# Data-driven Approach for Automatically Correcting Faulty Road Maps

###### Abstract

As maintaining road networks is labor-intensive, many automatic road
extraction approaches have been introduced to solve this real-world problem,
fueled by the abundance of large-scale high-resolution satellite imagery and
advances in data-driven vision technology. However, their performance is
limited to fully automating road map extraction in real-world services. Hence,
many services employ the human-in-the-loop approaches on the extracted road
maps: semi-automatic detection and repairing faulty road maps. Our paper
exclusively focuses on the latter, introducing a novel data-driven approach
for fixing road maps. We incorporate image inpainting approaches to tackle
complex road geometries without custom-made algorithms for each road shape,
yielding a method that is readily applicable to any road map segmentation
model. We demonstrate the effectiveness of our method on various real-world
road geometries, such as straight and curvy roads, T-junctions, and
intersections.

Index Terms—  Road Geometry Construction, Image Inpainting, Generative
Adversarial Networks

##  1 Introduction

Developing and maintaining accurate road maps is essential for many location-
based applications. However, keeping the road maps up-to-date is an arduous
process that often requires extensive manual labor [1]. Hence, recent advances
have been dominated by utilizing aerial and satellite imagery that offer
abundant, reliable, high-resolution, and real-time data [2, 3]. Its properties
are ideal for deep learning-based approaches such as semantic segmentation
[4], which predict the existence of roads.

To further enhance the performance of the segmentation model, post-processing
methods are often applied [5, 6, 7]. While delegating the extraction of the
overall geometry of roads to the segmentation models, post-processing methods
concentrate on the finer details: restoring the missing connections between
roads, removing noisy predictions, and generating final binary maps from real-
valued predictions. However, in many cases, heuristics are often too simple to
tackle real-world road structures with complex geometry, often hard to
categorize into a few geometric primitives [8].

(a) T-junction

(b) Intersection

Fig. 1:  GT, Input, and Output denotes the ground truth, the input image that
our model receives, and its road inpainting result. Our method successfully
reconstructs the real-world complex road types such as T-junctions or
intersections by capturing the geometric information of the surroundings.

To tackle the limitations above, human-in-the-loop approaches are often used
in industry-level applications, making the manual labor of labeling road maps
much more efficient via automation. A two-step approach is commonly employed
to maintain existing road maps: error localization and automatic mending [9].
Error localization recommends locations to be fixed to the labeling force,
where automatic mending tries to mend the road maps without human
intervention, giving a good prior for the human annotators to fix further.

Our paper focuses on the automatic mending step, to introduce a novel data-
driven method that leverages the abundant amount of satellite imagery
available. We borrow the image inpainting techniques [10, 11, 12], by
regarding a faulty map region to be an area to be inpainted. Our method is
easily applicable as it does not require bells and whistles and is intuitive
to use. Further, unlike hand-crafted heuristics, our method naturally improves
its predictive performance as data size increases. To demonstrate the
effectiveness of our approach, we separately evaluate on complex structures
such as curvy roads, T-junctions, and intersections, where existing heuristic
methods are known to fail. Our experiments confirm our approach’s superiority,
qualitatively and quantitatively.

To summarize, our contribution is twofold: (1) introduce a novel data-driven
approach for mending faulty road maps based on image inpainting techniques,
and (2) demonstrate that our approach is superior via extensive evaluation
with various road geometries.

##  2 Related Works

###  2.1 Automatic Road Map Extraction

Creating road maps is a laborious process; annotators often look at overhead
views of the roads one by one, being more problematic at scale. On the other
hand, crowd-sourced efforts such as OpenStreetMaps [13] provide scalability,
even though they often suffer low resolution and misalignment. To mitigate the
problem, automatic road detection has been studied for more than 30 years
[14]. However, reliable extraction has been challenging due to the inherent
nature of roads which contain various geometries and non-road objects such as
vegetation, occlusions, and shadows [15, 16].

With the abundance of high-resolution satellite and aerial imagery, deep
learning-based approaches became dominant. However, their performance often
degrades for complex road geometries [17]. For instance, segmentation models
based on fully convolutional networks [18] often fail to capture the overall
geometry of roads, lacking spatial consistency and road connectivity [17].

###  2.2 Post-processing Road Maps

Algorithm-driven post-processing efforts to fix incomplete road maps often
concentrate on refining the quality of extracted roads from segmentation
models. To reduce the prediction noise, state-of-the-art methods often employ
heuristics that are tailored toward each specific model, such as morphological
operation techniques [5, 6, 7], focusing on non-road objects, such as vehicles
or trees. Others adopt polynomial curve fitting to thin the extracted road
geometric lines from the road detection system [19]. However, both approaches
do not improve the quality from a road-geometric perspective, as it cannot fix
the predictions if the provided road geometry is wrong. Some further utilize
additional information, such as global navigation satellite system
trajectories [20] to estimate road centerlines [21] or intersections [22].
These approaches are not applicable if trajectory data are not available or
sparse on the area of interest. A more thorough survey of custom heuristics
can be found at [3].

