Salam Khalifa

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18ranked-venue papers

5as first author

7since 2021 · last 2024

0000-0003-0049-3637ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 18 · 5 first-author · 7 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence

4 papers

Information extraction and text analysis · 62% Knowledge representation and reasoning · 12% Learning theory · 12%

Topics — the 7 heaviest of 9, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Natural language and speech › Information extraction and text analysis › computational morphology morphological inflection	1.3	2	2023	Exploring Linguistic Probes for Morphological Inflection · EMNLP 2023 Morphological Inflection: A Reality Check · ACL (1) 2023
Machine learning › Learning theory generalization	0.7	1	2023	Morphological Inflection: A Reality Check · ACL (1) 2023
Natural language and speech › Information extraction and text analysis linguistic probing	0.7	1	2023	Exploring Linguistic Probes for Morphological Inflection · EMNLP 2023
Natural language and speech › Language models and text generation › linguistic generalization morphological generalization	0.7	1	2023	Exploring Linguistic Probes for Morphological Inflection · EMNLP 2023
Natural language and speech › Information extraction and text analysis arabic diacritization	0.2	1	2015	Improving Arabic Diacritization through Syntactic Analysis · EMNLP 2015
Natural language and speech › Information extraction and text analysis morphological analysis	0.2	1	2015	Improving Arabic Diacritization through Syntactic Analysis · EMNLP 2015
Natural language and speech › Information extraction and text analysis syntactic parsing	0.2	1	2015	Improving Arabic Diacritization through Syntactic Analysis · EMNLP 2015

Methods — techniques the papers use, named apart from their topics

supervised learning · 0.7language-independent data splitting · 0.7evaluation strategy · 0.7data sampling · 0.7morphological tagging · 0.2

