1 code implementation • NAACL (CLPsych) 2021 • Natalie Shapira, Dana Atzil-Slonim, Daniel Juravski, Moran Baruch, Dana Stolowicz-Melman, Adar Paz, Tal Alfi-Yogev, Roy Azoulay, Adi Singer, Maayan Revivo, Chen Dahbash, Limor Dayan, Tamar Naim, Lidar Gez, Boaz Yanai, Adva Maman, Adam Nadaf, Elinor Sarfati, Amna Baloum, Tal Naor, Ephraim Mosenkis, Badreya Sarsour, Jany Gelfand Morgenshteyn, Yarden Elias, Liat Braun, Moria Rubin, Matan Kenigsbuch, Noa Bergwerk, Noam Yosef, Sivan Peled, Coral Avigdor, Rahav Obercyger, Rachel Mann, Tomer Alper, Inbal Beka, Ori Shapira, Yoav Goldberg
We introduce a large set of Hebrew lexicons pertaining to psychological aspects.
1 code implementation • EMNLP (BlackboxNLP) 2020 • Hila Gonen, Shauli Ravfogel, Yanai Elazar, Yoav Goldberg
Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages.
no code implementations • 6 Dec 2024 • Amir DN Cohen, Shauli Ravfogel, Shaltiel Shmidman, Yoav Goldberg
In few-shot relation classification (FSRC), models must generalize to novel relations with only a few labeled examples.
no code implementations • 29 Oct 2024 • Royi Rassin, Aviv Slobodkin, Shauli Ravfogel, Yanai Elazar, Yoav Goldberg
GRADE leverages the world knowledge embedded in large language models and visual question-answering systems to identify relevant concept-specific axes of diversity (e. g., ``shape'' and ``color'' for the concept ``cookie'').
1 code implementation • 22 Oct 2024 • Shir Ashury-Tahan, Amir David Nissan Cohen, Nadav Cohen, yoram louzoun, Yoav Goldberg
While coreference resolution is traditionally used as a component in individual document understanding, in this work we take a more global view and explore what can we learn about a domain from the set of all document-level coreference relations that are present in a large corpus.
no code implementations • 28 Aug 2024 • Uri Katz, Mosh Levy, Yoav Goldberg
The exponential growth of scientific literature necessitates advanced tools for effective knowledge exploration.
no code implementations • 31 Jul 2024 • Oscar Sainz, Iker García-Ferrero, Alon Jacovi, Jon Ander Campos, Yanai Elazar, Eneko Agirre, Yoav Goldberg, Wei-Lin Chen, Jenny Chim, Leshem Choshen, Luca D'Amico-Wong, Melissa Dell, Run-Ze Fan, Shahriar Golchin, Yucheng Li, PengFei Liu, Bhavish Pahwa, Ameya Prabhu, Suryansh Sharma, Emily Silcock, Kateryna Solonko, David Stap, Mihai Surdeanu, Yu-Min Tseng, Vishaal Udandarao, Zengzhi Wang, Ruijie Xu, Jinglin Yang
The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models.
1 code implementation • 15 Jul 2024 • Asaf Achi Mordechai, Yoav Goldberg, Reut Tsarfaty
Current Text-to-Code models demonstrate impressive capabilities in generating executable code from natural language snippets.
no code implementations • 23 Jun 2024 • Royi Rassin, Yaron Fairstein, Oren Kalinsky, Guy Kushilevitz, Nachshon Cohen, Alexander Libov, Yoav Goldberg
We show that evaluating on a dataset containing annotations for only a subset of the relevant passages might result in misleading ranking of the retrieval systems and that as more relevant texts are included in the evaluation set, the rankings converge.
no code implementations • 9 Apr 2024 • Victoria Basmov, Yoav Goldberg, Reut Tsarfaty
In particular, while some models prove virtually unaffected by knowledge conflicts in affirmative and negative contexts, when faced with more semantically involved modal and conditional environments, they often fail to separate the text from their internal knowledge.
1 code implementation • 21 Feb 2024 • Gili Lior, Yoav Goldberg, Gabriel Stanovsky
Document collections of various domains, e. g., legal, medical, or financial, often share some underlying collection-wide structure, which captures information that can aid both human users and structure-aware models.
1 code implementation • 19 Feb 2024 • Mosh Levy, Alon Jacoby, Yoav Goldberg
We show that the degradation trend appears in every version of our dataset, although at different intensities.
1 code implementation • 17 Feb 2024 • Matan Avitan, Ryan Cotterell, Yoav Goldberg, Shauli Ravfogel
Interventions targeting the representation space of language models (LMs) have emerged as an effective means to influence model behavior.
