Houda Bouamor


2023

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Exploring the Impact of Transliteration on NLP Performance: Treating Maltese as an Arabic Dialect
Kurt Micallef | Fadhl Eryani | Nizar Habash | Houda Bouamor | Claudia Borg
Proceedings of the Workshop on Computation and Written Language (CAWL 2023)

Multilingual models such as mBERT have been demonstrated to exhibit impressive crosslingual transfer for a number of languages. Despite this, the performance drops for lowerresourced languages, especially when they are not part of the pre-training setup and when there are script differences. In this work we consider Maltese, a low-resource language of Arabic and Romance origins written in Latin script. Specifically, we investigate the impact of transliterating Maltese into Arabic scipt on a number of downstream tasks: Part-of-Speech Tagging, Dependency Parsing, and Sentiment Analysis. We compare multiple transliteration pipelines ranging from deterministic character maps to more sophisticated alternatives, including manually annotated word mappings and non-deterministic character mappings. For the latter, we show that selection techniques using n-gram language models of Tunisian Arabic, the dialect with the highest degree of mutual intelligibility to Maltese, yield better results on downstream tasks. Moreover, our experiments highlight that the use of an Arabic pre-trained model paired with transliteration outperforms mBERT. Overall, our results show that transliterating Maltese can be considered an option to improve the cross-lingual transfer capabilities.

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Benchmarking Dialectal Arabic-Turkish Machine Translation
Hasan Alkheder | Houda Bouamor | Nizar Habash | Ahmet Zengin
Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track

Due to the significant influx of Syrian refugees in Turkey in recent years, the Syrian Arabic dialect has become increasingly prevalent in certain regions of Turkey. Developing a machine translation system between Turkish and Syrian Arabic would be crucial in facilitating communication between the Turkish and Syrian communities in these regions, which can have a positive impact on various domains such as politics, trade, and humanitarian aid. Such a system would also contribute positively to the growing Arab-focused tourism industry in Turkey. In this paper, we present the first research effort exploring translation between Syrian Arabic and Turkish. We use a set of 2,000 parallel sentences from the MADAR corpus containing 25 different city dialects from different cities across the Arab world, in addition to Modern Standard Arabic (MSA), English, and French. Additionally, we explore the translation performance into Turkish from other Arabic dialects and compare the results to the performance achieved when translating from Syrian Arabic. We build our MADAR-Turk data set by manually translating the set of 2,000 sentences from the Damascus dialect of Syria to Turkish with the help of two native Arabic speakers from Syria who are also highly fluent in Turkish. We evaluate the quality of the translations and report the results achieved. We make this first-of-a-kind data set publicly available to support research in machine translation between these important but less studied language pairs.

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Findings of the Association for Computational Linguistics: EMNLP 2023
Houda Bouamor | Juan Pino | Kalika Bali
Findings of the Association for Computational Linguistics: EMNLP 2023

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Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Houda Bouamor | Juan Pino | Kalika Bali
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task
Muhammad Abdul-Mageed | AbdelRahim Elmadany | Chiyu Zhang | El Moatez Billah Nagoudi | Houda Bouamor | Nizar Habash
Proceedings of ArabicNLP 2023

We describe the findings of the fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023). The objective of NADI is to help advance state-of-the-art Arabic NLP by creating opportunities for teams of researchers to collaboratively compete under standardized conditions. It does so with a focus on Arabic dialects, offering novel datasets and defining subtasks that allow for meaningful comparisons between different approaches. NADI 2023 targeted both dialect identification (Subtask1) and dialect-to-MSA machine translation (Subtask 2 and Subtask 3). A total of 58 unique teams registered for the shared task, of whom 18 teams have participated (with 76 valid submissions during test phase). Among these, 16 teams participated in Subtask 1, 5 participated in Subtask 2, and 3 participated in Subtask 3. The winning teams achieved 87.27 F1 on Subtask 1, 14.76 Bleu in Subtask 2, and 21.10 Bleu in Subtask 3, respectively. Results show that all three subtasks remain challenging, thereby motivating future work in this area. We describe the methods employed by the participating teams and briefly offer an outlook for NADI.