Year	Publication	Venue	Position
2024	Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic abstract We present Camel Morph MSA, the largest open-source Modern Standard Arabic morphological analyzer and generator. Camel Morph MSA has over 100K lemmas, and includes rarely modeled morphological features of Modern Standard Arabic with Classical Arabic origins. Camel Morph MSA can produce ∼1.45B analyses and ∼535M unique diacritizations, almost an order of magnitude larger than SAMA (Maamouri et al., 2010c), in addition to having ∼36% less OOV rate than SAMA on a 10B word corpus. Furthermore, Camel Morph MSA fills the gaps of many lemma paradigms by modeling linguistic phenomena consistently. Camel Morph MSA seamlessly integrates with the Camel Tools Python toolkit (Obeid et al., 2020), ensuring ease of use and accessibility. Christian Khairallah, Salam Khalifa, Reham Marzouk, Mayar Nassar, Nizar Habash	LREC/COLING	2
2023	A Cautious Generalization Goes a Long Way: Learning Morphophonological Rules abstract Explicit linguistic knowledge, encoded by resources such as rule-based morphological analyzers, continues to prove useful in downstream NLP tasks, especially for low-resource languages and dialects.Rules are an important asset in descriptive linguistic grammars.However, creating such resources is usually expensive and non-trivial, especially for spoken varieties with no written standard.In this work, we present a novel approach for automatically learning morphophonological rules of Arabic from a corpus.Motivated by classic cognitive models for rule learning, rules are generalized cautiously.Rules that are memorized for individual items are only allowed to generalize to unseen forms if they are sufficiently reliable in the training data.The learned rules are further examined to ensure that they capture true linguistic phenomena described by domain experts.We also investigate the learnability of rules in low-resource settings across different experimental setups and dialects Salam Khalifa, Sarah R. B. Payne, Jordan Kodner, Ellen Broselow, Owen Rambow	ACL (1)	1
2023	Morphological Inflection: A Reality Check abstract Morphological inflection is a popular task in sub-word NLP with both practical and cognitive applications.For years now, state-of-theart systems have reported high, but also highly variable, performance across data sets and languages.We investigate the causes of this high performance and high variability; we find several aspects of data set creation and evaluation which systematically inflate performance and obfuscate differences between languages.To improve generalizability and reliability of results, we propose new data sampling and evaluation strategies that better reflect likely usecases.Using these new strategies, we make new observations on the generalization abilities of current inflection systems. Jordan Kodner, Sarah R. B. Payne, Salam Khalifa, Zoey Liu	ACL (1)	3
2023	Re-Evaluating the Evaluation of Neural Morphological Inflection Models Jordan Kodner, Salam Khalifa, Sarah R. B. Payne, Zoey Liu	CogSci	2
2023	Exploring Linguistic Probes for Morphological Inflection abstract Modern work on the cross-linguistic computational modeling of morphological inflection has typically employed language-independent data splitting algorithms.In this paper, we supplement that approach with language-specific probes designed to test aspects of morphological generalization.Testing these probes on three morphologically distinct languages, English, Spanish, and Swahili, we find evidence that three leading morphological inflection systems employ distinct generalization strategies over conjugational classes and feature sets on both orthographic and phonologically transcribed inputs. Jordan Kodner, Salam Khalifa, Sarah R. B. Payne	EMNLP	2
2022	The Bahrain Corpus: A Multi-genre Corpus of Bahraini Arabic abstract In recent years, the focus on developing natural language processing (NLP) tools for Arabic has shifted from Modern Standard Arabic to various Arabic dialects. Various corpora of various sizes and representing different genres, have been created for a number of Arabic dialects. As far as Gulf Arabic is concerned, Gumar Corpus (Khalifa et al., 2016) is the largest corpus, to date, that includes data representing the dialectal Arabic of the six Gulf Cooperation Council countries (Bahrain, Kuwait, Saudi Arabia, Qatar, United Arab Emirates, and Oman), particularly in the genre of “online forum novels”. In this paper, we present the Bahrain Corpus. Our objective is to create a specialized corpus of the Bahraini Arabic dialect, which includes written texts as well as transcripts of audio files, belonging to a different genre (folktales, comedy shows, plays, cooking shows, etc.). The corpus comprises 620K words, carefully curated. We provide automatic morphological annotations of the full corpus using state-of-the-art morphosyntactic disambiguation for Gulf Arabic. We validate the quality of the annotations on a 7.6K word sample. We plan to make the annotated sample as well as the full corpus publicly available to support researchers interested in Arabic NLP. Dana Abdulrahim, Go Inoue, Latifa Shamsan, Salam Khalifa, Nizar Habash	LREC	4
2022	UniMorph 4.0: Universal Morphology abstract The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet. Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieras, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane 0002, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóga, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer C. White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo Maria Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar 0002, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Tucker Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova	LREC	3
2020	A Spelling Correction Corpus for Multiple Arabic Dialects abstract 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. Fadhl Eryani, Nizar Habash, Houda Bouamor, Salam Khalifa	LREC	4
2020	Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods abstract In this paper we present the first full morphological analysis and disambiguation system for Gulf Arabic. We use an existing state-of-the-art morphological disambiguation system to investigate the effects of different data sizes and different combinations of morphological analyzers for Modern Standard Arabic, Egyptian Arabic, and Gulf Arabic. We find that in very low settings, morphological analyzers help boost the performance of the full morphological disambiguation task. However, as the size of resources increase, the value of the morphological analyzers decreases. Salam Khalifa, Nasser Zalmout, Nizar Habash	LREC	1
2020	CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing abstract We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python. CAMeL Tools currently provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and Sentiment Analysis. In this paper, we describe the design of CAMeL Tools and the functionalities it provides. Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, Nizar Habash	LREC	3
2019	Towards Variability Resistant Dialectal Speech Evaluation Ahmed Ali 0002, Salam Khalifa, Nizar Habash	INTERSPEECH	2
2018	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	LREC	8
2018	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 B. Alkhereyf, Basma Abdulkareem, Ramy Eskander, Mohammad Salameh, Hind Saddiki	LREC	3
2018	A Morphologically Annotated Corpus of Emirati Arabic Salam Khalifa, Nizar Habash, Fadhl Eryani, Ossama Obeid, Dana Abdulrahim, Meera Al Kaabi	LREC	1
2018	MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction Ossama Obeid, Salam Khalifa, Nizar Habash, Houda Bouamor, Wajdi Zaghouani, Kemal Oflazer	LREC	2
2016	DALILA: The Dialectal Arabic Linguistic Learning Assistant Salam Khalifa, Houda Bouamor, Nizar Habash	LREC	1
2016	A Large Scale Corpus of Gulf Arabic Salam Khalifa, Nizar Habash, Dana Abdulrahim, Sara Hassan	LREC	1
2015	Improving Arabic Diacritization through Syntactic Analysis abstract We present an approach to Arabic automatic diacritization that integrates syntactic analysis with morphological tagging through improving the prediction of case and state features.Our best system increases the accuracy of word diacritization by 2.5% absolute on all words, and 5.2% absolute on nominals over a state-of-theart baseline.Similar increases are shown on the full morphological analysis choice. Anas Shahrour, Salam Khalifa, Nizar Habash	EMNLP	2