, because performance degrades with
each additional pipeline component, further understanding of the scientific
fact-checking task and its subtasks is necessary before such a system could be
useful in practice. Accurate representations of partial evidence and
contextual knowledge are necessary steps towards this goal.

##  8 Conclusion

Fact checking is important in the scientific domain because it allows us to
trace the sources and measure the veracity of scientific claims. These
abilities have emerged as particularly important in the context of the
reproducibility crisis in science and the rise of disinformation in society.
In this article, we formalize the definition of scientific fact checking, and
release a dataset (SciFact) and models (VeriSci) to support work on this task.

Scientific fact checking poses a set of unique challenges, pushing the limits
of neural models on complex language understanding and reasoning. Domain-
adaptation techniques show promise, but our findings suggest that additional
work is necessary to improve the performance of end-to-end fact-checking
systems. We also demonstrate how fact checking might work in practice, by
applying our system to the real-world problem of verifying claims related to
COVID-19. We hope that these resources encourage others to pursue and expand
upon our work, and to further shed light on the broader and more challenging
goal of scientific document understanding.

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##  Appendix A Model implementation details

All models are implemented using the Huggingface Transformers package Wolf et
al. (2019).

###  A.1 Parameters for the final VeriSci system

For the AbstractRetrieval module, VeriSci retrieves the top $k=3$ documents
ranked by TF-IDF similarity using unigram + bigram features. These parameters
are tuned on the SciFact development set.

For both the RationaleSelection and LabelPrediction modules, we experiment
with SciBERT Beltagy et al. , BioMedRoBERTa Gururangan et al. (2020), RoBERTa-
base, and RoBERTa-large. RoBERTa-large achieves the best development set
performance for both subtasks and is used in the final model.

When making predictions using the RationaleSelection module described in §5.1,
we find that the usual decision rule of predicting $\hat{z}_{i}=1$ when
$\tilde{z}_{i}\geq 0.5$ works well for models trained on SciFact. However, for
models trained on Fever and UKP Snopes, we achieve better performance by
tuning the classification threshold $t$, such that $\hat{z}_{i}=1$ when
$\tilde{z}_{i}\geq t$, on the SciFact dev set. The best threshold was
$t=0.025$ for VeriSci trained on Fever, and $t=0.75$ for VeriSci trained on
UKP Snopes.

###  A.2 Training the RationaleSelection module

We experiment with various learning rates when training SciBERT,
BioMedRoBERTa, RoBERTa-base, and RoBERTa-large. Below we describe the setting
for training RoBERTa-large.

For models trained on SciFact, we use an initial learning rate of 1e-5 on the
transformer base and 1e-3 on the linear layer. For Fever \+ SciFact, the
learning rate is set to 1e-5 for the entire model for pre-training on Fever
and fine-tuning on SciFact. We use a batch size of 256 through gradient
accumulation and apply cosine learning rate decay over 20 epochs to find the
best performing model on the dev set.

For models trained on Fever, we set the learning rate to 0.5e-5 for the
transformer base and 0.5e-4 for the linear layer. For models trained on UKP
Snopes, we set the learning rate 1e-5 for the transformer base and 1e-4 for
the linear layer. We find that these learning rates help the models converge.
We only train the model for 5 epochs because Fever and UKP Snopes are larger
datasets and the models converged within the first 5 epochs.

###  A.3 Training the LabelPrediction module

We adopt similar settings as we used for the RationaleSelection module and
only change the learning rate to 1e-5 for the transformer base and 1e-4 for
the linear layer for models trained on SciFact, Fever, and UKP Snopes.

##  Appendix B Detailed corpus statistics

We compute statistics separately for _structured abstracts_ , abstracts that
are organized into well-defined sections, and for _unstructured abstracts_.

Table 4 provides statistics summarizing the lengths of abstracts and
rationales. Table 5 shows the counts for each claim-abstract label category in
the train, dev, and test sets. Table B shows the number of evidence documents
supporting each claim. The majority of claims are supported by a single
document set.

Figure 6(a) shows the distribution of the number of rationales in structured
and unstructured abstracts. Structured abstracts are more likely to have two
evidence sets – for instance, one in the “results” section, and one in the
“conclusions” section. Figure 6(b) shows the distribution of sentences per
rationale.

Figure 7 shows the fraction of sentences in each abstract that are part of a
rationale. Unstructured abstracts have a heavier “right tail”, representing
cases where the abstract is short and the entire abstract supports the claim.

MeSH terms for evidence documents appear in Figure 8. Terms like _Human_ ,
_Risk factors_ , and _Treatment outcome_ are common to randomized control
trial reports. Terms like _DNA_ , _RNA_ , and _Cell differentiation_ indicate
molecular biology research.

| Unstructured | Structured
---|---|---
# evidence abstracts | 340 | 146
Abstract length (median) | 7 | 13
Number of rationales (mean) | 1.53 | 1.89
Rationale fraction (median) | 0.14 | 0.17
Table 4: Summary statistics on the abstracts in the corpus. The _Abstract length_ is measured in number of sentences. The _Rationale fraction_ is the fraction of sentences in each abstract that are rationales. | Supports | NotEnoughInfo | Refutes | Total
---|---|---|---|---
Train | 332 | 304 | 173 | 809
Dev | 124 | 112 | 64 | 300
Test | 100 | 100 | 100 | 300
Table 5: Distribution of claim-abstract labels by train-dev-test split in
SciFact.

[autobooktabular, separator=tab, table head= # evidence abstracts Count
] tables/n-evidence-docs.tsvindex=, 0=

Table 6: The number of evidence documents supporting each claim. For instance,
37 claims are supported by 2 documents.

(a) The number of rationales supporting each claim. For instance, roughly 150
unstructured abstracts contain 2 rationales.

(b) The number of sentences per rationale.

Figure 6: Statistics on rationales. Most claims are supported by a single
rationale, but structured abstracts are more likely to have two rationales.
The great majority of rationales are a single sentence.

Figure 7: Distribution showing the fraction of each abstract (in sentences)
that is part of a rationale. Figure 8: Fraction of evidence abstracts in which
each MESH term occurs.

[autobooktabular, separator=tab, table head= Journal Structured Unstructured
Total
] tables/journal-counts.tsvjournal=, Structured=, Unstructured=, Total=

Table 7: Number of cited documents by journal.

##  Appendix C Annotation interfaces

The claim and evidence interfaces are shown in Figures 9 and 10.

Figure 9: The claim-writing interface. The citation sentence is highlighted in
blue on the top left. Additional context is provided on bottom left. The right
side shows two claims that could be written based on this citation sentence.
Figure 10: The evidence collection interface.