###  2.3 Road Geometry Error Localization

In the production setting, one cannot blindly trust the existing road maps due
to errors from both human and model predictions. However, relying only on
human annotations to detect and fix faulty road maps is often too expensive.
Hence, map companies often employ a semi-automated human-in-the-loop procedure
where the model and humans work together [23]. For example, occlusions such as
shadows, cars, and buildings that are similar to road features, such as
colors, reflectance, and patterns in aerial imagery, are known obstructions
for extracting road information [24]. Hence, non-road object detection models
[25, 26] are often employed to avoid manual labeling of bounding boxes [27].
Furthermore, complex road shapes, such as roundabouts, T-junctions,
intersections, or irregular curvy roads, can be prioritized for mending, where
it can be automatically detected [28].

###  2.4 Image Inpainting

Image inpainting models are used to inpaint (fill in) missing pixels of
photographic images to make the completed images look realistic. Context
Encoder [10], Globally and Locally Consistent Image Completion (GLCIC) [11]
and Free-Form Image inpainting with Gated Convolution [12] have shown
achievement in natural images. However, these models are not optimal for
restoring missing pixels of geometric images such as roads. Hence, we improve
the existing approaches to successfully mend road geometries in Section 3.

##  3 Automatically Mending Faulty Road Map Predictions

We introduce a novel data-driven approach for mending faulty road map
predictions motivated by the following idea: Given the abundance of existing
images of road maps, why not teach the geometric specifics of roads to a
neural network in a self-supervised manner? Based on this intuition, we
leverage the existing image inpainting model and automatically apply
modifications to serve the geometric characteristics of road maps. We describe
our neural network architecture in Sec. 3.1 and the customized loss in Sec.
3.2.

###  3.1 Model Architecture

Our model aims to inpaint the geometric road shape in the target area. As
mentioned previously in Sec. 1 and Sec. 2, we assume that the erroneous
location is already localized. We modify the model architecture of GLCIC [11]
to capture the spatial relationship between the target road segment and the
surrounding geometries. We denote our model architecture GLCRC (Globally and
Locally Consistent Road map Completion). GLCRC consists of generator and
discriminator.

The generator has 6 conv layers followed by 8 dilated conv layers, with an
increasing dilation factor from 2 to 9. They enable the model to learn the
geometric relationship between the road shape of the area to be inpainted
(i.e. target area) and the surroundings. This is why it is important to keep a
high dilation factor; to learn the spatial context as a whole via bigger
receptive field. Unlike natural images, which often have the freedom of
inaccuracy at a granular level, the road geometries have to be meticulously
correct, harmonized with both neighboring roads and the overall geometry.

The discriminator is based on the multi-scale discriminator architecture [11],
containing a global and a local context discriminator. We utilize this
architecture to handle the global and local context of the road maps, grasping
the overall geometry with its details. The global context discriminator takes
256 $\times$ 256 input and feed-forward to 6 conv layers and final linear
layer. The local context discriminator takes 128 $\times$ 128 image patch,
which is a target region to fill in. We concatenate two context vectors to
pass through a linear layer and a sigmoid activation to represent the
probability that a given inpainted image is plausible or not.

###  3.2 Loss function

To achieve better geometric line quality, we applied perceptual loss [29] to
avoid the blurriness and Relativistic Least Square GAN Loss (RaLSGAN) [30] to
achieve sharper maps. The goal of loss functions is to achieve correctness
while increasing the sharpness of the inpainted road geometries.

Our generator network is trained with the perceptual loss unlike the original
GLCIC which uses Mean Square Error (MSE) loss. We observed that MSE loss is
suboptimal for the road maps compared to the perceptual loss, as it produces
more blurry road predictions (Figure 2). Note that the sharpness of the
inpainted geometric lines depends on the degree of texture distortion along
the road lines.

For both global context discriminator and local context discriminator, we
adopt RaLSGAN loss. The vanilla GLCIC model use the Binary Cross Entropy (BCE)
loss. However, empirical findings indicate that RaLSGAN loss helps to generate
more textually clean and sharper lines [30], which is a desirable
characteristic for road maps.

(a) GLCIC with MSE Loss

(b) GLCIC with Perceptual Loss

Fig. 2:  Decreased road map blurriness by utilizing the perceptual loss: (a)
MSE loss, and (b) perceptual loss. We mask the road maps (rectangles in the
first row) so that the generator can reconstruct the roads within the mask
(second row).

##  4 Experiments

We experimentally demonstrate the effectiveness of our approach by comparing
three cases: (1) Vanilla GLCIC, (2) GLCRC (architectural modifications on
GLCIC, Sec. 3.1), and (3) GLCRC with better losses (Sec. 3.2). We provide the
source codes of GLCRC for additional
details.111https://github.com/SoojungHong/image_inpainting_model_for_lane_geomery_discovery
We construct the remaining section as follows. We first describe the dataset
and the training details (Sec. 4.1). Then, we show the experimental results on
various road types (Sec. 4.2).

###  4.1 Dataset and Training Details

All the models were trained and evaluated with Massachusetts Road Geometry
data [31] which covers over 2600 square kilometers with diverse rural,
suburban, and urban areas [17] with a 1m spatial resolution and 1500 $\times$
1500 pixel size, containing 1711 road images. We divided each image into nine
500 $\times$ 500 images, then resized into 256 $\times$ 256\. In all of our
experiments, we trained and tested the model with 9972 and 567 images,
respectively.