1 code implementation • 24 Oct 2023 • Mosh Levy, Shauli Ravfogel, Yoav Goldberg
Using GPT4 as the editor, we find it can successfully edit trigger shortcut in samples that fool LLMs.
1 code implementation • 22 Oct 2023 • Uri Katz, Matan Vetzler, Amir DN Cohen, Yoav Goldberg
The third, and most challenging, is the move from the recognition setup to a novel retrieval setup, where the query is a zero-shot entity type, and the expected result is all the sentences from a large, pre-indexed corpus that contain entities of these types, and their corresponding spans.
1 code implementation • 21 Oct 2023 • Amit Moryossef, Zifan Jiang, Mathias Müller, Sarah Ebling, Yoav Goldberg
We find that introducing BIO tagging is necessary to model sign boundaries.
no code implementations • 18 Sep 2023 • Itay Yair, Hillel Taub-Tabib, Yoav Goldberg
Information extraction systems often produce hundreds to thousands of strings on a specific topic.
no code implementations • 24 Jun 2023 • Aviv Weinstein, Yoav Goldberg
Deverbal nouns are nominal forms of verbs commonly used in written English texts to describe events or actions, as well as their arguments.
1 code implementation • NeurIPS 2023 • Royi Rassin, Eran Hirsch, Daniel Glickman, Shauli Ravfogel, Yoav Goldberg, Gal Chechik
This reflects an impaired mapping between linguistic binding of entities and modifiers in the prompt and visual binding of the corresponding elements in the generated image.
2 code implementations • 28 May 2023 • Amit Moryossef, Mathias Müller, Anne Göhring, Zifan Jiang, Yoav Goldberg, Sarah Ebling
Sign language translation systems are complex and require many components.
no code implementations • 26 May 2023 • Royi Rassin, Yoav Goldberg, Reut Tsarfaty
In this work we propose a conjunct resolution task that operates directly on the text and makes use of a split-and-rephrase paradigm in order to recover the missing elements in the coordination structure.
no code implementations • 24 May 2023 • Victoria Basmov, Yoav Goldberg, Reut Tsarfaty
We evaluate LLMs' language understanding capacities on simple inference tasks that most humans find trivial.
no code implementations • 24 May 2023 • Natalie Shapira, Mosh Levy, Seyed Hossein Alavi, Xuhui Zhou, Yejin Choi, Yoav Goldberg, Maarten Sap, Vered Shwartz
The escalating debate on AI's capabilities warrants developing reliable metrics to assess machine "intelligence".
no code implementations • 21 May 2023 • Shauli Ravfogel, Valentina Pyatkin, Amir DN Cohen, Avshalom Manevich, Yoav Goldberg
Identifying texts with a given semantics is central for many information seeking scenarios.
1 code implementation • 17 May 2023 • Alon Jacovi, Avi Caciularu, Omer Goldman, Yoav Goldberg
Data contamination has become prevalent and challenging with the rise of models pretrained on large automatically-crawled corpora.
1 code implementation • 4 May 2023 • Alon Jacovi, Hendrik Schuff, Heike Adel, Ngoc Thang Vu, Yoav Goldberg
Word-level saliency explanations ("heat maps over words") are often used to communicate feature-attribution in text-based models.
no code implementations • 4 May 2023 • Shauli Ravfogel, Yoav Goldberg, Jacob Goldberger
Language models generate text based on successively sampling the next word.
no code implementations • 26 Apr 2023 • Moran Baruch, Nir Drucker, Gilad Ezov, Yoav Goldberg, Eyal Kushnir, Jenny Lerner, Omri Soceanu, Itamar Zimerman
Training large-scale CNNs that during inference can be run under Homomorphic Encryption (HE) is challenging due to the need to use only polynomial operations.
no code implementations • 19 Mar 2023 • Yoav Goldberg
I demonstrate through experiments that while neural methods are indeed significantly better at d-recall, it is sometimes the case that pattern-based methods are still substantially better at e-recall.
no code implementations • 7 Mar 2023 • Amit Moryossef, Yanai Elazar, Yoav Goldberg
Piano fingering -- knowing which finger to use to play each note in a musical piece, is a hard and important skill to master when learning to play the piano.
