Search Results for author: Omri Abend

Found 73 papers, 46 papers with code

Putting Words in BERT’s Mouth: Navigating Contextualized Vector Spaces with Pseudowords

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.

Sentence

Q^{2}: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

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

Human Learning by Model Feedback: The Dynamics of Iterative Prompting with Midjourney

1 code implementation20 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.

Improving Cross-Lingual Transfer through Subtree-Aware Word Reordering

1 code implementation20 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.

Cross-Lingual Transfer POS +1

Generating Benchmarks for Factuality Evaluation of Language Models

2 code implementations13 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.

Language Modelling Retrieval

Evaluating and Improving the Coreference Capabilities of Machine Translation Models

no code implementations16 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.

coreference-resolution Machine Translation +1

A Large-Scale Multilingual Study of Visual Constraints on Linguistic Selection of Descriptions

no code implementations9 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.

Text Generation

Parallel Context Windows for Large Language Models

1 code implementation21 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.

In-Context Learning Playing the Game of 2048 +2

Cognitive Simplification Operations Improve Text Simplification

1 code implementation16 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.

Inductive Bias Text Simplification

DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering

1 code implementation10 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.

counterfactual Data Augmentation +2

Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies

1 code implementation25 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.

Segmentation Test

Reinforcement Learning with Large Action Spaces for Neural Machine Translation

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.

Machine Translation NMT +5

PreQuEL: Quality Estimation of Machine Translation Outputs in Advance

1 code implementation18 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.

Data Augmentation Machine Translation +2

A Computational Acquisition Model for Multimodal Word Categorization

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.

Language Acquisition Object Recognition

Some Grammatical Errors are Frequent, Others are Important

1 code implementation11 May 2022 Leshem Choshen, Ofir Shifman, Omri Abend

In Grammatical Error Correction, systems are evaluated by the number of errors they correct.

Grammatical Error Correction

On the Relation between Syntactic Divergence and Zero-Shot Performance

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.

Cross-lingual zero-shot dependency parsing Relation +1

On Neurons Invariant to Sentence Structural Changes in Neural Machine Translation

1 code implementation6 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.

Machine Translation Sentence +1

Putting Words in BERT's Mouth: Navigating Contextualized Vector Spaces with Pseudowords

1 code implementation23 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.

Sentence

The Grammar-Learning Trajectories of Neural Language Models

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.

Inductive Bias

Part of Speech and Universal Dependency effects on English Arabic Machine Translation

no code implementations1 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.

BIG-bench Machine Learning Machine Translation +1

$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

1 code implementation16 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.

Abstractive Text Summarization Dialogue Evaluation +4

Mediators in Determining what Processing BERT Performs First

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.

SERRANT: a syntactic classifier for English Grammatical Error Types

1 code implementation6 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.

General Classification

Enhancing the Transformer Decoder with Transition-based Syntax

1 code implementation29 Jan 2021 Leshem Choshen, Omri Abend

Notwithstanding recent advances, syntactic generalization remains a challenge for text decoders.

Machine Translation Test +2

UCCA's Foundational Layer: Annotation Guidelines v2.1

1 code implementation31 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.

Cross-lingual Semantic Representation for NLP with UCCA

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.

Philosophy UCCA Parsing

Semantic Structural Decomposition for Neural Machine Translation

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.

Machine Translation Semantic Parsing +3

Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics

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.

Natural Language Understanding Sentence

MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing

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.

Sentence

Classifying Syntactic Errors in Learner Language

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.

Classification General Classification +2

PMI-Masking: Principled masking of correlated spans

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.

Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences

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.

Cross-Lingual Transfer

Language (Re)modelling: Towards Embodied Language Understanding

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.

Natural Language Understanding Position

Automatically Extracting Challenge Sets for Non-Local Phenomena in Neural Machine Translation

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.

Machine Translation Translation

MRP 2019: Cross-Framework Meaning Representation Parsing

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.

Sentence

Made for Each Other: Broad-coverage Semantic Structures Meet Preposition Supersenses

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.

Automatically Extracting Challenge Sets for Non local Phenomena in Neural Machine Translation

1 code implementation15 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.

Machine Translation Translation

Preparing SNACS for Subjects and Objects

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.

On the Weaknesses of Reinforcement Learning for Neural Machine Translation

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).

Machine Translation reinforcement-learning +3

Semantically Constrained Multilayer Annotation: The Case of Coreference

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.

Content Differences in Syntactic and Semantic Representation

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.

Test UCCA Parsing

The Language of Legal and Illegal Activity on the Darknet

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.

POS Test

Content Differences in Syntactic and Semantic Representations

1 code implementation15 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.

Test UCCA Parsing

SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA

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.

UCCA Parsing

BLEU is Not Suitable for the Evaluation of Text Simplification

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).

Sentence Text Generation +1

Semantic Structural Evaluation for Text Simplification

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.

Semantic Parsing Sentence +2

Inherent Biases in Reference-based Evaluation for Grammatical Error Correction

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).

Grammatical Error Correction Sentence +3

SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation

no code implementations31 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.

UCCA Parsing

Comprehensive Supersense Disambiguation of English Prepositions and Possessives

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)

Natural Language Understanding

Multitask Parsing Across Semantic Representations

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.

Test UCCA Parsing

Automatic Metric Validation for Grammatical Error Correction

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.

Grammatical Error Correction

Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification

1 code implementation30 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).

Grammatical Error Correction Sentence +3

Reference-less Measure of Faithfulness for Grammatical Error Correction

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.

Grammatical Error Correction valid

The State of the Art in Semantic Representation

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.

Adposition and Case Supersenses v2.6: Guidelines for English

4 code implementations7 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).

A Transition-Based Directed Acyclic Graph Parser for UCCA

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.

UCCA Parsing

HUME: Human UCCA-Based Evaluation of Machine Translation

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.

Machine Translation Sentence +1

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