Method | Correctness | Completeness | Quality
---|---|---|---
Vanilla GLCIC | 0.787 | 0.803 | 0.664
GLCRC | 0.789 | 0.811 | 0.668
GLCRC+L (Ours) | 0.795 | 0.831 | 0.671

Table 1:  Road map fixing performance. GLCRC modified the architecture of
vanilla GLCIC and GLCRC+L changed the loss to perceptual and RaLSGAN loss.

(a)

(b)

(c)

Fig. 3:  Road inpainting results of Vanilla GLCIC (Baseline), GLCRC (Enhanced
Architecture), and GLCRC+L (Ours, GLCRC with perceptual loss and RaLSGAN loss)
on various road types, (a) Intersection, and (b, c) Curvy road.

We followed the same training scheme as GLCIC [11]. We pretrained the
generator and the discriminator network separately for 90k and 40k steps,
respectively. This pretraining approach is often used to train GANs [11] to
avoid the instability of adversarial training. After completing the
pretraining of each network, the generator and the discriminator are trained
in an alternating manner for 90k iterations.

Road type | Method | Correctness | Completeness | Quality
---|---|---|---|---
Straight | Vanilla GLCIC | 0.787 | 0.786 | 0.649
GLCRC | 0.750 | 0.806 | 0.635
GLCRC+L (Ours) | 0.894 | 0.898 | 0.811
Curvy | Vanilla GLCIC | 0.762 | 0.757 | 0.613
GLCRC | 0.723 | 0.789 | 0.606
GLCRC+L (Ours) | 0.754 | 0.766 | 0.613
T-junction | Vanilla GLCIC | 0.775 | 0.788 | 0.642
GLCRC | 0.785 | 0.792 | 0.651
GLCRC+L (Ours) | 0.842 | 0.849 | 0.733
Intersection | Vanilla GLCIC | 0.775 | 0.788 | 0.642
GLCRC | 0.785 | 0.792 | 0.651
GLCRC+L (Ours) | 0.786 | 0.793 | 0.652

Table 2:  Comparing road map fixing performance on various road geometries.
GLCRC denotes architectural modifications to the vanilla GLCIC and GLCRC+L
denotes GLCRC with perceptual and RaLSGAN loss.

###  4.2 Experimental Results

We conduct quantitative (Table 1) and qualitative analysis (Figure 1) on
whether the modifications are effective. We use three metrics, Correctness,
Completeness, and Quality, which are widely used in road extraction tasks [6]
Further, we also evaluate on various road types (Table 2) to verify to check
the method’s robustness. We choose four random road maps per each road type:
Straight, Curvy, T-junction, and Intersection, where the latter three types
are known to be challenging for post-processing methods.

Table 1 demonstrates the performance impact on various modifications: GLCRC
being the architectural and GLCRC+L being the training loss modifications. We
can clearly observe that our approach shows the best image quality in the road
geometry inpainting problem. Furthermore, Figure 1 shows the effectiveness of
our method on various road types, implying that it understands underlying road
geometries to reconstruct the road location.

We also compare and evaluate the modifications on separate road types
qualitatively (Figure 3) and quantitatively (Table 2). Figure 3 demonstrates
that complex road shapes such as multiple T-junction areas and irregular curvy
roads are successfully handled via our method. Even though architectural
changes can improve the overall road shapes, one has to employ perceptual loss
and RaLSGAN loss to enhance the smoothness and the sharpness of roads.
Further, Table 2 indicates that ours shows superior performance in all the
complex road types and evaluation metrics.

##  5 Conclusion

Even though numerous advances have been made to the automated extraction of
road maps from satellite imagery fueled by the abundance and availability of
data, many still rely on hand-crafted heuristics and algorithms to fix faulty
road map predictions. Our work shed new light on the road-mending techniques
motivated by the data-driven machine learning algorithms that prevail in
vision applications today. We introduce a novel method based on the image
inpainting technique by regarding faulty road map predictions as the image to
inpaint. We modify the existing image inpainting approaches to handle the
geometric context while maintaining accuracy and crispness for road maps. Our
method does not require specific tuning on the numerous real-world road types
nor additional trajectory data to interpolate; it only needs a sufficient
amount of data to enhance the predictions. Experiments demonstrate that our
method is robust to various real-world road geometries such as curvy,
T-junction, and intersection, which are challenging for existing post-
processing methods.

