Convolutional Neural Networks for Multi-topic Dialog State Tracking

Shi, Hongjie; Ushio, Takashi; Endo, Mitsuru; Yamagami, Katsuyoshi; Horii, Noriaki

doi:10.1007/978-981-10-2585-3_37

Cited by 19 publications

(14 citation statements)

References 4 publications

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“…To motivate the work presented here, we categorise prior research according to their reliance (or otherwise) on a separate SLU module for interpreting user utterances: 1 Separate SLU Traditional SDS pipelines use Spoken Language Understanding (SLU) decoders to detect slot-value pairs expressed in the Automatic Speech Recognition (ASR) output. The downstream DST model then combines this information with the past dialogue context to update the belief state Wang and Lemon, 2013;Perez, 2016;Sun et al, 2016;Jang et al, 2016;Shi et al, 2016;Dernoncourt et al, 2016;Vodolán et al, 2017). Figure 3: Architecture of the NBT Model.…”

Section: Introductionmentioning

confidence: 99%

Neural Belief Tracker: Data-Driven Dialogue State Tracking

Mrkšić¹,

Séaghdha²,

Wen³

et al. 2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

355

252

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One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user's goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users' language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.

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Section: Introductionmentioning

confidence: 99%

Neural Belief Tracker: Data-Driven Dialogue State Tracking

Mrkšić¹,

Séaghdha²,

Wen³

et al. 2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

355

252

View full text Add to dashboard Cite

show abstract

“…On the other hand, prior work on LU on H2H conversations has focused on dialog state detection and tracking for spoken dialog systems. Shi et al (2017) used CNN model, and later extended multiple channel model for a cross-language scenario (Shi et al, 2016). Jang et al (2018) used attention mechanism to focus on words with meaningful context, and Su et al (2018) used a time decay model to incorporate temporal information.…”

Section: Related Workmentioning

confidence: 99%

Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-Human Conversation Scenarios

Kim

Jha

Williams

et al. 2019

Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

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Task oriented language understanding (LU) in human-to-machine (H2M) conversations has been extensively studied for personal digital assistants. In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task. Recent advances on LU in H2M conversations have shown accuracy improvements by adding encoded knowledge from different sources. Inspired by this, we explore several variants of a bidirectional LSTM architecture that relies on different knowledge sources, such as Web data, search engine click logs, expert feedback from H2M models, as well as previous utterances in the conversation. We also propose ensemble techniques that aggregate these different knowledge sources into a single model. Experimental evaluation on a four-turn Twitter dataset in the restaurant and music domains shows improvements in the slot tagging F1-score of up to 6.09% compared to existing approaches.

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“…On the other hand, a person requests tour information to a (human) tour guide in DSTC4. Since the DSTC data set is used in most recent studies [ 20 , 21 ], it is now regarded as a standard data set for dialog state tracking.…”

Section: Related Workmentioning

confidence: 99%

“…The input to this LSTM is raw dialog utterances of a user. On the other hand, Shi et al used a CNN to focus on tracking general information in a subdialog segment [ 21 ]. To deal with multitopic dialogs, their CNN is composed of general and topic-specific filters that share topic information with each other.…”

Section: Related Workmentioning

confidence: 99%

A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing

Kim

Song

Park

2018

Computational Intelligence and Neuroscience

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Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance. Since the success of a dialog is influenced by the ability of the system to catch the requirements of the user, accurate state tracking is important for spoken dialog systems. This paper proposes a two-step neural dialog state tracker which is composed of an informativeness classifier and a neural tracker. The informativeness classifier which is implemented by a CNN first filters out noninformative utterances in a dialog. Then, the neural tracker estimates dialog states from the remaining informative utterances. The tracker adopts the attention mechanism and the hierarchical softmax for its performance and fast training. To prove the effectiveness of the proposed model, we do experiments on dialog state tracking in the human-human task-oriented dialogs with the standard DSTC4 data set. Our experimental results prove the effectiveness of the proposed model by showing that the proposed model outperforms the neural trackers without the informativeness classifier, the attention mechanism, or the hierarchical softmax.

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Convolutional Neural Networks for Multi-topic Dialog State Tracking

Cited by 19 publications

References 4 publications

Neural Belief Tracker: Data-Driven Dialogue State Tracking

Neural Belief Tracker: Data-Driven Dialogue State Tracking

Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-Human Conversation Scenarios

A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing

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