Context-sensitive and role-dependent spoken language understanding using bidirectional and attention LSTMs

Chiori Hori, Takaaki Hori, Shinji Watanabe, John R. Hershey

研究成果: Conference article査読

13 被引用数 (Scopus)

抄録

To understand speaker intentions accurately in a dialog, it is important to consider the context of the surrounding sequence of dialog turns. Furthermore, each speaker may play a different role in the conversation, such as agent versus client, and thus features related to these roles may be important to the context. In previous work, we proposed context-sensitive spoken language understanding (SLU) using role-dependent long short-term memory (LSTM) recurrent neural networks (RNNs), and showed improved performance at predicting concept tags representing the intentions of agent and client in a human-human hotel reservation task. In the present study, we use bidirectional and attention-based LSTMs to train a roledependent context-sensitive model to jointly represent both the local word-level context within each utterance, and the left and right context within the dialog. The different roles of client and agent are modeled by switching between role-dependent layers. We evaluated label accuracies in the hotel reservation task using a variety of models, including logistic regression, RNNs, LSTMs, and the proposed bidirectional and attentionbased LSTMs. The bidirectional and attention-based LSTMs yield significantly better performance in this task.

本文言語English
ページ(範囲)3236-3240
ページ数5
ジャーナルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
08-12-September-2016
DOI
出版ステータスPublished - 2016
外部発表はい
イベント17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States
継続期間: 2016 9月 82016 9月 16

ASJC Scopus subject areas

  • 言語および言語学
  • 人間とコンピュータの相互作用
  • 信号処理
  • ソフトウェア
  • モデリングとシミュレーション

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