1 code implementation • 23 Oct 2022 • Elron Bandel, Yoav Goldberg, Yanai Elazar
While fine-tuned language models perform well on many tasks, they were also shown to rely on superficial surface features such as lexical overlap.
no code implementations • 19 Oct 2022 • Royi Rassin, Shauli Ravfogel, Yoav Goldberg
We study the way DALLE-2 maps symbols (words) in the prompt to their references (entities or properties of entities in the generated image).
no code implementations • 18 Oct 2022 • Shauli Ravfogel, Yoav Goldberg, Ryan Cotterell
Methods for erasing human-interpretable concepts from neural representations that assume linearity have been found to be tractable and useful.
no code implementations • 12 Oct 2022 • Hongming Zhang, Yintong Huo, Yanai Elazar, Yangqiu Song, Yoav Goldberg, Dan Roth
We first align commonsense tasks with relevant knowledge from commonsense knowledge bases and ask humans to annotate whether the knowledge is enough or not.
1 code implementation • 7 Oct 2022 • Adi Haviv, Ido Cohen, Jacob Gidron, Roei Schuster, Yoav Goldberg, Mor Geva
In this work, we offer the first methodological framework for probing and characterizing recall of memorized sequences in transformer LMs.
1 code implementation • 9 Sep 2022 • Shon Otmazgin, Arie Cattan, Yoav Goldberg
We introduce fastcoref, a python package for fast, accurate, and easy-to-use English coreference resolution.
no code implementations • 28 Jul 2022 • Yanai Elazar, Nora Kassner, Shauli Ravfogel, Amir Feder, Abhilasha Ravichander, Marius Mosbach, Yonatan Belinkov, Hinrich Schütze, Yoav Goldberg
Our causal framework and our results demonstrate the importance of studying datasets and the benefits of causality for understanding NLP models.
no code implementations • 26 Jun 2022 • Teddy Lazebnik, Hanna Weitman, Yoav Goldberg, Gal A. Kaminka
We posit that in searching for research papers, a combination of a life-time search engine with an explicitly-provided context (project) provides a solution to the concept drift problem.
2 code implementations • 25 May 2022 • Shon Otmazgin, Arie Cattan, Yoav Goldberg
While coreference resolution typically involves various linguistic challenges, recent models are based on a single pairwise scorer for all types of pairs.
Ranked #5 on
Coreference Resolution
on OntoNotes
2 code implementations • NAACL 2022 • Aryeh Tiktinsky, Vijay Viswanathan, Danna Niezni, Dana Meron Azagury, Yosi Shamay, Hillel Taub-Tabib, Tom Hope, Yoav Goldberg
Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task.
1 code implementation • 26 Apr 2022 • Mor Geva, Avi Caciularu, Guy Dar, Paul Roit, Shoval Sadde, Micah Shlain, Bar Tamir, Yoav Goldberg
The opaque nature and unexplained behavior of transformer-based language models (LMs) have spurred a wide interest in interpreting their predictions.
1 code implementation • RepL4NLP (ACL) 2022 • Hila Gonen, Shauli Ravfogel, Yoav Goldberg
Multilingual language models were shown to allow for nontrivial transfer across scripts and languages.
1 code implementation • 28 Mar 2022 • Mor Geva, Avi Caciularu, Kevin Ro Wang, Yoav Goldberg
Transformer-based language models (LMs) are at the core of modern NLP, but their internal prediction construction process is opaque and largely not understood.
2 code implementations • 28 Jan 2022 • Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan Cotterell
Modern neural models trained on textual data rely on pre-trained representations that emerge without direct supervision.
1 code implementation • 28 Jan 2022 • Shauli Ravfogel, Francisco Vargas, Yoav Goldberg, Ryan Cotterell
One prominent approach for the identification of concepts in neural representations is searching for a linear subspace whose erasure prevents the prediction of the concept from the representations.
1 code implementation • 27 Jan 2022 • Hendrik Schuff, Alon Jacovi, Heike Adel, Yoav Goldberg, Ngoc Thang Vu
In this work, we focus on this question through a study of saliency-based explanations over textual data.
no code implementations • 27 Jan 2022 • Alon Jacovi, Jasmijn Bastings, Sebastian Gehrmann, Yoav Goldberg, Katja Filippova
We posit that folk concepts of behavior provide us with a "language" that humans understand behavior with.
no code implementations • 14 Jan 2022 • Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, Jonathan Berant
Constructing benchmarks that test the abilities of modern natural language understanding models is difficult - pre-trained language models exploit artifacts in benchmarks to achieve human parity, but still fail on adversarial examples and make errors that demonstrate a lack of common sense.
1 code implementation • ACL 2020 • Hila Gonen, Ganesh Jawahar, Djamé Seddah, Yoav Goldberg
The problem of comparing two bodies of text and searching for words that differ in their usage between them arises often in digital humanities and computational social science.
no code implementations • ACL 2022 • Matan Eyal, Shoval Sadde, Hillel Taub-Tabib, Yoav Goldberg
We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora.