2022

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Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences
Haithem Afli | Mehwish Alam | Houda Bouamor | Cristina Blasi Casagran | Colleen Boland | Sahar Ghannay
Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences

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Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)
Houda Bouamor | Hend Al-Khalifa | Kareem Darwish | Owen Rambow | Fethi Bougares | Ahmed Abdelali | Nadi Tomeh | Salam Khalifa | Wajdi Zaghouani
Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)

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NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task
Muhammad Abdul-Mageed | Chiyu Zhang | AbdelRahim Elmadany | Houda Bouamor | Nizar Habash
Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)

We describe the findings of the third Nuanced Arabic Dialect Identification Shared Task (NADI 2022). NADI aims at advancing state-of-the-art Arabic NLP, including Arabic dialects. It does so by affording diverse datasets and modeling opportunities in a standardized context where meaningful comparisons between models and approaches are possible. NADI 2022 targeted both dialect identification (Subtask 1) and dialectal sentiment analysis (Subtask 2) at the country level. A total of 41 unique teams registered for the shared task, of whom 21 teams have participated (with 105 valid submissions). Among these, 19 teams participated in Subtask 1, and 10 participated in Subtask 2. The winning team achieved F1=27.06 on Subtask 1 and F1=75.16 on Subtask 2, reflecting that both subtasks remain challenging and motivating future work in this area. We describe the methods employed by the participating teams and offer an outlook for NADI.

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The Shared Task on Gender Rewriting
Bashar Alhafni | Nizar Habash | Houda Bouamor | Ossama Obeid | Sultan Alrowili | Daliyah AlZeer | Kawla Mohmad Shnqiti | Ahmed Elbakry | Muhammad ElNokrashy | Mohamed Gabr | Abderrahmane Issam | Abdelrahim Qaddoumi | Vijay Shanker | Mahmoud Zyate
Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)

In this paper, we present the results and findings of the Shared Task on Gender Rewriting, which was organized as part of the Seventh Arabic Natural Language Processing Workshop. The task of gender rewriting refers to generating alternatives of a given sentence to match different target user gender contexts (e.g., a female speaker with a male listener, a male speaker with a male listener, etc.). This requires changing the grammatical gender (masculine or feminine) of certain words referring to the users. In this task, we focus on Arabic, a gender-marking morphologically rich language. A total of five teams from four countries participated in the shared task.

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The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses
Bashar Alhafni | Nizar Habash | Houda Bouamor
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Gender bias in natural language processing (NLP) applications, particularly machine translation, has been receiving increasing attention. Much of the research on this issue has focused on mitigating gender bias in English NLP models and systems. Addressing the problem in poorly resourced, and/or morphologically rich languages has lagged behind, largely due to the lack of datasets and resources. In this paper, we introduce a new corpus for gender identification and rewriting in contexts involving one or two target users (I and/or You) – first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. The corpus has multiple parallel components: four combinations of 1st and 2nd person in feminine and masculine grammatical genders, as well as English, and English to Arabic machine translation output. This corpus expands on Habash et al. (2019)’s Arabic Parallel Gender Corpus (APGC v1.0) by adding second person targets as well as increasing the total number of sentences over 6.5 times, reaching over 590K words. Our new dataset will aid the research and development of gender identification, controlled text generation, and post-editing rewrite systems that could be used to personalize NLP applications and provide users with the correct outputs based on their grammatical gender preferences. We make the Arabic Parallel Gender Corpus (APGC v2.0) publicly available

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Hierarchical Aggregation of Dialectal Data for Arabic Dialect Identification
Nurpeiis Baimukan | Houda Bouamor | Nizar Habash
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Arabic is a collection of dialectal variants that are historically related but significantly different. These differences can be seen across regions, countries, and even cities in the same countries. Previous work on Arabic Dialect identification has focused mainly on specific dialect levels (region, country, province, or city) using level-specific resources; and different efforts used different schemas and labels. In this paper, we present the first effort aiming at defining a standard unified three-level hierarchical schema (region-country-city) for dialectal Arabic classification. We map 29 different data sets to this unified schema, and use the common mapping to facilitate aggregating these data sets. We test the value of such aggregation by building language models and using them in dialect identification. We make our label mapping code and aggregated language models publicly available.