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# AraFinNLP 2024: The First Arabic Financial NLP Shared Task

Sanad Malaysha1 Mo El-Haj2 Saad Ezzini2 Mohammed Khalilia1 Mustafa Jarrar1
Sultan Almujaiwel3 Ismail Berrada4 Houda Bouamor5
1Birzeit University, Palestine 2Lancaster University, United Kingdom
3King Saud University, Saudi Arabia 4Mohammed VI Polytechnic University,
Morocco
5Carnegie Mellon University, Qatar

###### Abstract

The expanding financial markets of the Arab world require sophisticated Arabic
NLP tools. To address this need within the banking domain, the Arabic
Financial NLP (AraFinNLP) shared task proposes two subtasks: ($i$) Multi-
dialect Intent Detection and ($ii$) Cross-dialect Translation and Intent
Preservation. This shared task uses the updated ArBanking77 dataset, which
includes about $39$k parallel queries in MSA and four dialects. Each query is
labeled with one or more of a common 77 intents in the banking domain. These
resources aim to foster the development of robust financial Arabic NLP,
particularly in the areas of machine translation and banking chat-bots. A
total of $45$ unique teams registered for this shared task, with $11$ of them
actively participated in the test phase. Specifically, $11$ teams participated
in Subtask 1, while only $1$ team participated in Subtask 2. The winning team
of Subtask 1 achieved $F_{1}$ score of $0.8773$, and the only team submitted
in Subtask 2 achieved a $1.667$ BLEU score.

AraFinNLP 2024: The First Arabic Financial NLP Shared Task

Sanad Malaysha1 Mo El-Haj2 Saad Ezzini2 Mohammed Khalilia1 Mustafa Jarrar1
Sultan Almujaiwel3 Ismail Berrada4 Houda Bouamor5 1Birzeit University,
Palestine 2Lancaster University, United Kingdom 3King Saud University, Saudi
Arabia 4Mohammed VI Polytechnic University, Morocco 5Carnegie Mellon
University, Qatar

##  1 Introduction

Financial Natural Language Processing (FinNLP) is revolutionising the
financial sector, offering unmatched potential to enhance decision-making,
manage risks, and drive operational efficiency (Zavitsanos et al., 2023). By
leveraging advanced linguistic analysis (Malaysha et al., 2024) and NLP
algorithms (Barbon Junior et al., 2024), FinNLP optimises processes and
streamlines workflows, delivering a myriad of benefits (Darwish et al., 2021).
FinNLP enables the extraction of key information, including events (Aljabari
et al., 2024), relationships (Jarrar, 2021), and named entities Liqreina et
al. (2023), from diverse sources such as financial reports, news articles,
invoices, and social media posts.

Figure 1: AraBanking2024 Datasets for Intent Detection

Practical applications of Financial NLP include textual analysis in accounting
and finance Loughran and McDonald (2016); El-Haj et al. (2019, 2020), analysis
of financial transactions Jørgensen and Igel (2021), customer complaints
Jarrar (2008), and text classification Arslan et al. (2021); El-Haj et al.
(2014). Nevertheless, as recently highlighted by Jørgensen et al. (2023); El-
Haj et al. (2021), the majority of financial NLP research is conducted in
English.

On the other hand, the Arab world’s financial landscape is experiencing robust
growth, attracting global attention and investment across diverse sectors.
This expansion underscores the critical role of Financial NLP in understanding
and interpreting the intricacies of Arabic financial communications. As
highlighted by Zmandar et al. (2021, 2023), the dynamic nature of Middle
Eastern stock markets reflects the region’s evolving financial environment,
necessitating advanced NLP tools tailored to local linguistic nuances.

In this paper, we provide an overview of the AraFinNLP-2024 Shared
Task111Task: https://sina.birzeit.edu/arbanking77/arafinnlp/, which represents
a significant step forward in advancing the development of Arabic NLP
capabilities within the finance domain. We propose two subtasks aimed at
addressing key challenges in the banking sector: ($i$) Multi-dialect Intent
Detection and ($ii$) Cross-dialect Translation and Intent Preservation. For
this shared task, we provided participants access to the ArBanking77222Corpus:
https://sina.birzeit.edu/arbanking77/ corpus Jarrar et al. (2023b), for
training purposes, which contains about $31$k queries in Modern Standard
Arabic (MSA) and Palestinian dialect, each labeled by at least one of the 77
intents in the banking domain. The data excluded any examples covering the
Moroccan, Tunisian and Saudi dialects. We discuss the test data preparation in
Section 4. The two subtasks are designed to tackle the complexities inherent
in interpreting and managing diverse banking data prevalent in Arabic-speaking
regions, catering to the linguistic diversity across the Arab world (Haff et
al., 2022). By focusing on intent detection and dialectical translation,
AraFinNLP aims to enhance customer service, automate query handling, and
facilitate seamless communication across various Arabic dialects, thus
fostering inclusivity and efficiency in financial services.

The rest of the paper is organized as: Section 2 presents the related work;
Section 3 describes the tasks; Section 4 presents the dataset; Section 5
reports on the performance of the participating systems; and, Section 6
concludes.

##  2 Related Work

In the rapidly evolving field of NLP, integrating financial data has become a
significant area of research, particularly in the context of multilingual and
dialectal variations.

Recent studies in the financial NLP (FinNLP) domain have investigated machine
translation between MSA and dialectal Arabic Zmandar et al. (2021). Noteworthy
contributions to FinNLP have been made by Zmandar et al. (2023), who examined
the application of NLP techniques to analyse Arabic financial texts.
Additionally, Jarrar et al. (2023c) introduced the ArBanking77 dataset,
derived from the English Banking77 dataset Casanueva et al. (2020), which has
been pivotal in advancing research in this area. The related works can be
divided into four main categories: financial document extraction, lexical
diversity and sentiment analysis, cross-lingual and dialectal studies, and
related machine learning techniques.