1 code implementation • 24 Sep 2021 • Yanai Elazar, Victoria Basmov, Yoav Goldberg, Reut Tsarfaty
Understanding the relations between entities denoted by NPs in a text is a critical part of human-like natural language understanding.
1 code implementation • EMNLP 2021 • Valentina Pyatkin, Paul Roit, Julian Michael, Reut Tsarfaty, Yoav Goldberg, Ido Dagan
We develop a two-stage model for this task, which first produces a context-independent question prototype for each role and then revises it to be contextually appropriate for the passage.
5 code implementations • ACL 2022 • Elad Ben Zaken, Shauli Ravfogel, Yoav Goldberg
We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset of them) are being modified.
4 code implementations • 13 Jun 2021 • Gail Weiss, Yoav Goldberg, Eran Yahav
In this paper we aim to change that, proposing a computational model for the transformer-encoder in the form of a programming language.
no code implementations • ACL 2021 • Shauli Ravfogel, Hillel Taub-Tabib, Yoav Goldberg
We advocate for a search paradigm called ``extractive search'', in which a search query is enriched with capture-slots, to allow for such rapid extraction.
no code implementations • MTSummit 2021 • Amit Moryossef, Kayo Yin, Graham Neubig, Yoav Goldberg
Sign language translation (SLT) is often decomposed into video-to-gloss recognition and gloss-to-text translation, where a gloss is a sequence of transcribed spoken-language words in the order in which they are signed.
Data Augmentation
Low Resource Neural Machine Translation
+5
no code implementations • CoNLL (EMNLP) 2021 • Shauli Ravfogel, Grusha Prasad, Tal Linzen, Yoav Goldberg
We apply this method to study how BERT models of different sizes process relative clauses (RCs).
no code implementations • ACL 2021 • Kayo Yin, Amit Moryossef, Julie Hochgesang, Yoav Goldberg, Malihe Alikhani
Signed languages are the primary means of communication for many deaf and hard of hearing individuals.
no code implementations • 22 Apr 2021 • William Merrill, Yoav Goldberg, Roy Schwartz, Noah A. Smith
We study whether assertions enable a system to emulate representations preserving semantic relations like equivalence.
no code implementations • 17 Apr 2021 • Ofer Sabo, Yanai Elazar, Yoav Goldberg, Ido Dagan
We explore Few-Shot Learning (FSL) for Relation Classification (RC).
no code implementations • EMNLP 2021 • Yanai Elazar, Hongming Zhang, Yoav Goldberg, Dan Roth
To support this claim, we first show that the current evaluation method of WS is sub-optimal and propose a modification that uses twin sentences for evaluation.
Ranked #24 on
Coreference Resolution
on Winograd Schema Challenge
4 code implementations • NAACL 2021 • Eric Lehman, Sarthak Jain, Karl Pichotta, Yoav Goldberg, Byron C. Wallace
The cost of training such models (and the necessity of data access to do so) coupled with their utility motivates parameter sharing, i. e., the release of pretrained models such as ClinicalBERT.
1 code implementation • EMNLP 2021 • Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel, Yanai Elazar, Yejin Choi, Yoav Goldberg
Our method is based on projecting model representation to a latent space that captures only the features that are useful (to the model) to differentiate two potential decisions.
1 code implementation • EACL 2021 • Matan Eyal, Asaf Amrami, Hillel Taub-Tabib, Yoav Goldberg
The models also outperform models trained using NLG data augmentation techniques.
1 code implementation • 1 Feb 2021 • Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, Yoav Goldberg
In this paper we study the question: Are Pretrained Language Models (PLMs) consistent with respect to factual knowledge?
no code implementations • 18 Jan 2021 • Asaf Amrami, Yoav Goldberg
We present a simple proof for the benefit of depth in multi-layer feedforward network with rectified activation ("depth separation").
1 code implementation • COLING 2020 • Eyal Orbach, Yoav Goldberg
Recent advancements in self-attention neural network architectures have raised the bar for open-ended text generation.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Matan Ben Noach, Yoav Goldberg
Large pre-trained language models reach state-of-the-art results on many different NLP tasks when fine-tuned individually; They also come with a significant memory and computational requirements, calling for methods to reduce model sizes (green AI).
1 code implementation • EMNLP 2021 • William Merrill, Vivek Ramanujan, Yoav Goldberg, Roy Schwartz, Noah Smith
To better understand this bias, we study the tendency for transformer parameters to grow in magnitude ($\ell_2$ norm) during training, and its implications for the emergent representations within self attention layers.