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User-Centric Gender Rewriting
Bashar Alhafni | Nizar Habash | Houda Bouamor
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In this paper, we define the task of gender rewriting in contexts involving two users (I and/or You) – first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. We develop a multi-step system that combines the positive aspects of both rule-based and neural rewriting models. Our results successfully demonstrate the viability of this approach on a recently created corpus for Arabic gender rewriting, achieving 88.42 M2 F0.5 on a blind test set. Our proposed system improves over previous work on the first-person-only version of this task, by 3.05 absolute increase in M2 F0.5. We demonstrate a use case of our gender rewriting system by using it to post-edit the output of a commercial MT system to provide personalized outputs based on the users’ grammatical gender preferences. We make our code, data, and pretrained models publicly available.

2021

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An Exploration of Automatic Text Summarization of Financial Reports
Samir Abdaljalil | Houda Bouamor
Proceedings of the Third Workshop on Financial Technology and Natural Language Processing

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Proceedings of the Sixth Arabic Natural Language Processing Workshop
Nizar Habash | Houda Bouamor | Hazem Hajj | Walid Magdy | Wajdi Zaghouani | Fethi Bougares | Nadi Tomeh | Ibrahim Abu Farha | Samia Touileb
Proceedings of the Sixth Arabic Natural Language Processing Workshop

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The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models
Go Inoue | Bashar Alhafni | Nurpeiis Baimukan | Houda Bouamor | Nizar Habash
Proceedings of the Sixth Arabic Natural Language Processing Workshop

In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.

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NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task
Muhammad Abdul-Mageed | Chiyu Zhang | AbdelRahim Elmadany | Houda Bouamor | Nizar Habash
Proceedings of the Sixth Arabic Natural Language Processing Workshop

We present the findings and results of theSecond Nuanced Arabic Dialect IdentificationShared Task (NADI 2021). This Shared Taskincludes four subtasks: country-level ModernStandard Arabic (MSA) identification (Subtask1.1), country-level dialect identification (Subtask1.2), province-level MSA identification (Subtask2.1), and province-level sub-dialect identifica-tion (Subtask 2.2). The shared task dataset cov-ers a total of 100 provinces from 21 Arab coun-tries, collected from the Twitter domain. A totalof 53 teams from 23 countries registered to par-ticipate in the tasks, thus reflecting the interestof the community in this area. We received 16submissions for Subtask 1.1 from five teams, 27submissions for Subtask 1.2 from eight teams,12 submissions for Subtask 2.1 from four teams,and 13 Submissions for subtask 2.2 from fourteams.

2020

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Gender-Aware Reinflection using Linguistically Enhanced Neural Models
Bashar Alhafni | Nizar Habash | Houda Bouamor
Proceedings of the Second Workshop on Gender Bias in Natural Language Processing

In this paper, we present an approach for sentence-level gender reinflection using linguistically enhanced sequence-to-sequence models. Our system takes an Arabic sentence and a given target gender as input and generates a gender-reinflected sentence based on the target gender. We formulate the problem as a user-aware grammatical error correction task and build an encoder-decoder architecture to jointly model reinflection for both masculine and feminine grammatical genders. We also show that adding linguistic features to our model leads to better reinflection results. The results on a blind test set using our best system show improvements over previous work, with a 3.6% absolute increase in M2 F0.5.

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A Spelling Correction Corpus for Multiple Arabic Dialects
Fadhl Eryani | Nizar Habash | Houda Bouamor | Salam Khalifa
Proceedings of the Twelfth Language Resources and Evaluation Conference

Arabic dialects are the non-standard varieties of Arabic commonly spoken – and increasingly written on social media – across the Arab world. Arabic dialects do not have standard orthographies, a challenge for natural language processing applications. In this paper, we present the MADAR CODA Corpus, a collection of 10,000 sentences from five Arabic city dialects (Beirut, Cairo, Doha, Rabat, and Tunis) represented in the Conventional Orthography for Dialectal Arabic (CODA) in parallel with their raw original form. The sentences come from the Multi-Arabic Dialect Applications and Resources (MADAR) Project and are in parallel across the cities (2,000 sentences from each city). This publicly available resource is intended to support research on spelling correction and text normalization for Arabic dialects. We present results on a bootstrapping technique we use to speed up the CODA annotation, as well as on the degree of similarity across the dialects before and after CODA annotation.

