Tal Linzen

Exploring the Syntactic Abilities of RNNs with Multi-task Learning

Recent work has explored the syntactic abilities of RNNs using the subje...
06/12/2017 ∙ by Emile Enguehard, et al. ∙ 0 ∙ share

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Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

The success of long short-term memory (LSTM) neural networks in language...
11/04/2016 ∙ by Tal Linzen, et al. ∙ 0 ∙ share

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Issues in evaluating semantic spaces using word analogies

The offset method for solving word analogies has become a standard evalu...
06/24/2016 ∙ by Tal Linzen, et al. ∙ 0 ∙ share

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Phonological (un)certainty weights lexical activation

Spoken word recognition involves at least two basic computations. First ...
11/17/2017 ∙ by Laura Gwilliams, et al. ∙ 0 ∙ share

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

Syntactic rules in human language usually refer to the hierarchical stru...
02/25/2018 ∙ by R. Thomas McCoy, et al. ∙ 0 ∙ share

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Colorless green recurrent networks dream hierarchically

Recurrent neural networks (RNNs) have achieved impressive results in a v...
03/29/2018 ∙ by Kristina Gulordava, et al. ∙ 0 ∙ share

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Distinct patterns of syntactic agreement errors in recurrent networks and humans

Determining the correct form of a verb in context requires an understand...
07/18/2018 ∙ by Tal Linzen, et al. ∙ 0 ∙ share

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Targeted Syntactic Evaluation of Language Models

We present a dataset for evaluating the grammaticality of the prediction...
08/27/2018 ∙ by Rebecca Marvin, et al. ∙ 0 ∙ share

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What can linguistics and deep learning contribute to each other?

Joe Pater's target article calls for greater interaction between neural ...
09/11/2018 ∙ by Tal Linzen, et al. ∙ 0 ∙ share

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A Neural Model of Adaptation in Reading

It has been argued that humans rapidly adapt their lexical and syntactic...
08/29/2018 ∙ by Marten van Schijndel, et al. ∙ 0 ∙ share

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Can Entropy Explain Successor Surprisal Effects in Reading?

Human reading behavior is sensitive to surprisal: more predictable words...
10/26/2018 ∙ by Marten van Schijndel, et al. ∙ 0 ∙ share

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Non-entailed subsequences as a challenge for natural language inference

Neural network models have shown great success at natural language infer...
11/29/2018 ∙ by R. Thomas McCoy, et al. ∙ 0 ∙ share

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RNNs Implicitly Implement Tensor Product Representations

Recurrent neural networks (RNNs) can learn continuous vector representat...
12/20/2018 ∙ by R. Thomas McCoy, et al. ∙ 0 ∙ share

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Human few-shot learning of compositional instructions

People learn in fast and flexible ways that have not been emulated by ma...
01/14/2019 ∙ by Brenden M. Lake, et al. ∙ 0 ∙ share

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

Machine learning systems can often achieve high performance on a test se...
02/04/2019 ∙ by R. Thomas McCoy, et al. ∙ 0 ∙ share

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

How do typological properties such as word order and morphological case ...
03/15/2019 ∙ by Shauli Ravfogel, et al. ∙ 0 ∙ share

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Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop

The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techn...
04/05/2019 ∙ by Afra Alishahi, et al. ∙ 0 ∙ share

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Probing What Different NLP Tasks Teach Machines about Function Word Comprehension

We introduce a set of nine challenge tasks that test for the understandi...
04/25/2019 ∙ by Najoung Kim, et al. ∙ 0 ∙ share

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Quantity doesn't buy quality syntax with neural language models

Recurrent neural networks can learn to predict upcoming words remarkably...
08/31/2019 ∙ by Marten van Schijndel, et al. ∙ 0 ∙ share

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

Neural language models (LMs) perform well on tasks that require sensitiv...
09/23/2019 ∙ by Grusha Prasad, et al. ∙ 0 ∙ share

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Discovering the Compositional Structure of Vector Representations with Role Learning Networks

Neural networks (NNs) are able to perform tasks that rely on composition...
10/21/2019 ∙ by Paul Soulos, et al. ∙ 0 ∙ share

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

If the same neural architecture is trained multiple times on the same da...
11/07/2019 ∙ by R. Thomas McCoy, et al. ∙ 0 ∙ share

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Does syntax need to grow on trees? Sources of hierarchical inductive bias in sequence-to-sequence networks

Learners that are exposed to the same training data might generalize dif...
01/10/2020 ∙ by R. Thomas McCoy, et al. ∙ 0 ∙ share

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Tal Linzen

Featured Co-authors

Yoav Goldberg

Benjamin Van Durme

Samuel R. Bowman

Marco Baroni

Edouard Grave

Emmanuel Dupoux

Piotr Bojanowski

Brenden M. Lake

Paul Smolensky

Alex Wang

Grzegorz Chrupała

Exploring the Syntactic Abilities of RNNs with Multi-task Learning

Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

Issues in evaluating semantic spaces using word analogies

Phonological (un)certainty weights lexical activation

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

Colorless green recurrent networks dream hierarchically

Distinct patterns of syntactic agreement errors in recurrent networks and humans

Targeted Syntactic Evaluation of Language Models

What can linguistics and deep learning contribute to each other?

A Neural Model of Adaptation in Reading

Can Entropy Explain Successor Surprisal Effects in Reading?

Non-entailed subsequences as a challenge for natural language inference

RNNs Implicitly Implement Tensor Product Representations

Human few-shot learning of compositional instructions

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

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

Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop

Probing What Different NLP Tasks Teach Machines about Function Word Comprehension

Quantity doesn't buy quality syntax with neural language models

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

Discovering the Compositional Structure of Vector Representations with Role Learning Networks

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

Does syntax need to grow on trees? Sources of hierarchical inductive bias in sequence-to-sequence networks