1 code implementation • 16 Oct 2020 • Hila Gonen, Shauli Ravfogel, Yanai Elazar, Yoav Goldberg
Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages.
no code implementations • 15 Oct 2020 • Alon Jacovi, Ana Marasović, Tim Miller, Yoav Goldberg
We discuss a model of trust inspired by, but not identical to, sociology's interpersonal trust (i. e., trust between people).
no code implementations • EMNLP (insights) 2020 • Yanai Elazar, Victoria Basmov, Shauli Ravfogel, Yoav Goldberg, Reut Tsarfaty
In this work, we follow known methodologies of collecting labeled data for the complement coercion phenomenon.
1 code implementation • EMNLP (BlackboxNLP) 2020 • Shauli Ravfogel, Yanai Elazar, Jacob Goldberger, Yoav Goldberg
Contextualized word representations, such as ELMo and BERT, were shown to perform well on various semantic and syntactic tasks.
no code implementations • 9 Oct 2020 • Amir DN Cohen, Shachar Rosenman, Yoav Goldberg
The current supervised relation classification (RC) task uses a single embedding to represent the relation between a pair of entities.
Ranked #1 on
Relation Extraction
on SemEval-2010 Task-8
1 code implementation • EMNLP 2020 • Shachar Rosenman, Alon Jacovi, Yoav Goldberg
The process of collecting and annotating training data may introduce distribution artifacts which may limit the ability of models to learn correct generalization behavior.
1 code implementation • NeurIPS 2020 • Alon Talmor, Oyvind Tafjord, Peter Clark, Yoav Goldberg, Jonathan Berant
In this work, we provide a first demonstration that LMs can be trained to reliably perform systematic reasoning combining both implicit, pre-trained knowledge and explicit natural language statements.
no code implementations • WS 2020 • Hillel Taub-Tabib, Micah Shlain, Shoval Sadde, Dan Lahav, Matan Eyal, Yaara Cohen, Yoav Goldberg
We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword queries.
no code implementations • ACL 2020 • Micah Shlain, Hillel Taub-Tabib, Shoval Sadde, Yoav Goldberg
A demo of the wikipedia system is available at: https://allenai. github. io/spike
1 code implementation • 1 Jun 2020 • Alon Jacovi, Yoav Goldberg
We find that the requirement of model interpretations to be faithful is vague and incomplete.
no code implementations • 1 Jun 2020 • Yanai Elazar, Shauli Ravfogel, Alon Jacovi, Yoav Goldberg
In this work, we point out the inability to infer behavioral conclusions from probing results and offer an alternative method that focuses on how the information is being used, rather than on what information is encoded.
no code implementations • ACL 2020 • Avi Shmidman, Shaltiel Shmidman, Moshe Koppel, Yoav Goldberg
We present a system for automatic diacritization of Hebrew text.
1 code implementation • ACL 2020 • Aryeh Tiktinsky, Yoav Goldberg, Reut Tsarfaty
We present pyBART, an easy-to-use open-source Python library for converting English UD trees either to Enhanced UD graphs or to our representation.
1 code implementation • ACL 2020 • Guy Kushilevitz, Shaul Markovitch, Yoav Goldberg
We tackle the task of Term Set Expansion (TSE): given a small seed set of example terms from a semantic class, finding more members of that class.
no code implementations • ACL 2020 • William Merrill, Gail Weiss, Yoav Goldberg, Roy Schwartz, Noah A. Smith, Eran Yahav
While formally extending these findings to unsaturated RNNs is left to future work, we hypothesize that the practical learnable capacity of unsaturated RNNs obeys a similar hierarchy.
2 code implementations • ACL 2020 • Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, Yoav Goldberg
The ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models.
no code implementations • ACL 2020 • Alon Jacovi, Yoav Goldberg
With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems.
1 code implementation • ACL 2020 • Roee Aharoni, Yoav Goldberg
The notion of "in-domain data" in NLP is often over-simplistic and vague, as textual data varies in many nuanced linguistic aspects such as topic, style or level of formality.
4 code implementations • TACL 2020 • Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, Jonathan Berant
Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer.
2 code implementations • 31 Dec 2019 • Alon Talmor, Yanai Elazar, Yoav Goldberg, Jonathan Berant
A fundamental challenge is to understand whether the performance of a LM on a task should be attributed to the pre-trained representations or to the process of fine-tuning on the task data.
1 code implementation • CONLL 2019 • Hila Gonen, Yova Kementchedjhieva, Yoav Goldberg
Many natural languages assign grammatical gender also to inanimate nouns in the language.