Within the realm of FinNLP, the process of Financial Document Extraction
emerges as a critical component in the financial industry’s digital
transformation. This intricate process entails the automated retrieval and
meticulous analysis of pertinent data from a diverse array of financial
documents, including but not limited to invoices, receipts, and financial
statements. By harnessing cutting-edge methodologies such as semantic role-
labeling schemes and integrating deep NLP techniques with knowledge graphs
Hammouda et al. (2024), researchers have significantly elevated the accuracy
and efficiency of financial narrative processing Lamm et al. (2018); Cavar and
Josefy (2018); Abreu et al. (2019). These advanced techniques not only
streamline data extraction processes but also enable deeper insights into
complex financial structures and trends, thereby empowering decision-makers
with actionable intelligence to drive strategic initiatives, mitigate risks,
and seize opportunities in a rapidly evolving financial landscape. Through the
seamless integration of technology and financial expertise, FinNLP continues
to revolutionise traditional workflows, facilitating agile decision-making and
fostering innovation across the financial sector.

In addition to document extraction, FinNLP extensively explores the realms of
lexical diversity and sentiment analysis. The assessment of lexical diversity
delves into the richness and variety of vocabulary employed within financial
texts. Concurrently, sentiment analysis endeavours to decipher the emotional
nuances embedded in financial communications. For instance, researchers have
scrutinised sentiment patterns in Arabic financial tweets, shedding light on
the linguistic intricacies prevalent in such contexts Alshahrani et al.
(2018). Similarly, investigations into expressions of trust and doubt within
financial reports underscore the emotional dimensions inherent in financial
discourse Žnidaršič et al. (2018); Akl et al. (2019). By elucidating these
linguistic subtleties, FinNLP not only enhances our understanding of financial
communications, but also empowers stakeholders to navigate and interpret
complex data with confidence and clarity.

Although MSA and Arabic dialects have been studied, mainly from morphological
(and semantic) annotations and dialect identification perspectives Nayouf et
al. (2023); Jarrar et al. (2023e); Abdul-Mageed et al. (2023); Al-Hajj and
Jarrar (2021), less attention was given to intent detection. In terms of
cross-lingual and dialectal studies, important works examine how languages or
dialects interact and translate across cultural and linguistic boundaries,
focusing on understanding and processing variations in language usage and
meaning. Kwong (2018) addresses the challenges of cross-lingual financial
communications by focusing on analyzing and annotating English-Chinese
financial terms. The proposal of a multilingual financial narrative processing
system by El-Haj et al. (2018) highlights the necessity for tools that
function across diverse languages.

Štihec et al. (2021) used forward-looking sentence detection techniques to
anticipate future statements in financial texts. These efforts highlight the
varied methodologies in financial NLP, from structural data extraction to
predictive analytics, while noting a gap in addressing Arabic linguistic
nuances. Naskar et al. (2022) unveiled a transformer-based architecture to
detect causality in financial texts, and Tsutsumi and Utsuro (2022) employed a
BERT-based model to discern stock price triggers from news, enhancing the
understanding of nuanced financial changes. These studies advance automatic
text processing, although they primarily focus on English datasets and
traditional NLP tasks. Another study by Zmandar et al. (2022) focused on the
French financial sector, developing CoFiF Plus, a corpus for summarizing
French financial narratives, highlighting the need for tools that cater to
non-English financial data. In the last year, NLP has seen significant
advances in the understanding and generation of financial narratives across
various languages Zmandar et al. (2023) introduced FinAraT5, a text-to-text
model tailored for Arabic financial text understanding and generation,
emphasising the importance of language-specific models in financial contexts.

While exploring the frontier of FinNLP, it is essential to acknowledge the
broader landscape of shared tasks and initiatives aimed at understanding
Arabic MSA and dialects. These include notable endeavours such as ArabicNLU
for word-sense disambiguation Khalilia et al. (2024); Jarrar et al. (2023d),
NADI for dialect identification Abdul-Mageed et al. (2023), and WojoodNER for
named entity recognition Jarrar et al. (2024, 2023a). Notably, these tasks
extend their focus to entities within the finance and banking domains Jarrar
et al. (2022); Liqreina et al. (2023), aligning closely with the objectives of
FinNLP. Through collaborative efforts and interdisciplinary research, these
shared tasks complement the advancements in FinNLP, fostering a holistic
understanding of linguistic nuances and enhancing the applicability of NLP
techniques within financial contexts. As FinNLP continues to evolve,
leveraging insights from these shared tasks can further enrich its
capabilities, ultimately driving innovation and efficacy in financial
analysis, decision-making, and risk management.

##  3 Task Description

The AraFinNLP shared task comprises two subtasks aimed at advancing Financial
Arabic NLP: Subtask 1 focuses on Multi-dialect Intent Detection, while Subtask
2 addresses Cross-dialect Translation and Intent Preservation within the
banking domain. Nonetheless, AraFinNLP is the first shared task in Arabic
financial NLP, as well as the first Arabic multi and cross dialects, where
banking intents predicted and preserved across four dialects translated from
MSA and English. In this section we break into details of each sub-task.

