An End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification

2019-12-19 · Yue Ma, Zengfeng Zeng, Dawei Zhu, Xuan Li, Yiying Yang, Xiaoyuan Yao, Kaijie Zhou, Jianping Shen

This paper describes our approach in DSTC 8 Track 4: Schema-Guided Dialogue State Tracking. The goal of this task is to predict the intents and slots in each user turn to complete the dialogue state tracking (DST) based on the information provided by the task's schema. Different from traditional stage-wise DST, we propose an end-to-end DST system to avoid error accumulation between the dialogue turns. The DST system consists of a machine reading comprehension (MRC) model for non-categorical slots and a Wide & Deep model for categorical slots. As far as we know, this is the first time that MRC and Wide & Deep model are applied to DST problem in a fully end-to-end way. Experimental results show that our framework achieves an excellent performance on the test dataset including 50% zero-shot services with a joint goal accuracy of 0.8652 and a slot tagging F1-Score of 0.9835.

📄 PDF Abstract BibTeX arXiv:1912.09297

Code (0)

등록된 구현이 없습니다.

Tasks

Dialogue State Tracking General Classification Machine Reading Comprehension Reading Comprehension

Methods 이 논문이 사용한 방법론

DST Dynamic sparse training methods train neural networks in a sparse manner, starting with an initial sparse mask, and periodically updating the mask based on some criteria.

Test 설명 없음

From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap

2020-04-13 · WS 2020 7 · Shuyang Gao, Sanchit Agarwal, Tagyoung Chung, Di Jin 외

Dialogue state tracking (DST) is at the heart of task-oriented dialogue systems. However, the scarcity of labeled data is an obstacle to building accurate and robust state tracking systems that work across a variety of d…

Dialogue State Tracking Machine Reading Comprehension Multiple-choice Reading Comprehension+1

Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking

2022-08-04 · Ruolin Su, Ting-Wei Wu, Biing-Hwang Juang

As an essential component in task-oriented dialogue systems, dialogue state tracking (DST) aims to track human-machine interactions and generate state representations for managing the dialogue. Representations of dialogu…

Dialogue State Tracking Machine Reading Comprehension Multi-domain Dialogue State Tracking Reading Comprehension+1

Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker

2020-02-05 · Pavel Gulyaev, Eugenia Elistratova, Vasily Konovalov, Yuri Kuratov 외

Dialogue State Tracking (DST) is a core component of virtual assistants such as Alexa or Siri. To accomplish various tasks, these assistants need to support an increasing number of services and APIs. The Schema-Guided St…

Dialogue State Tracking Question Answering Reading Comprehension

KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information

2023-01-18 · Hyungtak Choi, Hyeonmok Ko, Gurpreet Kaur, Lohith Ravuru 외

Dialogue State Tracking (DST) is core research in dialogue systems and has received much attention. In addition, it is necessary to define a new problem that can deal with dialogue between users as a step toward the conv…

Dialogue State Tracking Machine Reading Comprehension Reading Comprehension Scheduling

SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task

2022-09-16 · Jihyun Lee, Gary Geunbae Lee

Few-shot dialogue state tracking (DST) model tracks user requests in dialogue with reliable accuracy even with a small amount of data. In this paper, we introduce an ontology-free few-shot DST with self-feeding belief st…

Dialogue State Tracking Reading Comprehension