1 code implementation • NeurIPS 2019 • Gail Weiss, Yoav Goldberg, Eran Yahav
We present an algorithm for extraction of a probabilistic deterministic finite automaton (PDFA) from a given black-box language model, such as a recurrent neural network (RNN).
1 code implementation • EACL 2021 • Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama
We consider the situation in which a user has collected a small set of documents on a cohesive topic, and they want to retrieve additional documents on this topic from a large collection.
no code implementations • 25 Sep 2019 • Amit Moryossef, Yanai Elazar, Yoav Goldberg
Automatic Piano Fingering is a hard task which computers can learn using data.
1 code implementation • WS 2019 • Amit Moryossef, Ido Dagan, Yoav Goldberg
We follow the step-by-step approach to neural data-to-text generation we proposed in Moryossef et al (2019), in which the generation process is divided into a text-planning stage followed by a plan-realization stage.
1 code implementation • IJCNLP 2019 • Matan Ben Noach, Yoav Goldberg
We propose a novel application of the XR framework for transfer learning between related tasks, where knowing the labels of task A provides an estimation of the label proportion of task B.
2 code implementations • IJCNLP 2019 • Mor Geva, Yoav Goldberg, Jonathan Berant
Crowdsourcing has been the prevalent paradigm for creating natural language understanding datasets in recent years.
2 code implementations • NAACL 2021 • Carlo Meloni, Shauli Ravfogel, Yoav Goldberg
Historical linguists have identified regularities in the process of historic sound change.
no code implementations • WS 2019 • Amit Moryossef, Roee Aharoni, Yoav Goldberg
When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must {``}guess{''} this missing information, often leading to incorrect translations in the given context.
1 code implementation • NAACL 2019 • Noa Yehezkel Lubin, Jacob Goldberger, Yoav Goldberg
The algorithm jointly learns the noise level in the lexicon, finds the set of noisy pairs, and learns the mapping between the spaces.
2 code implementations • 29 May 2019 • Asaf Amrami, Yoav Goldberg
Word sense induction (WSI) is the task of unsupervised clustering of word usages within a sentence to distinguish senses.
Ranked #1 on
Word Sense Induction
on SemEval 2010 WSI
1 code implementation • 26 May 2019 • Yanai Elazar, Yoav Goldberg
We provide the first computational treatment of fused-heads constructions (FH), focusing on the numeric fused-heads (NFH).
Ranked #1 on
Missing Elements
on Numeric Fused-Head (dev)
no code implementations • 20 May 2019 • Omer Katz, Yuval Olshaker, Yoav Goldberg, Eran Yahav
We address the problem of automatic decompilation, converting a program in low-level representation back to a higher-level human-readable programming language.
1 code implementation • NAACL 2019 • Amit Moryossef, Yoav Goldberg, Ido Dagan
We propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization.
Ranked #15 on
Data-to-Text Generation
on WebNLG
1 code implementation • 25 Mar 2019 • Noa Yehezkel Lubin, Jacob Goldberger, Yoav Goldberg
The algorithm jointly learns the noise level in the lexicon, finds the set of noisy pairs, and learns the mapping between the spaces.
2 code implementations • NAACL 2019 • Shauli Ravfogel, Yoav Goldberg, Tal Linzen
How do typological properties such as word order and morphological case marking affect the ability of neural sequence models to acquire the syntax of a language?
2 code implementations • NAACL 2019 • Hila Gonen, Yoav Goldberg
Word embeddings are widely used in NLP for a vast range of tasks.
no code implementations • 8 Mar 2019 • Amit Moryossef, Roee Aharoni, Yoav Goldberg
When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must "guess" this missing information, often leading to incorrect translations in the given context.
no code implementations • TACL 2019 • Yanai Elazar, Yoav Goldberg
We provide the first computational treatment of fused-heads constructions (FHs), focusing on the numeric fused-heads (NFHs).
4 code implementations • NeurIPS 2019 • Moran Baruch, Gilad Baruch, Yoav Goldberg
We show that 20% of corrupt workers are sufficient to degrade a CIFAR10 model accuracy by 50%, as well as to introduce backdoors into MNIST and CIFAR10 models without hurting their accuracy
3 code implementations • 16 Jan 2019 • Yoav Goldberg
I assess the extent to which the recently introduced BERT model captures English syntactic phenomena, using (1) naturally-occurring subject-verb agreement stimuli; (2) "coloreless green ideas" subject-verb agreement stimuli, in which content words in natural sentences are randomly replaced with words sharing the same part-of-speech and inflection; and (3) manually crafted stimuli for subject-verb agreement and reflexive anaphora phenomena.