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Proceedings of the Fifth Arabic Natural Language Processing Workshop
Imed Zitouni | Muhammad Abdul-Mageed | Houda Bouamor | Fethi Bougares | Mahmoud El-Haj | Nadi Tomeh | Wajdi Zaghouani
Proceedings of the Fifth Arabic Natural Language Processing Workshop

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NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task
Muhammad Abdul-Mageed | Chiyu Zhang | Houda Bouamor | Nizar Habash
Proceedings of the Fifth Arabic Natural Language Processing Workshop

We present the results and findings of the First Nuanced Arabic Dialect Identification Shared Task (NADI). This Shared Task includes two subtasks: country-level dialect identification (Subtask 1) and province-level sub-dialect identification (Subtask 2). The data for the shared task covers a total of 100 provinces from 21 Arab countries and is collected from the Twitter domain. As such, NADI is the first shared task to target naturally-occurring fine-grained dialectal text at the sub-country level. A total of 61 teams from 25 countries registered to participate in the tasks, thus reflecting the interest of the community in this area. We received 47 submissions for Subtask 1 from 18 teams and 9 submissions for Subtask 2 from 9 teams.

2019

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Automatic Gender Identification and Reinflection in Arabic
Nizar Habash | Houda Bouamor | Christine Chung
Proceedings of the First Workshop on Gender Bias in Natural Language Processing

The impressive progress in many Natural Language Processing (NLP) applications has increased the awareness of some of the biases these NLP systems have with regards to gender identities. In this paper, we propose an approach to extend biased single-output gender-blind NLP systems with gender-specific alternative reinflections. We focus on Arabic, a gender-marking morphologically rich language, in the context of machine translation (MT) from English, and for first-person-singular constructions only. Our contributions are the development of a system-independent gender-awareness wrapper, and the building of a corpus for training and evaluating first-person-singular gender identification and reinflection in Arabic. Our results successfully demonstrate the viability of this approach with 8% relative increase in Bleu score for first-person-singular feminine, and 5.3% comparable increase for first-person-singular masculine on top of a state-of-the-art gender-blind MT system on a held-out test set.

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A Little Linguistics Goes a Long Way: Unsupervised Segmentation with Limited Language Specific Guidance
Alexander Erdmann | Salam Khalifa | Mai Oudah | Nizar Habash | Houda Bouamor
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

We present de-lexical segmentation, a linguistically motivated alternative to greedy or other unsupervised methods, requiring only minimal language specific input. Our technique involves creating a small grammar of closed-class affixes which can be written in a few hours. The grammar over generates analyses for word forms attested in a raw corpus which are disambiguated based on features of the linguistic base proposed for each form. Extending the grammar to cover orthographic, morpho-syntactic or lexical variation is simple, making it an ideal solution for challenging corpora with noisy, dialect-inconsistent, or otherwise non-standard content. In two evaluations, we consistently outperform competitive unsupervised baselines and approach the performance of state-of-the-art supervised models trained on large amounts of data, providing evidence for the value of linguistic input during preprocessing.

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The MADAR Shared Task on Arabic Fine-Grained Dialect Identification
Houda Bouamor | Sabit Hassan | Nizar Habash
Proceedings of the Fourth Arabic Natural Language Processing Workshop

In this paper, we present the results and findings of the MADAR Shared Task on Arabic Fine-Grained Dialect Identification. This shared task was organized as part of The Fourth Arabic Natural Language Processing Workshop, collocated with ACL 2019. The shared task includes two subtasks: the MADAR Travel Domain Dialect Identification subtask (Subtask 1) and the MADAR Twitter User Dialect Identification subtask (Subtask 2). This shared task is the first to target a large set of dialect labels at the city and country levels. The data for the shared task was created or collected under the Multi-Arabic Dialect Applications and Resources (MADAR) project. A total of 21 teams from 15 countries participated in the shared task.

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The FinSBD-2019 Shared Task: Sentence Boundary Detection in PDF Noisy Text in the Financial Domain
Abderrahim Ait Azzi | Houda Bouamor | Sira Ferradans
Proceedings of the First Workshop on Financial Technology and Natural Language Processing

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Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)
Mahmoud El-Haj | Paul Rayson | Steven Young | Houda Bouamor | Sira Ferradans
Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)

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ADIDA: Automatic Dialect Identification for Arabic
Ossama Obeid | Mohammad Salameh | Houda Bouamor | Nizar Habash
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

This demo paper describes ADIDA, a web-based system for automatic dialect identification for Arabic text. The system distinguishes among the dialects of 25 Arab cities (from Rabat to Muscat) in addition to Modern Standard Arabic. The results are presented with either a point map or a heat map visualizing the automatic identification probabilities over a geographical map of the Arab World.