###  3.1 Subtask 1: Multi-dialect Intent Detection

Subtask 1 of the AraFinNLP shared task revolves around Multi-dialect Intent
Detection in the banking domain. Participants are tasked with developing NLP
models capable of accurately classifying customer intents from queries in
diverse Arabic dialects, taking into consideration that dialects classes are
hidden from participants. The challenge lies in training models that can
understand both MSA and regional dialects, such as Gulf, Levantine, and North
African, to enhance customer service and automate query handling. Figure 2
shows example of queries in different dialects with their corresponding
intents.

Figure 2: Translation of MSA into four Arabic dialects.

###  3.2 Subtask 2: Cross-dialect Translation and Intent Preservation

In Subtask 2 of the AraFinNLP shared task, participants focus on Cross-dialect
Translation and Intent Preservation. The objective is to translate queries
from MSA language to various Arabic dialects, while ensuring the preservation
of the original intent as shown in Figure 2. The target dialects are limited
to Gulf (Saudi), Moroccan (Darija), Palestinian, and Tunisian. Participants
are provided with datasets containing MSA queries and their corresponding
intents, tasked with accurately translating them into dialectal Arabic while
maintaining semantic integrity.

###  3.3 Restrictions

The followed norm in the shared tasks is to set a lot of strict restrictions
on the usage of external data and online resources. However, since these tasks
related to Arabic language and dialects which have low resources (Malaysha et
al., 2023), we relaxed the restrictions and allowed participants to exploit
any resources at their disposal including pre-trained encoders, generative
models, and augmented datasets. Participants have the freedom to incorporate
diverse online resources to enhance their models further, fostering a broader
exploration of methodologies and potentially leading to more innovative
solutions in intent detection and dialectical translation within the banking
domain and beyond.

However, we dictated the submission format. Because we utilized CodaLab
333https://codalab.lisn.upsaclay.fr/ framework for scoring the participants’
results, we define a JSON-based structure for representing their submission in
both sub-tasks, submission details are posted in the official shared task
website444https://sina.birzeit.edu/arbanking77/arafinnlp/ of the shared task.

##  4 Dataset

The shared task provided a multi and cross dialectal Arabic banking-related
dataset, which consists of queries translated into MSA and various Arabic
dialects, including Palestinian, Saudi, Tunisian, and Moroccan (Figure 1). The
queries are classified into intent classes selected from 77 intents, where
each query could match to one or more intent. The initial data was obtained
from ArBanking77 dataset (Jarrar et al., 2023c), available in MSA and
Palestinian dialects. For this shared task, we augmented the ArBanking77
dataset with three additional dialects. We cooperated with teams of linguists
each specialized in each dialect. Table 1 outlines the statistics of the final
dataset.

Dialect | Train | Development | Test
---|---|---|---
MSA | 10,733 | 1231 | 3,574
Moroccan | – | – | 3,574
Palestinian | 10,821 | 1234 | 3,574
Saudi | – | – | 3,574
Tunisian | – | – | 1,000
Total | 21,554 | 2,465 | 15,296
Table 1: Statistics of the AraFinNLP shared task dataset.

Moroccan Dialect: the team first translated the original sentences from
English and MSA to Moroccan Darija using GPT-4 and Meta’s seamless-m4t-v2
models (Barrault et al., 2023). A team of seven native Moroccan Darija
speakers from various regions manually reviewed and corrected the translations
for accuracy. They divided the dataset into seven parts, each checked by one
annotator. The annotators were asked to refer to both MSA and English queries
when the translation was ambiguous. Each translated sentence was carefully
reviewed, resulting in approximately 67% of them being edited. Lastly, two
additional annotators conducted a final review of all the data to ensure
consistency.

Saudi and Tunisian Dialects: one specialized linguist in each of these two
dialects worked on translating data from MSA to the Saudi and Tunisian
dialects. As they are native speakers of these dialects, they ensured the
translations captured all the linguistic nuances of the Najdi dialect for
Saudi and local terms for Tunisian, preserving the true meaning of the text.
During the process, they carefully double-checked the translation to address
any typos or ambiguity.

##  5 Results and Discussion

The AraFinNLP shared subtasks attracted a good number of teams, with a diverse
array of participants employing various techniques and methodologies tailored
to the challenges of multi-dialectal intent detection and cross-dialect
translation in the banking domain. Leveraging a range of NLP models and
approaches, including fine-tuning pre-trained transformers and utilising deep
learning models, participating teams navigated the complexities of Arabic
dialects and the nuances inherent in financial communications Khalilia et al.
(2024); Jarrar et al. (2023d); Abdul-Mageed et al. (2023); Jarrar et al.
(2024, 2023a, 2022); Liqreina et al. (2023).

With queries in MSA and several Arabic dialects, extractive techniques were
predominantly employed. The AraFinNLP shared task results (Table 2) provide
insights into the effectiveness of different methodologies and approaches in
addressing the unique challenges of Financial Arabic NLP.

The evaluation metrics for each subtask offer a comprehensive overview of
system performance across various dialects and tasks, facilitating comparisons
and informing future research directions in Arabic NLP, particularly within
the finance domain.