1 code implementation • IJCNLP 2019 • Hila Gonen, Yoav Goldberg
We focus on the problem of language modeling for code-switched language, in the context of automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
2 code implementations • WS 2018 • Alon Jacovi, Oren Sar Shalom, Yoav Goldberg
We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text.
no code implementations • WS 2018 • Shauli Ravfogel, Francis M. Tyers, Yoav Goldberg
We propose the Basque agreement prediction task as challenging benchmark for models that attempt to learn regularities in human language.
1 code implementation • EMNLP 2018 • Asaf Amrami, Yoav Goldberg
An established method for Word Sense Induction (WSI) uses a language model to predict probable substitutes for target words, and induces senses by clustering these resulting substitute vectors.
1 code implementation • EMNLP 2018 • Yanai Elazar, Yoav Goldberg
Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation.
no code implementations • COLING 2018 • Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Ido Dagan, Yoav Goldberg, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class.
no code implementations • 26 Jul 2018 • Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Ido Dagan, Yoav Goldberg, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class.
1 code implementation • ACL 2018 • Roee Aharoni, Yoav Goldberg
To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8. 68 BLEU and fostering further progress on the task.
3 code implementations • ICLR 2019 • Shani Gamrian, Yoav Goldberg
Despite the remarkable success of Deep RL in learning control policies from raw pixels, the resulting models do not generalize.
1 code implementation • ACL 2018 • Gail Weiss, Yoav Goldberg, Eran Yahav
While Recurrent Neural Networks (RNNs) are famously known to be Turing complete, this relies on infinite precision in the states and unbounded computation time.
2 code implementations • ACL 2018 • Max Glockner, Vered Shwartz, Yoav Goldberg
We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge.
2 code implementations • 2 May 2018 • Roee Aharoni, Yoav Goldberg
To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8. 68 BLEU and fostering further progress on the task.
1 code implementation • ICML 2018 • Danny Karmon, Daniel Zoran, Yoav Goldberg
Most works on adversarial examples for deep-learning based image classifiers use noise that, while small, covers the entire image.
1 code implementation • ICML 2018 • Gail Weiss, Yoav Goldberg, Eran Yahav
We do this using Angluin's L* algorithm as a learner and the trained RNN as an oracle.
no code implementations • CONLL 2017 • Miryam de Lhoneux, Yan Shao, Ali Basirat, Eliyahu Kiperwasser, Sara Stymne, Yoav Goldberg, Joakim Nivre
We present the Uppsala submission to the CoNLL 2017 shared task on parsing from raw text to universal dependencies.
no code implementations • WS 2017 • Jessica Ficler, Yoav Goldberg
Most work on neural natural language generation (NNLG) focus on controlling the content of the generated text.
1 code implementation • CONLL 2017 • Emile Enguehard, Yoav Goldberg, Tal Linzen
Recent work has explored the syntactic abilities of RNNs using the subject-verb agreement task, which diagnoses sensitivity to sentence structure.
no code implementations • CL 2017 • Miguel Ballesteros, Chris Dyer, Yoav Goldberg, Noah A. Smith
During training, dynamic oracles alternate between sampling parser states from the training data and from the model as it is being learned, making the model more robust to the kinds of errors that will be made at test time.
2 code implementations • NeurIPS 2017 • Graham Neubig, Yoav Goldberg, Chris Dyer
Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offer more flexibility for implementing models that cope with data of varying dimensions and structure, relative to toolkits that operate on statically declared computations (e. g., TensorFlow, CNTK, and Theano).
no code implementations • ACL 2017 • Roee Aharoni, Yoav Goldberg
We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees.
1 code implementation • EACL 2017 • Oded Avraham, Yoav Goldberg
We explore the ability of word embeddings to capture both semantic and morphological similarity, as affected by the different types of linguistic properties (surface form, lemma, morphological tag) used to compose the representation of each word.
no code implementations • EACL 2017 • Jessica Ficler, Yoav Goldberg
While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others.
4 code implementations • 15 Jan 2017 • Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, Pengcheng Yin
In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.
no code implementations • COLING 2016 • Hila Gonen, Yoav Goldberg
Prepositions are very common and very ambiguous, and understanding their sense is critical for understanding the meaning of the sentence.
1 code implementation • WS 2016 • Oded Avraham, Yoav Goldberg
We suggest a new method for creating and using gold-standard datasets for word similarity evaluation.
1 code implementation • ACL 2017 • Roee Aharoni, Yoav Goldberg
We present a neural model for morphological inflection generation which employs a hard attention mechanism, inspired by the nearly-monotonic alignment commonly found between the characters in a word and the characters in its inflection.
