Kfir Bar


2023

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Automatic Translation of Span-Prediction Datasets
Ofri Masad | Kfir Bar | Amir Cohen
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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Temporal Relation Classification using Boolean Question Answering
Omer Cohen | Kfir Bar
Findings of the Association for Computational Linguistics: ACL 2023

Classifying temporal relations between a pair of events is crucial to natural language understanding and a well-known natural language processing task. Given a document and two event mentions, the task is aimed at finding which one started first. We propose an efficient approach for temporal relation classification (TRC) using a boolean question answering (QA) model which we fine-tune on questions that we carefully design based on the TRC annotation guidelines, thereby mimicking the way human annotators approach the task. Our new QA-based TRC model outperforms previous state-of-the-art results by 2.4%.

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Temporal Relation Classification in Hebrew
Guy Yanko | Shahaf Pariente | Kfir Bar
Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)

2022

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Can Yes-No Question-Answering Models be Useful for Few-Shot Metaphor Detection?
Lena Dankin | Kfir Bar | Nachum Dershowitz
Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)

Metaphor detection has been a challenging task in the NLP domain both before and after the emergence of transformer-based language models. The difficulty lies in subtle semantic nuances that are required to detect metaphor and in the scarcity of labeled data. We explore few-shot setups for metaphor detection, and also introduce new question answering data that can enhance classifiers that are trained on a small amount of data. We formulate the classification task as a question-answering one, and train a question-answering model. We perform extensive experiments for few shot on several architectures and report the results of several strong baselines. Thus, the answer to the question posed in the title is a definite “Yes!”

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Using Cross-Lingual Part of Speech Tagging for Partially Reconstructing the Classic Language Family Tree Model
Anat Samohi | Daniel Weisberg Mitelman | Kfir Bar
Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change

The tree model is well known for expressing the historic evolution of languages. This model has been considered as a method of describing genetic relationships between languages. Nevertheless, some researchers question the model’s ability to predict the proximity between two languages, since it represents genetic relatedness rather than linguistic resemblance. Defining other language proximity models has been an active research area for many years. In this paper we explore a part-of-speech model for defining proximity between languages using a multilingual language model that was fine-tuned on the task of cross-lingual part-of-speech tagging. We train the model on one language and evaluate it on another; the measured performance is then used to define the proximity between the two languages. By further developing the model, we show that it can reconstruct some parts of the tree model.

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How Much Does Lookahead Matter for Disambiguation? Partial Arabic Diacritization Case Study
Saeed Esmail | Kfir Bar | Nachum Dershowitz
Computational Linguistics, Volume 48, Issue 4 - December 2022

We suggest a model for partial diacritization of deep orthographies. We focus on Arabic, where the optional indication of selected vowels by means of diacritics can resolve ambiguity and improve readability. Our partial diacritizer restores short vowels only when they contribute to the ease of understandability during reading a given running text. The idea is to identify those uncertainties of absent vowels that require the reader to look ahead to disambiguate. To achieve this, two independent neural networks are used for predicting diacritics, one that takes the entire sentence as input and another that considers only the text that has been read thus far. Partial diacritization is then determined by retaining precisely those vowels on which the two networks disagree, preferring the reading based on consideration of the whole sentence over the more naïve reading-order diacritization. For evaluation, we prepared a new dataset of Arabic texts with both full and partial vowelization. In addition to facilitating readability, we find that our partial diacritizer improves translation quality compared either to their total absence or to random selection. Lastly, we study the benefit of knowing the text that follows the word in focus toward the restoration of short vowels during reading, and we measure the degree to which lookahead contributes to resolving ambiguities encountered while reading. L’Herbelot had asserted, that the most ancient Korans, written in the Cufic character, had no vowel points; and that these were first invented by Jahia–ben Jamer, who died in the 127th year of the Hegira. “Toderini’s History of Turkish Literature,” Analytical Review (1789)

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Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis
Yaara Shriki | Ido Ziv | Nachum Dershowitz | Eiran Harel | Kfir Bar
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

Natural language processing tools have been shown to be effective for detecting symptoms of schizophrenia in transcribed speech. We analyze and assess the contribution of the various syntactic and morphological categories towards successful machine classification of texts produced by subjects with schizophrenia and by others. Specifically, we fine-tune a language model for the classification task, and mask all words that are attributed with each category of interest. The speech samples were generated in a controlled way by interviewing inpatients who were officially diagnosed with schizophrenia, and a corresponding group of healthy controls. All participants are native Hebrew speakers. Our results show that nouns are the most significant category for classification performance.

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Love Me, Love Me Not: Human-Directed Sentiment Analysis in Arabic
Abraham Israeli | Aviv Naaman | Yotam Nahum | Razan Assi | Shai Fine | Kfir Bar
Proceedings of the Third International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2022) co-located with ICNLSP 2022

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Improving Few-Shot Domain Transfer for Named Entity Disambiguation with Pattern Exploitation
Philip Blair | Kfir Bar
Findings of the Association for Computational Linguistics: EMNLP 2022

Named entity disambiguation (NED) is a critical subtask of entity linking, which seeks to connect knowledge base entities with textual mentions of those entities. Naturally, the performance of a model depends on the domain it was trained on; thus, reducing the amount of data required to train models is advantageous. In this work, we leverage recent research on pattern exploitation for NED and explore whether it can reduce the amount of data required for domain adaptation by reformulating the disambiguation task as a masked language modeling problem. Using ADAPET (Tam et al., 2021), which implements a new approach for few-shot learning using fine-tuned transformer-based language models, we produce an NED model which yields, without any sacrifice of in-domain accuracy, a 7% improvement in zero-shot cross-domain performance as evaluated on NEDMed, a new NED dataset of mental health news which we release with this work.