###  5.1 Participating Teams and Results

A total of 45 unique teams registered for the AraFinNLP shared task, with 11
teams effectively participating and submitting systems to Subtask 1 and
Subtask 2, leading to 30 submissions during the development phase and 168
submissions during the test phase.

For Subtask 1, which involves multi-class classification, the $F_{1}$ micro
score was the primary metric for ranking and comparison. Additionally,
secondary metrics such as macro precision and recall were provided for further
reference in the evaluation process. Subtask 2 utilised the BiLingual
Evaluation Understudy (BLEU) score as the primary metric for ranking and
comparison, with secondary metrics like CHaRacter-level F-score (chrF) and
Translation Error Rate (TER).

The results of the AraFinNLP shared task, presented in Table 2, reveal that
the MA team achieved the highest Micro-$F_{1}$ score of 0.8773 during the test
phase, securing the top rank. This impressive performance can be attributed to
their methodology, which involved using an ensemble of fine-tuned BERT-based
models and integrating contrastive loss for training. This approach likely
provided a robust way to handle the nuanced differences in Arabic dialects and
ensured better generalisation across the diverse dataset. The MA team’s method
also included augmenting the ArBanking77 dataset with additional Arabic
dialects, which could have enriched the training data and improved the model’s
ability to generalise better on unseen data. Their approach stands out as it
meticulously addressed both model architecture and data augmentation, crucial
factors in achieving superior performance in NLP tasks involving complex and
diverse languages like Arabic. A detailed breakdown of the Subtask 1 results
for each team, including performance during both the development and test
phases, is presented in Table 2. Six out of 11 teams participated in the
development phase as the participation was optional. It is shown that the
SemanticCUETSync and SMASH teams ranked 1st and 2nd during the development
phase, but dropped to 5th and 9th place in the final evaluation phase. This
indicates that the developed solutions overfitted on the development data and
could not generalise to the additional dialects in the test set.

Submission ID | Codalab Username | Team Name | Test Phase | Dev Phase
---|---|---|---|---
Micro-$F_{1}$ | Rank | Micro-$F_{1}$ | Rank
758563 | AsmaaRamadan | MA | 0.8773 | 1 |  |
757837 | Hossam-Elkordi | AlexuNLP24 | 0.8762 | 2 | 0.9614 | 3
757408 | murhaf | BabelBot | 0.8709 | 3 | 0.9458 | 5
749856 | sultan | -NPS- | 0.8342 | 4 |  |
755226 | SemanticCUETSync | SemanticCUETSync | 0.8208 | 5 | 0.9852 | 1
755293 | abdelmomenbennasr | SENIT | 0.8204 | 6 |  |
755331 | Fired_from_NLP | Fired_from_NLP | 0.8014 | 7 | 0.9466 | 4
753646 | Haithem | -NPS- | 0.7894 | 8 |  |
747668 | yalhariri | SMASH | 0.7866 | 9 | 0.9639 | 2
748164 | licvol | dzFinNlp | 0.6721 | 10 | 0.9302 | 6
747581 | Nsrin_Ashraf | BFCI | 0.4907 | 11 |  |

-NPS-: No Paper Submission.

Table 2: Subtask 1 results breakdown by team for both development and test
phases.

###  5.2 Teams Description

The AraFinNLP shared subtasks attracted a diverse array of participants, each
employing unique methodologies tailored to the specific challenges of multi-
dialectal intent detection and cross-dialect translation within the banking
domain. Here are descriptions of each team that participated in the shared
task, highlighting their team name, task, methodology, and techniques used:

  1. 1.

BFCI: This team participated in the AraFinNLP2024 shared task, specifically in
the subtask of Multi-dialect Intent Detection. They employed traditional
machine learning approaches integrated with basic vectorization for feature
extraction. The primary algorithms used were Multi-layer Perceptron,
Stochastic Gradient Descent, and Support Vector Machines (SVM), with SVM
outperforming the others. The approach achieved a micro $F_{1}$ score of
0.4907.

  2. 2.

SENIT: Hailing from the National Engineering School of Tunisia, the SENIT Team
tackled the Multi-dialect Intent Detection subtask by fine-tuning several pre-
trained contextualised text representation models, including multilingual BERT
and Arabic-specific models like MARBERTv1 (Abdul-Mageed et al., 2021),
MARBERTv2555https://huggingface.co/UBC-NLP/MARBERTv2, and CAMeLBERT (Inoue et
al., 2021). They also employed an ensemble technique, combining MARBERTv2 and
CAMeLBERT embeddings, with MARBERTv2 achieving a micro $F_{1}$ score of
0.8204.

  3. 3.

dzFinNlp: Focused on improving intent detection in financial conversational
agents, the dzFinNlp Team experimented with various models and feature
configurations. They explored traditional machine learning methods like
LinearSVC with Term Frequency-Inverse Document Frequency (TF-IDF) and deep
learning models like Long Short-Term Memory (LSTM) and bidirectional LSTM
(BiLSTM). Additionally, transformer-based models were employed, achieving a
micro $F_{1}$-score of 0.6721.

  4. 4.

MA: From Alexandria University, the MA Team participated in the cross-
dialectal Arabic intent detection subtask using an ensemble of fine-tuned
BERT-based models. They integrated contrastive loss for training and augmented
the ArBanking77 dataset with additional Arabic dialects, achieving an
$F_{1}$-score of 0.8773, ranking first in the task.