2018

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Fine-Grained Arabic Dialect Identification
Mohammad Salameh | Houda Bouamor | Nizar Habash
Proceedings of the 27th International Conference on Computational Linguistics

Previous work on the problem of Arabic Dialect Identification typically targeted coarse-grained five dialect classes plus Standard Arabic (6-way classification). This paper presents the first results on a fine-grained dialect classification task covering 25 specific cities from across the Arab World, in addition to Standard Arabic – a very challenging task. We build several classification systems and explore a large space of features. Our results show that we can identify the exact city of a speaker at an accuracy of 67.9% for sentences with an average length of 7 words (a 9% relative error reduction over the state-of-the-art technique for Arabic dialect identification) and reach more than 90% when we consider 16 words. We also report on additional insights from a data analysis of similarity and difference across Arabic dialects.

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MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction
Ossama Obeid | Salam Khalifa | Nizar Habash | Houda Bouamor | Wajdi Zaghouani | Kemal Oflazer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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The MADAR Arabic Dialect Corpus and Lexicon
Houda Bouamor | Nizar Habash | Mohammad Salameh | Wajdi Zaghouani | Owen Rambow | Dana Abdulrahim | Ossama Obeid | Salam Khalifa | Fadhl Eryani | Alexander Erdmann | Kemal Oflazer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Unified Guidelines and Resources for Arabic Dialect Orthography
Nizar Habash | Fadhl Eryani | Salam Khalifa | Owen Rambow | Dana Abdulrahim | Alexander Erdmann | Reem Faraj | Wajdi Zaghouani | Houda Bouamor | Nasser Zalmout | Sara Hassan | Faisal Al-Shargi | Sakhar Alkhereyf | Basma Abdulkareem | Ramy Eskander | Mohammad Salameh | Hind Saddiki
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic
Alexander Erdmann | Nizar Habash | Dima Taji | Houda Bouamor
Proceedings of Machine Translation Summit XVI: Research Track

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Proceedings of the Third Arabic Natural Language Processing Workshop
Nizar Habash | Mona Diab | Kareem Darwish | Wassim El-Hajj | Hend Al-Khalifa | Houda Bouamor | Nadi Tomeh | Mahmoud El-Haj | Wajdi Zaghouani
Proceedings of the Third Arabic Natural Language Processing Workshop

2016

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Eyes Don’t Lie: Predicting Machine Translation Quality Using Eye Movement
Hassan Sajjad | Francisco Guzmán | Nadir Durrani | Ahmed Abdelali | Houda Bouamor | Irina Temnikova | Stephan Vogel
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings
Francisco Guzmán | Houda Bouamor | Ramy Baly | Nizar Habash
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Evaluation of machine translation (MT) into morphologically rich languages (MRL) has not been well studied despite posing many challenges. In this paper, we explore the use of embeddings obtained from different levels of lexical and morpho-syntactic linguistic analysis and show that they improve MT evaluation into an MRL. Specifically we report on Arabic, a language with complex and rich morphology. Our results show that using a neural-network model with different input representations produces results that clearly outperform the state-of-the-art for MT evaluation into Arabic, by almost over 75% increase in correlation with human judgments on pairwise MT evaluation quality task. More importantly, we demonstrate the usefulness of morpho-syntactic representations to model sentence similarity for MT evaluation and address complex linguistic phenomena of Arabic.

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Using Ambiguity Detection to Streamline Linguistic Annotation
Wajdi Zaghouani | Abdelati Hawwari | Sawsan Alqahtani | Houda Bouamor | Mahmoud Ghoneim | Mona Diab | Kemal Oflazer
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)

Arabic writing is typically underspecified for short vowels and other markups, referred to as diacritics. In addition to the lexical ambiguity exhibited in most languages, the lack of diacritics in written Arabic adds another layer of ambiguity which is an artifact of the orthography. In this paper, we present the details of three annotation experimental conditions designed to study the impact of automatic ambiguity detection, on annotation speed and quality in a large scale annotation project.

