Neural dialog state tracker for large ontologies by attention mechanism

Jang, Young-Soo; Byung-Jun, Lee; Chang, Youngjae; Kim, Kee-Eung

doi:10.1109/slt.2016.7846314

Cited by 14 publications

(11 citation statements)

References 11 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%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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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)

358

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%

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)

358

252

View full text Add to dashboard Cite

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“…The dialogue state tracking (DST) problem has attracted the research community for years. The traditional DST models focus on single domain di-alogue state tracking Wang and Lemon, 2013;Liu and Perez, 2017;Jang et al, 2016;Shi et al, 2016;Vodolán et al, 2017;Yu et al, 2015;Henderson et al, 2014;Zilka and Jurcícek, 2015;Mrksic et al, 2017;Xu and Hu, 2018;Zhong et al, 2018;Ren et al, 2018). Some of these models solve DST problem by incorporating a natural language understanding (NLU) module Wang and Lemon, 2013) or jointly modeling NLU and DST (Henderson et al, 2014;Zilka and Jurcícek, 2015), which rely on hand-crafted features or delexicalisation features.…”

Section: Related Workmentioning

confidence: 99%

Parallel Interactive Networks for Multi-Domain Dialogue State Generation

Chen¹,

Zhang²,

Mao³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) models. In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies. Specifically, we integrate an interactive encoder to jointly model the in-turn dependencies and cross-turn dependencies. The slot-level context is introduced to extract more expressive features for different slots. And a distributed copy mechanism is utilized to selectively copy words from historical system utterances or historical user utterances. Empirical studies demonstrated the superiority of the proposed PIN model. * Corresponding author much attention in the research community. Instead of only predicting the (slot, value) pair, in MDST, a model is expected to predict the (domain, slot, value) triplets for each slot in each domain. This task is a great challenge not only because of the large ontology involving 30 slots and exceeding 4500 values , but also the mixed-domain nature of the dialogues and some complex cases involving cross-turn inference. u1: I want a cheap european restaurant. s1: Can I help you? s2: There is a Curry Garden, and hotel? u2: I need a hotel with free-wifi.

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“…Traditional works deal with the DST task using Spoken Language Understanding (SLU), including (Thomson and Young, 2010;Wang and Lemon, 2013;Liu and Perez, 2017;Jang et al, 2016;Shi et al, 2016;Vodolán et al, 2017). Joint modeling of SLU and DST (Henderson et al, 2014c;Zilka and Jurcícek, 2015;Mrksic et al, 2015) has also been presented and shown to outperform the separate SLU models.…”

Section: Related Workmentioning

confidence: 99%

Neural Dialogue State Tracking with Temporally Expressive Networks

Chen

Zhang

Mao

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to improve the accuracy of turn-level-state prediction and the state aggregation.

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Neural dialog state tracker for large ontologies by attention mechanism

Cited by 14 publications

References 11 publications

Neural Belief Tracker: Data-Driven Dialogue State Tracking

Neural Belief Tracker: Data-Driven Dialogue State Tracking

Parallel Interactive Networks for Multi-Domain Dialogue State Generation

Neural Dialogue State Tracking with Temporally Expressive Networks

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