Abstract
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 language | English |
|---|---|
| Pages (from-to) | 3236-3240 |
| Number of pages | 5 |
| Journal | Unknown Journal |
| Volume | 08-12-September-2016 |
| DOIs | |
| Publication status | Published - 2016 |
| Externally published | Yes |
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Keywords
- 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
Cite this
Context-sensitive and role-dependent spoken language understanding using bidirectional and attention LSTMs. / Hori, Chiori; Hori, Takaaki; Watanabe, Shinji; Hershey, John R.
In: Unknown Journal, Vol. 08-12-September-2016, 2016, p. 3236-3240.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Context-sensitive and role-dependent spoken language understanding using bidirectional and attention LSTMs
AU - Hori, Chiori
AU - Hori, Takaaki
AU - Watanabe, Shinji
AU - Hershey, John R.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Attention LSTMs
KW - Bidirectional LSTMs
KW - Context sensitive understanding
KW - Role-dependent model
KW - Spoken language understanding
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U2 - 10.21437/Interspeech.2016-1171
DO - 10.21437/Interspeech.2016-1171
M3 - Article
VL - 08-12-September-2016
SP - 3236
EP - 3240
JO - Nuclear Physics A
JF - Nuclear Physics A
SN - 0375-9474
ER -