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DALILA: The Dialectal Arabic Linguistic Learning Assistant
Salam Khalifa | Houda Bouamor | Nizar Habash
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Dialectal Arabic (DA) poses serious challenges for Natural Language Processing (NLP). The number and sophistication of tools and datasets in DA are very limited in comparison to Modern Standard Arabic (MSA) and other languages. MSA tools do not effectively model DA which makes the direct use of MSA NLP tools for handling dialects impractical. This is particularly a challenge for the creation of tools to support learning Arabic as a living language on the web, where authentic material can be found in both MSA and DA. In this paper, we present the Dialectal Arabic Linguistic Learning Assistant (DALILA), a Chrome extension that utilizes cutting-edge Arabic dialect NLP research to assist learners and non-native speakers in understanding text written in either MSA or DA. DALILA provides dialectal word analysis and English gloss corresponding to each word.

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Building an Arabic Machine Translation Post-Edited Corpus: Guidelines and Annotation
Wajdi Zaghouani | Nizar Habash | Ossama Obeid | Behrang Mohit | Houda Bouamor | Kemal Oflazer
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present our guidelines and annotation procedure to create a human corrected machine translated post-edited corpus for the Modern Standard Arabic. Our overarching goal is to use the annotated corpus to develop automatic machine translation post-editing systems for Arabic that can be used to help accelerate the human revision process of translated texts. The creation of any manually annotated corpus usually presents many challenges. In order to address these challenges, we created comprehensive and simplified annotation guidelines which were used by a team of five annotators and one lead annotator. In order to ensure a high annotation agreement between the annotators, multiple training sessions were held and regular inter-annotator agreement measures were performed to check the annotation quality. The created corpus of manual post-edited translations of English to Arabic articles is the largest to date for this language pair.

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Guidelines and Framework for a Large Scale Arabic Diacritized Corpus
Wajdi Zaghouani | Houda Bouamor | Abdelati Hawwari | Mona Diab | Ossama Obeid | Mahmoud Ghoneim | Sawsan Alqahtani | Kemal Oflazer
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents the annotation guidelines developed as part of an effort to create a large scale manually diacritized corpus for various Arabic text genres. The target size of the annotated corpus is 2 million words. We summarize the guidelines and describe issues encountered during the training of the annotators. We also discuss the challenges posed by the complexity of the Arabic language and how they are addressed. Finally, we present the diacritization annotation procedure and detail the quality of the resulting annotations.

2015

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Correction Annotation for Non-Native Arabic Texts: Guidelines and Corpus
Wajdi Zaghouani | Nizar Habash | Houda Bouamor | Alla Rozovskaya | Behrang Mohit | Abeer Heider | Kemal Oflazer
Proceedings of the 9th Linguistic Annotation Workshop

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The Second QALB Shared Task on Automatic Text Correction for Arabic
Alla Rozovskaya | Houda Bouamor | Nizar Habash | Wajdi Zaghouani | Ossama Obeid | Behrang Mohit
Proceedings of the Second Workshop on Arabic Natural Language Processing

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A Pilot Study on Arabic Multi-Genre Corpus Diacritization
Houda Bouamor | Wajdi Zaghouani | Mona Diab | Ossama Obeid | Kemal Oflazer | Mahmoud Ghoneim | Abdelati Hawwari
Proceedings of the Second Workshop on Arabic Natural Language Processing

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QCMUQ@QALB-2015 Shared Task: Combining Character level MT and Error-tolerant Finite-State Recognition for Arabic Spelling Correction
Houda Bouamor | Hassan Sajjad | Nadir Durrani | Kemal Oflazer
Proceedings of the Second Workshop on Arabic Natural Language Processing

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UMMU@QALB-2015 Shared Task: Character and Word level SMT pipeline for Automatic Error Correction of Arabic Text
Fethi Bougares | Houda Bouamor
Proceedings of the Second Workshop on Arabic Natural Language Processing

