Search Results for author: Gabriel Stanovsky

Found 43 papers, 22 papers with code

A Computational Acquisition Model for Multimodal Word Categorization

1 code implementation12 May 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

A Balanced Data Approach for Evaluating Cross-Lingual Transfer: Mapping the Linguistic Blood Bank

1 code implementation9 May 2022 Dan Malkin, Tomasz Limisiewicz, Gabriel Stanovsky

We show that the choice of pretraining languages affects downstream cross-lingual transfer for BERT-based models.

Cross-Lingual Transfer

On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations

no code implementations27 Apr 2022 Roy Schwartz, Gabriel Stanovsky

Recent work has shown that deep learning models in NLP are highly sensitive to low-level correlations between simple features and specific output labels, leading to overfitting and lack of generalization.

Common Sense Reasoning

Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach

1 code implementation EMNLP 2021 Koren Lazar, Benny Saret, Asaf Yehudai, Wayne Horowitz, Nathan Wasserman, Gabriel Stanovsky

We present models which complete missing text given transliterations of ancient Mesopotamian documents, originally written on cuneiform clay tablets (2500 BCE - 100 CE).

Language Modelling

Data Efficient Masked Language Modeling for Vision and Language

1 code implementation Findings (EMNLP) 2021 Yonatan Bitton, Gabriel Stanovsky, Michael Elhadad, Roy Schwartz

We investigate a range of alternative masking strategies specific to the cross-modal setting that address these shortcomings, aiming for better fusion of text and image in the learned representation.

Language Modelling Masked Language Modeling

Realistic Evaluation Principles for Cross-document Coreference Resolution

1 code implementation Joint Conference on Lexical and Computational Semantics 2021 Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, Ido Dagan

We point out that common evaluation practices for cross-document coreference resolution have been unrealistically permissive in their assumed settings, yielding inflated results.

Cross Document Coreference Resolution

Cross-document Coreference Resolution over Predicted Mentions

1 code implementation Findings (ACL) 2021 Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, Ido Dagan

Here, we introduce the first end-to-end model for CD coreference resolution from raw text, which extends the prominent model for within-document coreference to the CD setting.

Cross Document Coreference Resolution

Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA

2 code implementations NAACL 2021 Yonatan Bitton, Gabriel Stanovsky, Roy Schwartz, Michael Elhadad

Recent works have shown that supervised models often exploit data artifacts to achieve good test scores while their performance severely degrades on samples outside their training distribution.

Question Answering Relational Reasoning +1

Process-Level Representation of Scientific Protocols with Interactive Annotation

2 code implementations EACL 2021 Ronen Tamari, Fan Bai, Alan Ritter, Gabriel Stanovsky

We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, long-range coreference, grounding, and implicit arguments.

Relation Extraction

GENIE: A Leaderboard for Human-in-the-Loop Evaluation of Text Generation

no code implementations17 Jan 2021 Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel S. Weld

Leaderboards have eased model development for many NLP datasets by standardizing their evaluation and delegating it to an independent external repository.

Machine Translation Reading Comprehension +2

Gender Coreference and Bias Evaluation at WMT 2020

1 code implementation WMT (EMNLP) 2020 Tom Kocmi, Tomasz Limisiewicz, Gabriel Stanovsky

Our work presents the largest evidence for the phenomenon in more than 19 systems submitted to the WMT over four diverse target languages: Czech, German, Polish, and Russian.

Machine Translation Translation

MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics

1 code implementation EMNLP 2020 Anthony Chen, Gabriel Stanovsky, Sameer Singh, Matt Gardner

Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers.

14 Question Answering +1

Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling

2 code implementations23 Sep 2020 Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, Ido Dagan

Recent evaluation protocols for Cross-document (CD) coreference resolution have often been inconsistent or lenient, leading to incomparable results across works and overestimation of performance.

Cross Document Coreference Resolution Entity Cross-Document Coreference Resolution +1

Active Learning for Coreference Resolution using Discrete Annotation

1 code implementation ACL 2020 Belinda Z. Li, Gabriel Stanovsky, Luke Zettlemoyer

We improve upon pairwise annotation for active learning in coreference resolution, by asking annotators to identify mention antecedents if a presented mention pair is deemed not coreferent.

