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

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

Research output: Contribution to journalArticle

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3236-3240
Number of pages5
JournalUnknown Journal
Publication statusPublished - 2016
Externally publishedYes


  • Attention LSTMs
  • Bidirectional LSTMs
  • Context sensitive understanding
  • Role-dependent model
  • Spoken language understanding

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

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