Scaffolded input promotes atomic organization in the recurrent neural network language model

Philip A. Huebner, Jon A. Willits

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The recurrent neural network (RNN) language model is a powerful tool for learning arbitrary sequential dependencies in language data. Despite its enormous success in representing lexical sequences, little is known about the quality of the lexical representations that it acquires. In this work, we conjecture that it is straightforward to extract lexical representations (i.e. static word embeddings) from an RNN, but that the amount of semantic information that is encoded is limited when lexical items in the training data provide redundant semantic information. We conceptualize this limitation of the RNN as a failure to learn atomic internal states - states which capture information relevant to single word types without being influenced by redundant information provided by words with which they co-occur. Using a corpus of artificial language, we verify that redundancy in the training data yields non-atomic internal states, and propose a novel method for inducing atomic internal states. We show that 1) our method successfully induces atomic internal organization in controlled experiments, and 2) under more realistic conditions in which the training consists of child-directed language, application of our method improves the performance of lexical representations on a downstream semantic categorization task.

Original languageEnglish (US)
Title of host publicationCoNLL 2021 - 25th Conference on Computational Natural Language Learning, Proceedings
EditorsArianna Bisazza, Omri Abend
PublisherAssociation for Computational Linguistics (ACL)
Pages408-422
Number of pages15
ISBN (Electronic)9781955917056
StatePublished - 2021
Event25th Conference on Computational Natural Language Learning, CoNLL 2021 - Virtual, Online
Duration: Nov 10 2021Nov 11 2021

Publication series

NameCoNLL 2021 - 25th Conference on Computational Natural Language Learning, Proceedings

Conference

Conference25th Conference on Computational Natural Language Learning, CoNLL 2021
CityVirtual, Online
Period11/10/2111/11/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

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