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Using the Interpolated Maze Task to Assess Incremental Processing in English Relative Clauses
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
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In: Association for Computational Linguistics (2021)
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SyntaxGym: An Online Platform for Targeted Evaluation of Language Models
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In: Association for Computational Linguistics (2021)
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Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization
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In: Association for Computational Linguistics (2021)
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Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study
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In: Association for Computational Linguistics (2021)
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SyntaxGym: An Online Platform for Targeted Evaluation of Language Models
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In: Association for Computational Linguistics (2021)
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Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models
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In: Association for Computational Linguistics (2021)
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Abstract:
Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts. We assess the ability of modern neural language models to reproduce this behavior in English and evaluate the effect of structural supervision on learning outcomes. First, we assess few-shot learning capabilities by developing controlled experiments that probe models’ syntactic nominal number and verbal argument structure generalizations for tokens seen as few as two times during training. Second, we assess invariance properties of learned representation: the ability of a model to transfer syntactic generalizations from a base context (e.g., a simple declarative active-voice sentence) to a transformed context (e.g., an interrogative sentence). We test four models trained on the same dataset: an n-gram baseline, an LSTM, and two LSTM-variants trained with explicit structural supervision (Dyer et al., 2016; Charniak et al., 2016). We find that in most cases, the neural models are able to induce the proper syntactic generalizations after minimal exposure, often from just two examples during training, and that the two structurally supervised models generalize more accurately than the LSTM model. All neural models are able to leverage information learned in base contexts to drive expectations in transformed contexts, indicating that they have learned some invariance properties of syntax.
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URL: https://hdl.handle.net/1721.1/138280
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Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
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In: Association for Computational Linguistics (2021)
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What do RNN Language Models Learn about Filler–Gap Dependencies?
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In: Association for Computational Linguistics (2021)
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A Targeted Assessment of Incremental Processing in Neural Language Models and Humans ...
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A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humans ...
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Which Presuppositions are Subject to Contextual Felicity Constraints?
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In: Semantics and Linguistic Theory; Proceedings of SALT 31; 345-364 ; 2163-5951 (2021)
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A Systematic Assessment of Syntactic Generalization in Neural Language Models ...
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Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations ...
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Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study ...
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RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency ...
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