Dialog state tracking, a machine reading approach using Memory Network

By Fei Liu, J. Perez. 2016

59 citations recorded by the source corpus.

In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced by the speech recognition and the natural language understanding modules. This paper introduces a novel method of dialog state tracking based on the general paradigm of machine reading and proposes to solve it using an End-to-End Memory Network, MemN2N, a memory-enhanced neural network architecture. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset. The corpus has been converted for the occasion in order to frame the hidden state variable inference as a question-answering task based on a sequence of utterances extracted from a dialog. We show that the proposed tracker gives encouraging results. Then, we propose to extend the DSTC-2 dataset with specific reasoning capabilities requirement like counting, list maintenance, yes-no question answering and indefinite knowledge management. Finally, we present encouraging results using our proposed MemN2N based tracking model.

Subfields: Artificial Intelligence, Dialogue & Conversational AI

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Citations in the corpus

Neural Belief Tracker: Data-Driven Dialogue State Tracking (2016, methodology): “The downstream DST model then combines this information with the past dialogue context to update the belief state (Thomson and Young, 2010; Wang and Lemon, 2013; Lee and Kim, 2016; Perez, 2016; Perez and Liu, 2017; Sun et al., 2016; Jang et al., 2016; Shi et al., 2016; Dernoncourt et al., 2016; Liu ”
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management (2017, background): “This includes a spoken language understanding module (Chen et al., 2016; Yang et al., 2017), a dialogue belief state tracker (Henderson et al., 2014; Perez and Liu, 2016; Mrkˇsi´c et al., 2017) to predict user intent and track the dialogue history, a dialogue policy (Young et al., 2013; Gaˇsi´c and…”
Multi-task Learning for Joint Language Understanding and Dialogue State Tracking (2018, background): “Perez and Liu 2017 incorporated end-to-end memory networks, as introduced in Sukhbaatar et al. 2015, into state tracking and Liu and Lane 2017 proposed an end-to-end model for belief tracking.”
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory (2017, background): “, 2016) and dialogue state tracking (Perez and Liu, 2016).”
Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering (2019, background): “For example, Perez & Liu (2017) formulate slot tracking as four different types of questions (Factoid, Yes/No, Indefinite knowledge, Counting and Lists/Sets), and use memory network to do reasoning and to predict answers.”
An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking (2018, methodology): “joint modeling of SLU and DST has also been presented in the literature (Henderson et al., 2014b,c; Mrksic et al., 2015; Zilka and Jurcicek, 2015; Perez and Liu, 2017; Mrksic et al., 2017).”
Dialog State Tracking: A Neural Reading Comprehension Approach (2019, background): “Previously, Perez and Liu (2017) also formulated state tracking as a machine reading comprehension problem.”