2014

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CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning
Kamla Al-Mannai | Hanan Alshikhabobakr | Sabih Bin Wasi | Rukhsar Neyaz | Houda Bouamor | Behrang Mohit
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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CMUQ@Qatar:Using Rich Lexical Features for Sentiment Analysis on Twitter
Sabih Bin Wasi | Rukhsar Neyaz | Houda Bouamor | Behrang Mohit
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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CMUQ@QALB-2014: An SMT-based System for Automatic Arabic Error Correction
Serena Jeblee | Houda Bouamor | Wajdi Zaghouani | Kemal Oflazer
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

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Domain and Dialect Adaptation for Machine Translation into Egyptian Arabic
Serena Jeblee | Weston Feely | Houda Bouamor | Alon Lavie | Nizar Habash | Kemal Oflazer
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

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A Multidialectal Parallel Corpus of Arabic
Houda Bouamor | Nizar Habash | Kemal Oflazer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The daily spoken variety of Arabic is often termed the colloquial or dialect form of Arabic. There are many Arabic dialects across the Arab World and within other Arabic speaking communities. These dialects vary widely from region to region and to a lesser extent from city to city in each region. The dialects are not standardized, they are not taught, and they do not have official status. However they are the primary vehicles of communication (face-to-face and recently, online) and have a large presence in the arts as well. In this paper, we present the first multidialectal Arabic parallel corpus, a collection of 2,000 sentences in Standard Arabic, Egyptian, Tunisian, Jordanian, Palestinian and Syrian Arabic, in addition to English. Such parallel data does not exist naturally, which makes this corpus a very valuable resource that has many potential applications such as Arabic dialect identification and machine translation.

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YouDACC: the Youtube Dialectal Arabic Comment Corpus
Ahmed Salama | Houda Bouamor | Behrang Mohit | Kemal Oflazer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents YOUDACC, an automatically annotated large-scale multi-dialectal Arabic corpus collected from user comments on Youtube videos. Our corpus covers different groups of dialects: Egyptian (EG), Gulf (GU), Iraqi (IQ), Maghrebi (MG) and Levantine (LV). We perform an empirical analysis on the crawled corpus and demonstrate that our location-based proposed method is effective for the task of dialect labeling.

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A Human Judgement Corpus and a Metric for Arabic MT Evaluation
Houda Bouamor | Hanan Alshikhabobakr | Behrang Mohit | Kemal Oflazer
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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SuMT: A Framework of Summarization and MT
Houda Bouamor | Behrang Mohit | Kemal Oflazer
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Dudley North visits North London: Learning When to Transliterate to Arabic
Mahmoud Azab | Houda Bouamor | Behrang Mohit | Kemal Oflazer
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Generalizing Sub-sentential Paraphrase Acquisition across Original Signal Type of Text Pairs
Aurélien Max | Houda Bouamor | Anne Vilnat
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Validation sur le Web de reformulations locales: application à la Wikipédia (Assisted Rephrasing for Wikipedia Contributors through Web-based Validation) [in French]
Houda Bouamor | Aurélien Max | Gabriel Illouz | Anne Vilnat
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN

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Une étude en 3D de la paraphrase: types de corpus, langues et techniques (A Study of Paraphrase along 3 Dimensions : Corpus Types, Languages and Techniques) [in French]
Houda Bouamor | Aurélien Max | Anne Vilnat
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN

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Étude bilingue de l’acquisition et de la validation automatiques de paraphrases sous-phrastiques [Automatic acquisition and validation of sub-sentential paraphrases : a bilingual study]
Houda Bouamor | Aurélien Max | Anne Vilnat
Traitement Automatique des Langues, Volume 53, Numéro 1 : Varia [Varia]

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A contrastive review of paraphrase acquisition techniques
Houda Bouamor | Aurélien Max | Gabriel Illouz | Anne Vilnat
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper addresses the issue of what approach should be used for building a corpus of sententential paraphrases depending on one's requirements. Six strategies are studied: (1) multiple translations into a single language from another language; (2) multiple translations into a single language from different other languages; (3) multiple descriptions of short videos; (4) multiple subtitles for the same language; (5) headlines for similar news articles; and (6) sub-sentential paraphrasing in the context of a Web-based game. We report results on French for 50 paraphrase pairs collected for all these strategies, where corpora were manually aligned at the finest possible level to define oracle performance in terms of accessible sub-sentential paraphrases. The differences observed will be used as criteria for motivating the choice of a given approach before attempting to build a new paraphrase corpus.

