Tal Linzen

According to our database, Tal Linzen authored at least 75 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser.

Grusha Prasad, Tal Linzen

CoRR, 2024

Can You Learn Semantics Through Next-Word Prediction? The Case of Entailment.

William Merrill, Zhaofeng Wu, Norihito Naka, Yoon Kim, Tal Linzen

CoRR, 2024

2023

In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax.

Aaron Mueller, Albert Webson, Jackson Petty, Tal Linzen

CoRR, 2023

A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models.

Tiwalayo Eisape, Michael Henry Tessler, Ishita Dasgupta, Fei Sha, Sjoerd van Steenkiste, Tal Linzen

CoRR, 2023

The Impact of Depth and Width on Transformer Language Model Generalization.

Jackson Petty, Sjoerd van Steenkiste, Ishita Dasgupta, Fei Sha, Dan Garrette, Tal Linzen

CoRR, 2023

Do Language Models Refer?

Matthew Mandelkern, Tal Linzen

CoRR, 2023

Language Models Can Learn Exceptions to Syntactic Rules.

Cara Su-Yi Leong, Tal Linzen

CoRR, 2023

A Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing.

William Timkey, Tal Linzen

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

SLOG: A Structural Generalization Benchmark for Semantic Parsing.

Bingzhi Li, Lucia Donatelli, Alexander Koller, Tal Linzen, Yuekun Yao, Najoung Kim

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Verb Conjugation in Transformers Is Determined by Linear Encodings of Subject Number.

Sophie Hao, Tal Linzen

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech.

Aditya Yedetore, Tal Linzen, Robert Frank, R. Thomas McCoy

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases.

Aaron Mueller, Tal Linzen

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark.

Nouha Dziri, Hannah Rashkin, Tal Linzen, David Reitter

Trans. Assoc. Comput. Linguistics, 2022

Uncontrolled Lexical Exposure Leads to Overestimation of Compositional Generalization in Pretrained Models.

Najoung Kim, Tal Linzen, Paul Smolensky

CoRR, 2022

When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it.

Sebastian Schuster, Tal Linzen

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

Improving Compositional Generalization with Latent Structure and Data Augmentation.

Linlu Qiu, Peter Shaw, Panupong Pasupat, Pawel Krzysztof Nowak, Tal Linzen, Fei Sha, Kristina Toutanova

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

The MultiBERTs: BERT Reproductions for Robustness Analysis.

Thibault Sellam, Steve Yadlowsky, Ian Tenney, Jason Wei, Naomi Saphra, Alexander D'Amour, Tal Linzen, Jasmijn Bastings, Iulia Raluca Turc, Jacob Eisenstein, Dipanjan Das, Ellie Pavlick

Proceedings of the Tenth International Conference on Learning Representations, 2022

Causal Analysis of Syntactic Agreement Neurons in Multilingual Language Models.

Aaron Mueller, Yu Xia, Tal Linzen

Proceedings of the 26th Conference on Computational Natural Language Learning, 2022

Entailment Semantics Can Be Extracted from an Ideal Language Model.

William Merrill, Alex Warstadt, Tal Linzen

Proceedings of the 26th Conference on Computational Natural Language Learning, 2022

Characterizing Verbatim Short-Term Memory in Neural Language Models.

Kristijan Armeni, Christopher J. Honey, Tal Linzen

Proceedings of the 26th Conference on Computational Natural Language Learning, 2022

Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities.

Suhas Arehalli, Brian Dillon, Tal Linzen

Proceedings of the 26th Conference on Computational Natural Language Learning, 2022

Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models.

Aaron Mueller, Robert Frank, Tal Linzen, Luheng Wang, Sebastian Schuster

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022

2021

How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN.

R. Thomas McCoy, Paul Smolensky, Tal Linzen, Jianfeng Gao, Asli Celikyilmaz

CoRR, 2021

Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks.

Wang Zhu, Peter Shaw, Tal Linzen, Fei Sha

CoRR, 2021

Evaluating Groundedness in Dialogue Systems: The BEGIN Benchmark.

Nouha Dziri, Hannah Rashkin, Tal Linzen, David Reitter

CoRR, 2021

Single-Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty.

Marten van Schijndel, Tal Linzen

Cogn. Sci., 2021

Predicting Inductive Biases of Pre-Trained Models.

Charles Lovering, Rohan Jha, Tal Linzen, Ellie Pavlick

Proceedings of the 9th International Conference on Learning Representations, 2021

Frequency Effects on Syntactic Rule Learning in Transformers.

Jason Wei, Dan Garrette, Tal Linzen, Ellie Pavlick

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Does Putting a Linguist in the Loop Improve NLU Data Collection?

Alicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alex Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, Samuel R. Bowman

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction.

Shauli Ravfogel, Grusha Prasad, Tal Linzen, Yoav Goldberg

Proceedings of the 25th Conference on Computational Natural Language Learning, 2021

NOPE: A Corpus of Naturally-Occurring Presuppositions in English.

Alicia Parrish, Sebastian Schuster, Alex Warstadt, Omar Agha, Soo-Hwan Lee, Zhuoye Zhao, Samuel R. Bowman, Tal Linzen

Proceedings of the 25th Conference on Computational Natural Language Learning, 2021

The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation.

Laura Aina, Tal Linzen

Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2021

Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models.

Matthew Finlayson, Aaron Mueller, Sebastian Gehrmann, Stuart M. Shieber, Tal Linzen, Yonatan Belinkov

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020

Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks.