  5. 5.

BabelBot: Participating in the Multi-dialect Intent Detection subtask, the
BabelBot Team employed an encoder-only T5 model fine-tuned for the task. They
generated synthetic data and used model ensembling to address cross-dialect
challenges, securing third place with a micro-$F_{1}$ score of 0.8709.

  6. 6.

SemanticCuETSync: This team worked on intent detection using a combination of
traditional machine learning and deep learning techniques. They implemented
models like LSTM and transformer-based models, focusing on enhancing feature
extraction and classification performance in the context of Arabic financial
text analysis. The approach achieved a micro $F_{1}$ score of 0.8208.

  7. 7.

AlexUNLP24: Hailing from the University of Edinburgh, the AlexUNLP24 Team
tackled the intent detection task using various BERT and BART-based models.
Their approach involved direct fine-tuning across all intents, with QARiB and
MARBERTv2 achieving the a micro $F_{1}$ score of 0.8762 and ranking second
place in the task. They found that translating the data to MSA impaired model
performance in multi-dialect settings.

  8. 8.

Fired from NLP: This team employed a bidirectional interrelated model for
joint intent detection and slot filling, leveraging both machine learning and
deep learning approaches. They focused on enhancing the accuracy of intent
detection in noisy and unstructured text data in financial conversational
settings. The approach achieved a micro $F_{1}$ score of 0.8014.

  9. 9.

SMASH: Utilising several BERT and BART-based models for the Multi-dialect
Intent Detection task, the SMASH Team’s experiments showed that MARBERTv2
outperformed other models using a two-step approach, achieving an $F_{1}$
score of 0.7866. Their work highlighted the challenges and opportunities in
Arabic financial NLP.

##  6 Conclusions and Future Work

In conclusion, the AraFinNLP shared task represents a significant effort in
advancing Financial Arabic Natural Language Processing by addressing critical
challenges in multi-dialect intent detection and cross-dialect translation
within the banking domain. The participation of diverse teams employing
various methodologies has yielded valuable insights into the effectiveness of
different approaches. These findings can inform future research, guiding the
development of more robust and accurate NLP models for handling the
complexities of Arabic dialects in financial contexts.

The results highlight the impressive performance of the MA team, whose use of
fine-tuned BERT-based models and contrastive loss for training, coupled with
data augmentation techniques, proved particularly effective. This underscores
the importance of both sophisticated model architectures and enriched training
datasets in achieving high performance in NLP tasks involving diverse
languages.

Looking ahead, future work in Financial Arabic NLP could explore several
avenues for further improvement and innovation. Enhancing the performance and
adaptability of NLP models across a wider range of Arabic dialects could lead
to more inclusive and effective solutions for financial communication.
Additionally, the development of specialized resources and datasets tailored
to specific financial domains and dialectal variations could facilitate more
targeted and accurate analysis of financial texts. Furthermore, ongoing
advancements in NLP technologies, such as the integration of multi-modal
inputs and the incorporation of domain-specific knowledge, offer promising
opportunities for improving the capabilities of Financial Arabic NLP systems.

The AraFinNLP shared task serves as a catalyst for advancing research and
development in Financial Arabic NLP, paving the way for more sophisticated and
versatile systems capable of addressing the diverse linguistic and
communicative needs of Arabic-speaking communities in the financial domain. By
fostering collaboration and innovation, the shared task contributes to the
broader goal of enhancing accessibility, efficiency, and inclusivity in
financial services through the application of natural language processing
technologies.

## Limitations

It is important to acknowledge the limitations inherent in the AraFinNLP
shared task and its associated datasets. One potential limitation is the
representation of Arabic dialects in the provided data, as dialectal
variations may not be fully captured or balanced across different regions.
Additionally, the complexity of financial communications and the nuances of
Arabic language usage may pose challenges for accurate interpretation and
analysis, particularly in cross-dialect translation tasks. Furthermore, the
availability of annotated data and resources for training NLP models in Arabic
dialects may be limited, potentially impacting the scalability and
generalisation of systems developed within the shared task.

## Ethics Statement

The datasets provided for this shared task are derived from public sources,
eliminating specific privacy concerns. The results of the shared task will be
made publicly available to enable the research community to build upon them
for the public good and peaceful purposes. Our data and ideas are strictly
intended for non-malicious, peaceful, and non-military purposes. The AraFinNLP
shared task is committed to upholding ethical standards and promoting
responsible research practices in NLP. Participants are expected to adhere to
principles of fairness, transparency, and accountability throughout the
development and evaluation of their systems. Additionally, participants are
encouraged to consider the broader societal implications of their research,
including issues related to accessibility, inclusivity, and potential impacts
on vulnerable populations. The organizers of the shared task are dedicated to
fostering an inclusive and collaborative research environment, where diverse
perspectives and ethical considerations are valued and integrated into the
development and dissemination of NLP technologies.

## Acknowledgements

This research is partially funded by the Palestinian Higher Council for
Innovation and Excellence and by the research committee at Birzeit University.
We extend our gratitude to Taymaa Hammouda for the technical support.

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