• DOI:10.18653/v1/W18-5423
Corpus ID: 52156878

What do RNN Language Models Learn about Filler-Gap Dependencies?

@inproceedings{Wilcox2018WhatDR,
  title={What do RNN Language Models Learn about Filler-Gap Dependencies?},
  author={E. Wilcox and R. Levy and T. Morita and Richard Futrell},
  booktitle={BlackboxNLP@EMNLP},
  year={2018}
}

E. Wilcox, R. Levy, +1 author Richard Futrell

Published in BlackboxNLP@EMNLP 2018

Computer Science

RNN language models have achieved state-of-the-art perplexity results and have proven useful in a suite of NLP tasks, but it is as yet unclear what syntactic generalizations they learn. Here we investigate whether state-of-the-art RNN language models represent long-distance filler-gap dependencies and constraints on them. Examining RNN behavior on experimentally controlled sentences designed to expose filler-gap dependencies, we show that RNNs can represent the relationship in multiple… CONTINUE READING

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