R. Thomas McCoy, Robert Frank, Tal Linzen

Trans. Assoc. Comput. Linguistics, 2020

Syntactic Structure from Deep Learning.

Tal Linzen, Marco Baroni

CoRR, 2020

COGS: A Compositional Generalization Challenge Based on Semantic Interpretation.

Najoung Kim, Tal Linzen

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Universal linguistic inductive biases via meta-learning.

Richard Thomas McCoy, Erin Grant, Paul Smolensky, Tom Griffiths, Tal Linzen

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

Neural Language Models Capture Some, But Not All Agreement Attraction Effects.

Suhas Arehalli, Tal Linzen

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

Discovering the Compositional Structure of Vector Representations with Role Learning Networks.

Paul Soulos, R. Thomas McCoy, Tal Linzen, Paul Smolensky

Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2020

BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance.

R. Thomas McCoy, Junghyun Min, Tal Linzen

Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2020

Cross-Linguistic Syntactic Evaluation of Word Prediction Models.

Aaron Mueller, Garrett Nicolai, Panayiota Petrou-Zeniou, Natalia Talmina, Tal Linzen

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

Syntactic Data Augmentation Increases Robustness to Inference Heuristics.

Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler, Tal Linzen

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

How Can We Accelerate Progress Towards Human-like Linguistic Generalization?

Tal Linzen

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs.

Michael A. Lepori, Tal Linzen, R. Thomas McCoy

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

Analyzing and interpreting neural networks for NLP: A report on the first BlackboxNLP workshop.

Afra Alishahi, Grzegorz Chrupala, Tal Linzen

Nat. Lang. Eng., 2019

Probing What Different NLP Tasks Teach Machines about Function Word Comprehension.

Najoung Kim, Roma Patel, Adam Poliak, Patrick Xia, Alex Wang, Tom McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Samuel R. Bowman, Ellie Pavlick

Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics, 2019

Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages.

Shauli Ravfogel, Yoav Goldberg, Tal Linzen

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

RNNs implicitly implement tensor-product representations.

R. Thomas McCoy, Tal Linzen, Ewan Dunbar, Paul Smolensky

Proceedings of the 7th International Conference on Learning Representations, 2019

Quantity doesn't buy quality syntax with neural language models.

Marten van Schijndel, Aaron Mueller, Tal Linzen

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models.

Grusha Prasad, Marten van Schijndel, Tal Linzen

Proceedings of the 23rd Conference on Computational Natural Language Learning, 2019

How much harder are hard garden-path sentences than easy ones?

Grusha Prasad, Tal Linzen

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Human few-shot learning of compositional instructions.

Brenden M. Lake, Tal Linzen, Marco Baroni

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference.

Tom McCoy, Ellie Pavlick, Tal Linzen

Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

2018

Non-entailed subsequences as a challenge for natural language inference.

R. Thomas McCoy, Tal Linzen

CoRR, 2018

Can Entropy Explain Successor Surprisal Effects in Reading?

Marten van Schijndel, Tal Linzen

CoRR, 2018

What can linguistics and deep learning contribute to each other?

Tal Linzen

CoRR, 2018

Colorless Green Recurrent Networks Dream Hierarchically.

Kristina Gulordava, Piotr Bojanowski, Edouard Grave, Tal Linzen, Marco Baroni

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018

A Neural Model of Adaptation in Reading.

Marten van Schijndel, Tal Linzen

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

Targeted Syntactic Evaluation of Language Models.

Rebecca Marvin, Tal Linzen

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

Modeling garden path effects without explicit hierarchical syntax.

Marten van Schijndel, Tal Linzen

Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks.

Richard Thomas McCoy, Robert Frank, Tal Linzen

Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

Distinct patterns of syntactic agreement errors in recurrent networks and humans.

Tal Linzen, Brian Leonard

Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

Phonological (un)certainty weights lexical activation.

Laura Gwilliams, David Poeppel, Alec Marantz, Tal Linzen

Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics, 2018

2017

Comparing Character-level Neural Language Models Using a Lexical Decision Task.

Gaël Le Godais, Tal Linzen, Emmanuel Dupoux

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, 2017

Exploring the Syntactic Abilities of RNNs with Multi-task Learning.

Émile Enguehard, Yoav Goldberg, Tal Linzen

Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), 2017

Prediction and uncertainty in an artificial language.

Tal Linzen, Noam Siegelman, Louisa Bogaerts

Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

2016

Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies.

Tal Linzen, Emmanuel Dupoux, Yoav Goldberg

Trans. Assoc. Comput. Linguistics, 2016

Uncertainty and Expectation in Sentence Processing: Evidence From Subcategorization Distributions.

Tal Linzen, T. Florian Jaeger

Cogn. Sci., 2016

Quantificational features in distributional word representations.

Tal Linzen, Emmanuel Dupoux, Benjamin Spector

Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics, 2016

Issues in evaluating semantic spaces using word analogies.

Tal Linzen

Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP, 2016

Evaluating vector space models using human semantic priming results.

Allyson Ettinger, Tal Linzen

Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP, 2016

2015

Lexical Preactivation in Basic Linguistic Phrases.

Joseph Fruchter, Tal Linzen, Masha Westerlund, Alec Marantz

J. Cogn. Neurosci., 2015

A model of rapid phonotactic generalization.

Tal Linzen, Timothy O'Donnell

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015

2014

The timecourse of phonotactic learning.

Tal Linzen, Gillian Gallagher

Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 2014

Investigating the role of entropy in sentence processing.

Tal Linzen, T. Florian Jaeger

Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics, 2014