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Validation of sub-sentential paraphrases acquired from parallel monolingual corpora
Houda Bouamor | Aurélien Max | Anne Vilnat
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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Paraphrases et modifications locales dans l’historique des révisions de Wikipédia (Paraphrases and local changes in the revision history of Wikipedia)
Camille Dutrey | Houda Bouamor | Delphine Bernhard | Aurélien Max
Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Dans cet article, nous analysons les modifications locales disponibles dans l’historique des révisions de la version française de Wikipédia. Nous définissons tout d’abord une typologie des modifications fondée sur une étude détaillée d’un large corpus de modifications. Puis, nous détaillons l’annotation manuelle d’une partie de ce corpus afin d’évaluer le degré de complexité de la tâche d’identification automatique de paraphrases dans ce genre de corpus. Enfin, nous évaluons un outil d’identification de paraphrases à base de règles sur un sous-ensemble de notre corpus.

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Combinaison d’informations pour l’alignement monolingue (Information combination for monolingual alignment)
Houda Bouamor | Aurélien Max | Anne Vilnat
Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Dans cet article, nous décrivons une nouvelle méthode d’alignement automatique de paraphrases d’énoncés. Nous utilisons des méthodes développées précédemment afin de produire différentes approches hybrides (hybridations). Ces différentes méthodes permettent d’acquérir des équivalences textuelles à partir d’un corpus monolingue parallèle. L’hybridation combine des informations obtenues par diverses techniques : alignements statistiques, approche symbolique, fusion d’arbres syntaxiques et alignement basé sur des distances d’édition. Nous avons évalué l’ensemble de ces résultats et nous constatons une amélioration sur l’acquisition de paraphrases sous-phrastiques.

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Web-based Validation for Contextual Targeted Paraphrasing
Houda Bouamor | Aurélien Max | Gabriel Illouz | Anne Vilnat
Proceedings of the Workshop on Monolingual Text-To-Text Generation

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Monolingual Alignment by Edit Rate Computation on Sentential Paraphrase Pairs
Houda Bouamor | Aurélien Max | Anne Vilnat
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Acquisition de paraphrases sous-phrastiques depuis des paraphrases d’énoncés
Houda Bouamor | Aurélien Max | Anne Vilnat
Actes de la 17e conférence sur le Traitement Automatique des Langues Naturelles. Articles courts

Dans cet article, nous présentons la tâche d’acquisition de paraphrases sous-phrastiques (impliquant des paires de mots ou de groupes de mots), et décrivons plusieurs techniques opérant à différents niveaux. Nous décrivons une évaluation visant à comparer ces techniques et leurs combinaisons sur deux corpus de paraphrases d’énoncés obtenus par traduction multiple. Les conclusions que nous tirons peuvent servir de guide pour améliorer des techniques existantes.

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Construction d’un corpus de paraphrases d’énoncés par traduction multiple multilingue
Houda Bouamor
Actes de la 17e conférence sur le Traitement Automatique des Langues Naturelles. REncontres jeunes Chercheurs en Informatique pour le Traitement Automatique des Langues

Les corpus de paraphrases à large échelle sont importants dans de nombreuses applications de TAL. Dans cet article nous présentons une méthode visant à obtenir un corpus parallèle de paraphrases d’énoncés en français. Elle vise à collecter des traductions multiples proposées par des contributeurs volontaires francophones à partir de plusieurs langues européennes. Nous formulons l’hypothèse que deux traductions soumises indépendamment par deux participants conservent généralement le sens de la phrase d’origine, quelle que soit la langue à partir de laquelle la traduction est effectuée. L’analyse des résultats nous permet de discuter cette hypothèse.

2009

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Amener des utilisateurs à créer et évaluer des paraphrases par le jeu
Houda Bouamor | Aurélien Max | Anne Vilnat
Actes de la 16ème conférence sur le Traitement Automatique des Langues Naturelles. Démonstrations

Dans cet article, nous présentons une application sur le web pour l’acquisition de paraphrases phrastiques et sous-phrastiques sous forme de jeu. L’application permet l’acquisition à la fois de paraphrases et de jugements humains multiples sur ces paraphrases, ce qui constitue des données particulièrement utiles pour les applications du TAL basées sur les phénomènes paraphrastiques.
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