Active Learning Coreference Resolution

The Right Tool for the Job: Matching Model and Instance Complexities

1 code implementation ACL 2020 Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, Jesse Dodge, Noah A. Smith

Our method presents a favorable speed/accuracy tradeoff in almost all cases, producing models which are up to five times faster than the state of the art, while preserving their accuracy.

Natural Language Inference Text Classification

Ecological Semantics: Programming Environments for Situated Language Understanding

no code implementations10 Mar 2020 Ronen Tamari, Gabriel Stanovsky, Dafna Shahaf, Reut Tsarfaty

Large-scale natural language understanding (NLU) systems have made impressive progress: they can be applied flexibly across a variety of tasks, and employ minimal structural assumptions.

Common Sense Reasoning Grounded language learning +1

Controlled Crowdsourcing for High-Quality QA-SRL Annotation

1 code implementation ACL 2020 Paul Roit, Ayal Klein, Daniela Stepanov, Jonathan Mamou, Julian Michael, Gabriel Stanovsky, Luke Zettlemoyer, Ido Dagan

Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen.

Semantic Role Labeling

Evaluating Question Answering Evaluation

no code implementations WS 2019 Anthony Chen, Gabriel Stanovsky, Sameer Singh, Matt Gardner

Our study suggests that while current metrics may be suitable for existing QA datasets, they limit the complexity of QA datasets that can be created.

Answer Generation Multiple-choice +1

Y'all should read this! Identifying Plurality in Second-Person Personal Pronouns in English Texts

no code implementations WS 2019 Gabriel Stanovsky, Ronen Tamari

Distinguishing between singular and plural {``}you{''} in English is a challenging task which has potential for downstream applications, such as machine translation or coreference resolution.

Coreference Resolution Machine Translation +1

Yall should read this! Identifying Plurality in Second-Person Personal Pronouns in English Texts

1 code implementation26 Oct 2019 Gabriel Stanovsky, Ronen Tamari

Distinguishing between singular and plural "you" in English is a challenging task which has potential for downstream applications, such as machine translation or coreference resolution.

Coreference Resolution Machine Translation +1

Evaluating Gender Bias in Machine Translation

1 code implementation ACL 2019 Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer

We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT).

Coreference Resolution Machine Translation +2

Semantics as a Foreign Language

no code implementations EMNLP 2018 Gabriel Stanovsky, Ido Dagan

We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect.

Dependency Parsing Machine Translation +3

Crowdsourcing Question-Answer Meaning Representations

1 code implementation NAACL 2018 Julian Michael, Gabriel Stanovsky, Luheng He, Ido Dagan, Luke Zettlemoyer

We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs.

Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets

no code implementations ACL 2017 Gabriel Stanovsky, Judith Eckle-Kohler, Yevgeniy Puzikov, Ido Dagan, Iryna Gurevych

Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results.

Knowledge Base Population Question Answering

A Consolidated Open Knowledge Representation for Multiple Texts

1 code implementation WS 2017 Rachel Wities, Vered Shwartz, Gabriel Stanovsky, Meni Adler, Ori Shapira, Shyam Upadhyay, Dan Roth, Eugenio Martinez Camara, Iryna Gurevych, Ido Dagan

We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner.

Lexical Entailment Open Information Extraction

Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models

no code implementations EACL 2017 Gabriel Stanovsky, Daniel Gruhl, Pablo Mendes

Recognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are context-dependent and include long, varied and unconventional descriptions as compared to more formal medical symptom terminology.

Active Learning Knowledge Graph Embeddings

Modeling Extractive Sentence Intersection via Subtree Entailment

no code implementations COLING 2016 Omer Levy, Ido Dagan, Gabriel Stanovsky, Judith Eckle-Kohler, Iryna Gurevych

Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity.

Abstractive Text Summarization Natural Language Inference +1

Getting More Out Of Syntax with PropS

no code implementations4 Mar 2016 Gabriel Stanovsky, Jessica Ficler, Ido Dagan, Yoav Goldberg

Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences.

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