5 code implementations • TACL 2016 • Tal Linzen, Emmanuel Dupoux, Yoav Goldberg
The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities.
no code implementations • EMNLP 2016 • Jessica Ficler, Yoav Goldberg
We propose a neural-network based model for coordination boundary prediction.
no code implementations • EACL 2017 • Omer Levy, Anders Søgaard, Yoav Goldberg
While cross-lingual word embeddings have been studied extensively in recent years, the qualitative differences between the different algorithms remain vague.
3 code implementations • 15 Aug 2016 • Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, Yoav Goldberg
The analysis sheds light on the relative strengths of different sentence embedding methods with respect to these low level prediction tasks, and on the effect of the encoded vector's dimensionality on the resulting representations.
1 code implementation • ACL 2016 • Jessica Ficler, Yoav Goldberg
Coordination is an important and common syntactic construction which is not handled well by state of the art parsers.
no code implementations • ACL 2016 • Jessica Ficler, Yoav Goldberg
Syntactic parsers perform poorly in prediction of Argument-Cluster Coordination (ACC).
no code implementations • LREC 2016 • Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Yoav Goldberg, Jan Haji{\v{c}}, Christopher D. Manning, Ryan Mcdonald, Slav Petrov, Sampo Pyysalo, Natalia Silveira, Reut Tsarfaty, Daniel Zeman
Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments.
3 code implementations • ACL 2016 • Barbara Plank, Anders Søgaard, Yoav Goldberg
Bidirectional long short-term memory (bi-LSTM) networks have recently proven successful for various NLP sequence modeling tasks, but little is known about their reliance to input representations, target languages, data set size, and label noise.
Ranked #4 on
Part-Of-Speech Tagging
on UD
no code implementations • NAACL 2016 • Sigrid Klerke, Yoav Goldberg, Anders Søgaard
We show how eye-tracking corpora can be used to improve sentence compression models, presenting a novel multi-task learning algorithm based on multi-layer LSTMs.
Ranked #5 on
Sentence Compression
on Google Dataset
1 code implementation • ACL 2016 • Vered Shwartz, Yoav Goldberg, Ido Dagan
Detecting hypernymy relations is a key task in NLP, which is addressed in the literature using two complementary approaches.
1 code implementation • TACL 2016 • Eliyahu Kiperwasser, Yoav Goldberg
The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing.
Ranked #3 on
Chinese Dependency Parsing
on Chinese Pennbank
no code implementations • 11 Mar 2016 • Miguel Ballesteros, Yoav Goldberg, Chris Dyer, Noah A. Smith
We adapt the greedy Stack-LSTM dependency parser of Dyer et al. (2015) to support a training-with-exploration procedure using dynamic oracles(Goldberg and Nivre, 2013) instead of cross-entropy minimization.
Ranked #2 on
Chinese Dependency Parsing
on Chinese Pennbank
no code implementations • 4 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.
Ranked #27 on
Open Information Extraction
on CaRB
no code implementations • TACL 2016 • Eliyahu Kiperwasser, Yoav Goldberg
We suggest a compositional vector representation of parse trees that relies on a recursive combination of recurrent-neural network encoders.
1 code implementation • 2 Oct 2015 • Yoav Goldberg
Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing.
no code implementations • TACL 2015 • Omer Levy, Yoav Goldberg, Ido Dagan
Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks.
no code implementations • NeurIPS 2014 • Omer Levy, Yoav Goldberg
We analyze skip-gram with negative-sampling (SGNS), a word embedding method introduced by Mikolov et al., and show that it is implicitly factorizing a word-context matrix, whose cells are the pointwise mutual information (PMI) of the respective word and context pairs, shifted by a global constant.
5 code implementations • 15 Feb 2014 • Yoav Goldberg, Omer Levy
The word2vec software of Tomas Mikolov and colleagues (https://code. google. com/p/word2vec/ ) has gained a lot of traction lately, and provides state-of-the-art word embeddings.
no code implementations • TACL 2014 • Yoav Goldberg, Francesco Sartorio, Giorgio Satta
We develop parsing oracles for two transition-based dependency parsers, including the arc-standard parser, solving a problem that was left open in (Goldberg and Nivre, 2013).
no code implementations • WS 2013 • Djam{\'e} Seddah, Reut Tsarfaty, S K{\"u}bler, ra, C, Marie ito, Jinho D. Choi, Rich{\'a}rd Farkas, Jennifer Foster, Iakes Goenaga, Koldo Gojenola Galletebeitia, Yoav Goldberg, Spence Green, Nizar Habash, Marco Kuhlmann,