2021

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Supporting Undotted Arabic with Pre-trained Language Models
Aviad Rom | Kfir Bar
Proceedings of the 4th International Conference on Natural Language and Speech Processing (ICNLSP 2021)

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The IDC System for Sentiment Classification and Sarcasm Detection in Arabic
Abraham Israeli | Yotam Nahum | Shai Fine | Kfir Bar
Proceedings of the Sixth Arabic Natural Language Processing Workshop

Sentiment classification and sarcasm detection attract a lot of attention by the NLP research community. However, solving these two problems in Arabic and on the basis of social network data (i.e., Twitter) is still of lower interest. In this paper we present designated solutions for sentiment classification and sarcasm detection tasks that were introduced as part of a shared task by Abu Farha et al. (2021). We adjust the existing state-of-the-art transformer pretrained models for our needs. In addition, we use a variety of machine-learning techniques such as down-sampling, augmentation, bagging, and usage of meta-features to improve the models performance. We achieve an F1-score of 0.75 over the sentiment classification problem where the F1-score is calculated over the positive and negative classes (the neutral class is not taken into account). We achieve an F1-score of 0.66 over the sarcasm detection problem where the F1-score is calculated over the sarcastic class only. In both cases, the above reported results are evaluated over the ArSarcasm-v2–an extended dataset of the ArSarcasm (Farha and Magdy, 2020) that was introduced as part of the shared task. This reflects an improvement to the state-of-the-art results in both tasks.

2020

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Transliteration of Judeo-Arabic Texts into Arabic Script Using Recurrent Neural Networks
Ori Terner | Kfir Bar | Nachum Dershowitz
Proceedings of the Fifth Arabic Natural Language Processing Workshop

We trained a model to automatically transliterate Judeo-Arabic texts into Arabic script, enabling Arabic readers to access those writings. We employ a recurrent neural network (RNN), combined with the connectionist temporal classification (CTC) loss to deal with unequal input/output lengths. This obligates adjustments in the training data to avoid input sequences that are shorter than their corresponding outputs. We also utilize a pretraining stage with a different loss function to improve network converge. Since only a single source of parallel text was available for training, we take advantage of the possibility of generating data synthetically. We train a model that has the capability to memorize words in the output language, and that also utilizes context for distinguishing ambiguities in the transliteration. We obtain an improvement over the baseline 9.5% character error, achieving 2% error with our best configuration. To measure the contribution of context to learning, we also tested word-shuffled data, for which the error rises to 2.5%.

2019

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Semantic Characteristics of Schizophrenic Speech
Kfir Bar | Vered Zilberstein | Ido Ziv | Heli Baram | Nachum Dershowitz | Samuel Itzikowitz | Eiran Vadim Harel
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

Natural language processing tools are used to automatically detect disturbances in transcribed speech of schizophrenia inpatients who speak Hebrew. We measure topic mutation over time and show that controls maintain more cohesive speech than inpatients. We also examine differences in how inpatients and controls use adjectives and adverbs to describe content words and show that the ones used by controls are more common than the those of inpatients. We provide experimental results and show their potential for automatically detecting schizophrenia in patients by means only of their speech patterns.

2016

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SLS at SemEval-2016 Task 3: Neural-based Approaches for Ranking in Community Question Answering
Mitra Mohtarami | Yonatan Belinkov | Wei-Ning Hsu | Yu Zhang | Tao Lei | Kfir Bar | Scott Cyphers | Jim Glass
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2014

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The Tel Aviv University System for the Code-Switching Workshop Shared Task
Kfir Bar | Nachum Dershowitz
Proceedings of the First Workshop on Computational Approaches to Code Switching

2012

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Building an Arabic Multiword Expressions Repository
Abdelati Hawwari | Kfir Bar | Mona Diab
Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages

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Deriving Paraphrases for Highly Inflected Languages from Comparable Documents
Kfir Bar | Nachum Dershowitz
Proceedings of COLING 2012

2010

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Using Synonyms for Arabic-to-English Example-Based Translation
Kfir Bar | Nachum Dershowitz
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Student Research Workshop

An implementation of a non-structural Example-Based Machine Translation system that translates sentences from Arabic to English, using a parallel corpus aligned at the sentence level, is described. Source-language synonyms were derived automatically and used to help locate potential translation examples for fragments of a given input sentence. The smaller the parallel corpus, the greater the contribution provided by synonyms. Considering the degree of relevance of the subject matter of a potential match contributes to the quality of the final results.

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Tel Aviv University’s system description for IWSLT 2010
Kfir Bar | Nachum Dershowitz
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign