1 code implementation • EMNLP 2021 • Taelin Karidi, Yichu Zhou, Nathan Schneider, Omri Abend, Vivek Srikumar
We present a method for exploring regions around individual points in a contextualized vector space (particularly, BERT space), as a way to investigate how these regions correspond to word senses.
no code implementations • EMNLP 2021 • Or Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman, Idan Szpektor, Omri Abend
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
Abstractive Text Summarization Natural Language Inference +3
no code implementations • 28 Mar 2024 • Opher Lieber, Barak Lenz, Hofit Bata, Gal Cohen, Jhonathan Osin, Itay Dalmedigos, Erez Safahi, Shaked Meirom, Yonatan Belinkov, Shai Shalev-Shwartz, Omri Abend, Raz Alon, Tomer Asida, Amir Bergman, Roman Glozman, Michael Gokhman, Avashalom Manevich, Nir Ratner, Noam Rozen, Erez Shwartz, Mor Zusman, Yoav Shoham
We present Jamba, a new base large language model based on a novel hybrid Transformer-Mamba mixture-of-experts (MoE) architecture.
1 code implementation • 20 Nov 2023 • Shachar Don-Yehiya, Leshem Choshen, Omri Abend
Generating images with a Text-to-Image model often requires multiple trials, where human users iteratively update their prompt based on feedback, namely the output image.
1 code implementation • 20 Oct 2023 • Ofir Arviv, Dmitry Nikolaev, Taelin Karidi, Omri Abend
Despite the impressive growth of the abilities of multilingual language models, such as XLM-R and mT5, it has been shown that they still face difficulties when tackling typologically-distant languages, particularly in the low-resource setting.
2 code implementations • 13 Jul 2023 • Dor Muhlgay, Ori Ram, Inbal Magar, Yoav Levine, Nir Ratner, Yonatan Belinkov, Omri Abend, Kevin Leyton-Brown, Amnon Shashua, Yoav Shoham
FACTOR automatically transforms a factual corpus of interest into a benchmark evaluating an LM's propensity to generate true facts from the corpus vs. similar but incorrect statements.
no code implementations • 24 May 2023 • Taelin Karidi, Leshem Choshen, Gal Patel, Omri Abend
For example, nouns and verbs are among the most frequent POS tags.
no code implementations • 16 Feb 2023 • Asaf Yehudai, Arie Cattan, Omri Abend, Gabriel Stanovsky
Machine translation (MT) requires a wide range of linguistic capabilities, which current end-to-end models are expected to learn implicitly by observing aligned sentences in bilingual corpora.
no code implementations • 9 Feb 2023 • Uri Berger, Lea Frermann, Gabriel Stanovsky, Omri Abend
We study the relation between visual input and linguistic choices by training classifiers to predict the probability of expressing a property from raw images, and find evidence supporting the claim that linguistic properties are constrained by visual context across languages.
1 code implementation • 21 Dec 2022 • Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Inbal Magar, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham
We present Parallel Context Windows (PCW), a method that alleviates the context window restriction for any off-the-shelf LLM without further training.
1 code implementation • 16 Nov 2022 • Eytan Chamovitz, Omri Abend
Text Simplification (TS) is the task of converting a text into a form that is easier to read while maintaining the meaning of the original text.
1 code implementation • 10 Nov 2022 • Ella Neeman, Roee Aharoni, Or Honovich, Leshem Choshen, Idan Szpektor, Omri Abend
Question answering models commonly have access to two sources of "knowledge" during inference time: (1) parametric knowledge - the factual knowledge encoded in the model weights, and (2) contextual knowledge - external knowledge (e. g., a Wikipedia passage) given to the model to generate a grounded answer.
1 code implementation • 25 Oct 2022 • Eitan Wagner, Renana Keydar, Amit Pinchevski, Omri Abend
The task of topical segmentation is well studied, but previous work has mostly addressed it in the context of structured, well-defined segments, such as segmentation into paragraphs, chapters, or segmenting text that originated from multiple sources.
no code implementations • COLING 2022 • Asaf Yehudai, Leshem Choshen, Lior Fox, Omri Abend
Applying Reinforcement learning (RL) following maximum likelihood estimation (MLE) pre-training is a versatile method for enhancing neural machine translation (NMT) performance.
1 code implementation • 18 May 2022 • Shachar Don-Yehiya, Leshem Choshen, Omri Abend
We show that this augmentation method can improve the performance of the Quality-Estimation task as well.
1 code implementation • NAACL 2022 • Uri Berger, Gabriel Stanovsky, Omri Abend, Lea Frermann
Recent advances in self-supervised modeling of text and images open new opportunities for computational models of child language acquisition, which is believed to rely heavily on cross-modal signals.
1 code implementation • 11 May 2022 • Leshem Choshen, Ofir Shifman, Omri Abend
In Grammatical Error Correction, systems are evaluated by the number of errors they correct.
no code implementations • 1 May 2022 • Ehud Karpas, Omri Abend, Yonatan Belinkov, Barak Lenz, Opher Lieber, Nir Ratner, Yoav Shoham, Hofit Bata, Yoav Levine, Kevin Leyton-Brown, Dor Muhlgay, Noam Rozen, Erez Schwartz, Gal Shachaf, Shai Shalev-Shwartz, Amnon Shashua, Moshe Tenenholtz
Huge language models (LMs) have ushered in a new era for AI, serving as a gateway to natural-language-based knowledge tasks.
no code implementations • *SEM (NAACL) 2022 • Aviv Slobodkin, Leshem Choshen, Omri Abend
We further show an additional gain when using both semantic and syntactic structures in some language pairs.
1 code implementation • EMNLP 2021 • Ofir Arviv, Dmitry Nikolaev, Taelin Karidi, Omri Abend
We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting.
1 code implementation • 6 Oct 2021 • Gal Patel, Leshem Choshen, Omri Abend
We present a methodology that explores how sentence structure is reflected in neural representations of machine translation systems.
1 code implementation • 23 Sep 2021 • Taelin Karidi, Yichu Zhou, Nathan Schneider, Omri Abend, Vivek Srikumar
We present a method for exploring regions around individual points in a contextualized vector space (particularly, BERT space), as a way to investigate how these regions correspond to word senses.
2 code implementations • 22 Sep 2021 • Ida Szubert, Omri Abend, Nathan Schneider, Samuel Gibbon, Louis Mahon, Sharon Goldwater, Mark Steedman
We then demonstrate the utility of the compiled corpora through (1) a longitudinal corpus study of the prevalence of different syntactic and semantic phenomena in the CDS, and (2) applying an existing computational model of language acquisition to the two corpora and briefly comparing the results across languages.
1 code implementation • ACL 2022 • Leshem Choshen, Guy Hacohen, Daphna Weinshall, Omri Abend
These findings suggest that there is some mutual inductive bias that underlies these models' learning of linguistic phenomena.
no code implementations • 1 Jun 2021 • Ofek Rafaeli, Omri Abend, Leshem Choshen, Dmitry Nikolaev
In this research paper, I will elaborate on a method to evaluate machine translation models based on their performance on underlying syntactical phenomena between English and Arabic languages.
1 code implementation • 16 Apr 2021 • Or Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman, Idan Szpektor, Omri Abend
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
1 code implementation • NAACL 2021 • Aviv Slobodkin, Leshem Choshen, Omri Abend
Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks.
1 code implementation • 6 Apr 2021 • Leshem Choshen, Matanel Oren, Dmitry Nikolaev, Omri Abend
SERRANT is a system and code for automatic classification of English grammatical errors that combines SErCl and ERRANT.
1 code implementation • 29 Jan 2021 • Leshem Choshen, Omri Abend
Notwithstanding recent advances, syntactic generalization remains a challenge for text decoders.
1 code implementation • 31 Dec 2020 • Omri Abend, Nathan Schneider, Dotan Dvir, Jakob Prange, Ari Rappoport
This is the annotation manual for Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013), specifically the Foundational Layer.
no code implementations • COLING 2020 • Omri Abend, Dotan Dvir, Daniel Hershcovich, Jakob Prange, Nathan Schneider
This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew.
1 code implementation • Joint Conference on Lexical and Computational Semantics 2020 • Elior Sulem, Omri Abend, Ari Rappoport
Building on recent advances in semantic parsing and text simplification, we investigate the use of semantic splitting of the source sentence as preprocessing for machine translation.
2 code implementations • COLING 2020 • Daniel Hershcovich, Nathan Schneider, Dotan Dvir, Jakob Prange, Miryam de Lhoneux, Omri Abend
Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other.
no code implementations • CONLL 2020 • Stephan Oepen, Omri Abend, Lasha Abzianidze, Johan Bos, Jan Hajic, Daniel Hershcovich, Bin Li, Tim O{'}Gorman, Nianwen Xue, Daniel Zeman
Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework.
1 code implementation • CONLL 2020 • Leshem Choshen, Dmitry Nikolaev, Yevgeni Berzak, Omri Abend
We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence.
1 code implementation • ICLR 2021 • Yoav Levine, Barak Lenz, Opher Lieber, Omri Abend, Kevin Leyton-Brown, Moshe Tennenholtz, Yoav Shoham
Specifically, we show experimentally that PMI-Masking reaches the performance of prior masking approaches in half the training time, and consistently improves performance at the end of training.
1 code implementation • ACL 2020 • Or Honovich, Lucas Torroba Hennigen, Omri Abend, Shay B. Cohen
Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities.
1 code implementation • ACL 2020 • Dmitry Nikolaev, Ofir Arviv, Taelin Karidi, Neta Kenneth, Veronika Mitnik, Lilja Maria Saeboe, Omri Abend
The patterns in which the syntax of different languages converges and diverges are often used to inform work on cross-lingual transfer.
no code implementations • ACL 2020 • Ronen Tamari, Chen Shani, Tom Hope, Miriam R. L. Petruck, Omri Abend, Dafna Shahaf
While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization.
no code implementations • CONLL 2019 • Leshem Choshen, Omri Abend
We show that the state-of-the-art Transformer MT model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model.
no code implementations • CONLL 2019 • Stephan Oepen, Omri Abend, Jan Hajic, Daniel Hershcovich, Marco Kuhlmann, Tim O{'}Gorman, Nianwen Xue, Jayeol Chun, Milan Straka, Zdenka Uresova
The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks.
1 code implementation • CONLL 2019 • Jakob Prange, Nathan Schneider, Omri Abend
Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013) is a typologically-informed, broad-coverage semantic annotation scheme that describes coarse-grained predicate-argument structure but currently lacks semantic roles.
1 code implementation • 15 Sep 2019 • Leshem Choshen, Omri Abend
We show that the state of the art Transformer Machine Translation (MT) model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model.
1 code implementation • WS 2019 • Adi Shalev, Jena D. Hwang, Nathan Schneider, Vivek Srikumar, Omri Abend, Ari Rappoport
Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens.
no code implementations • ICLR 2020 • Leshem Choshen, Lior Fox, Zohar Aizenbud, Omri Abend
Reinforcement learning (RL) is frequently used to increase performance in text generation tasks, including machine translation (MT), notably through the use of Minimum Risk Training (MRT) and Generative Adversarial Networks (GAN).
no code implementations • WS 2019 • Jakob Prange, Nathan Schneider, Omri Abend
We propose a coreference annotation scheme as a layer on top of the Universal Conceptual Cognitive Annotation foundational layer, treating units in predicate-argument structure as a basis for entity and event mentions.
2 code implementations • NAACL 2019 • Daniel Hershcovich, Omri Abend, Ari Rappoport
Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
2 code implementations • ACL 2019 • Leshem Choshen, Dan Eldad, Daniel Hershcovich, Elior Sulem, Omri Abend
The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity.
1 code implementation • 15 Mar 2019 • Daniel Hershcovich, Omri Abend, Ari Rappoport
Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
no code implementations • SEMEVAL 2019 • Daniel Hershcovich, Zohar Aizenbud, Leshem Choshen, Elior Sulem, Ari Rappoport, Omri Abend
We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results.
1 code implementation • EMNLP 2018 • Elior Sulem, Omri Abend, Ari Rappoport
BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS).
1 code implementation • NAACL 2018 • Elior Sulem, Omri Abend, Ari Rappoport
Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects.
no code implementations • ACL 2018 • Elior Sulem, Omri Abend, Ari Rappoport
Here we present a simple and efficient splitting algorithm based on an automatic semantic parser.
Ranked #19 on Text Simplification on TurkCorpus
1 code implementation • CONLL 2018 • Daniel Hershcovich, Omri Abend, Ari Rappoport
This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task.
1 code implementation • ACL 2018 • Leshem Choshen, Omri Abend
The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality (henceforth, low coverage bias or LCB).
no code implementations • 31 May 2018 • Daniel Hershcovich, Leshem Choshen, Elior Sulem, Zohar Aizenbud, Ari Rappoport, Omri Abend
Given the success of recent semantic parsing shared tasks (on SDP and AMR), we expect the task to have a significant contribution to the advancement of UCCA parsing in particular, and semantic parsing in general.
1 code implementation • ACL 2018 • Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Jakob Prange, Austin Blodgett, Sarah R. Moeller, Aviram Stern, Adi Bitan, Omri Abend
Semantic relations are often signaled with prepositional or possessive marking--but extreme polysemy bedevils their analysis and automatic interpretation.
Ranked #4 on Natural Language Understanding on STREUSLE (Role F1 (Preps) metric)
1 code implementation • ACL 2018 • Daniel Hershcovich, Omri Abend, Ari Rappoport
The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others.
Ranked #3 on UCCA Parsing on SemEval 2019 Task 1
1 code implementation • ACL 2018 • Leshem Choshen, Omri Abend
Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings.
1 code implementation • 30 Apr 2018 • Leshem Choshen, Omri Abend
The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality ({\it low coverage bias} or LCB).
1 code implementation • NAACL 2018 • Leshem Choshen, Omri Abend
We propose USim, a semantic measure for Grammatical Error Correction (GEC) that measures the semantic faithfulness of the output to the source, thereby complementing existing reference-less measures (RLMs) for measuring the output's grammaticality.
no code implementations • ACL 2017 • Omri Abend, Ari Rappoport
Semantic representation is receiving growing attention in NLP in the past few years, and many proposals for semantic schemes (e. g., AMR, UCCA, GMB, UDS) have been put forth.
4 code implementations • 7 Apr 2017 • Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Archna Bhatia, Na-Rae Han, Tim O'Gorman, Sarah R. Moeller, Omri Abend, Adi Shalev, Austin Blodgett, Jakob Prange
This document offers a detailed linguistic description of SNACS (Semantic Network of Adposition and Case Supersenses; Schneider et al., 2018), an inventory of 52 semantic labels ("supersenses") that characterize the use of adpositions and case markers at a somewhat coarse level of granularity, as demonstrated in the STREUSLE corpus (https://github. com/nert-nlp/streusle/ ; version 4. 5 tracks guidelines version 2. 6).
1 code implementation • ACL 2017 • Daniel Hershcovich, Omri Abend, Ari Rappoport
We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation.
Ranked #4 on UCCA Parsing on SemEval 2019 Task 1
1 code implementation • EMNLP 2016 • Alexandra Birch, Omri Abend, Ondrej Bojar, Barry Haddow
Human evaluation of machine translation normally uses sentence-level measures such as relative ranking or adequacy scales.