Domain State Tracking for a Simplified Dialogue System

• URL: http://arxiv.org/abs/2103.06648v1
• Date: Thu, 11 Mar 2021 13:00:54 GMT
• Title: Domain State Tracking for a Simplified Dialogue System
• Authors: Hyunmin Jeon, Gary Geunbae Lee
• Abstract summary: We present DoTS, a task-oriented dialogue system that uses a simplified input context instead of the entire dialogue history. DoTS improves the inform rate and success rate by 1.09 points and 1.24 points, respectively, compared to the previous state-of-the-art model on MultiWOZ.
• Score: 3.962145079528281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Task-oriented dialogue systems aim to help users achieve their goals in specific domains. Recent neural dialogue systems use the entire dialogue history for abundant contextual information accumulated over multiple conversational turns. However, the dialogue history becomes increasingly longer as the number of turns increases, thereby increasing memory usage and computational costs. In this paper, we present DoTS (Domain State Tracking for a Simplified Dialogue System), a task-oriented dialogue system that uses a simplified input context instead of the entire dialogue history. However, neglecting the dialogue history can result in a loss of contextual information from previous conversational turns. To address this issue, DoTS tracks the domain state in addition to the belief state and uses it for the input context. Using this simplified input, DoTS improves the inform rate and success rate by 1.09 points and 1.24 points, respectively, compared to the previous state-of-the-art model on MultiWOZ, which is a well-known benchmark.

Related papers

• Adapting Text-based Dialogue State Tracker for Spoken Dialogues [20.139351605832665]
  We describe our engineering effort in building a highly successful model that participated in the speech-aware dialogue systems technology challenge track in DSTC11. Our model consists of three major modules: (1) automatic speech recognition error correction to bridge the gap between the spoken and the text utterances, (2) text-based dialogue system (D3ST) for estimating the slots and values using slot descriptions, and (3) post-processing for recovering the error of the estimated slot value.
  arXiv  Detail & Related papers  (2023-08-29T06:27:58Z)
• PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model [79.64376762489164]
  PK-Chat is a Pointer network guided generative dialogue model, incorporating a unified pretrained language model and a pointer network over knowledge graphs. The words generated by PK-Chat in the dialogue are derived from the prediction of word lists and the direct prediction of the external knowledge graph knowledge. Based on the PK-Chat, a dialogue system is built for academic scenarios in the case of geosciences.
  arXiv  Detail & Related papers  (2023-04-02T18:23:13Z)
• A Benchmark for Understanding and Generating Dialogue between Characters in Stories [75.29466820496913]
  We present the first study to explore whether machines can understand and generate dialogue in stories. We propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory.
  arXiv  Detail & Related papers  (2022-09-18T10:19:04Z)
• Multimodal Dialogue State Tracking [97.25466640240619]
  Video-Dialogue Transformer Network (VDTN) learns contextual dependencies between videos and dialogues to generate multimodal dialogue states. VDTN combines both object-level features and segment-level features and learns contextual dependencies between videos and dialogues to generate multimodal dialogue states.
  arXiv  Detail & Related papers  (2022-06-16T03:18:42Z)
• Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking [18.172993687706708]
  In dialogue state tracking, dialogue history is a crucial material, and its utilization varies between different models. We propose DiCoS-DST to dynamically select the relevant dialogue contents corresponding to each slot for state updating. Our approach achieves new state-of-the-art performance on MultiWOZ 2.1 and MultiWOZ 2.2, and achieves superior performance on multiple mainstream benchmark datasets.
  arXiv  Detail & Related papers  (2022-05-20T10:08:45Z)
• End-to-end Spoken Conversational Question Answering: Task, Dataset and Model [92.18621726802726]
  In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. We propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogue flows. Our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering.
  arXiv  Detail & Related papers  (2022-04-29T17:56:59Z)
• HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data [87.67278915655712]
  We present a new dialogue dataset, HybriDialogue, which consists of crowdsourced natural conversations grounded on both Wikipedia text and tables. The conversations are created through the decomposition of complex multihop questions into simple, realistic multiturn dialogue interactions.
  arXiv  Detail & Related papers  (2022-04-28T00:52:16Z)
• Integrating Dialog History into End-to-End Spoken Language Understanding Systems [37.08876551722831]
  We investigate the importance of dialog history and how it can be effectively integrated into end-to-end spoken language understanding systems. While processing a spoken utterance, our proposed RNN transducer (RNN-T) based SLU model has access to its dialog history in the form of decoded transcripts and SLU labels of previous turns. We evaluate our approach on a recently released spoken dialog data set, the HarperValleyBank corpus.
  arXiv  Detail & Related papers  (2021-08-18T22:24:11Z)
• Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking? [17.476030563395714]
  Dialogue state tracking (DST) plays a key role in task-oriented dialogue systems to monitor the user's goal. In general, there are two strategies to track a dialogue state: predicting it from scratch and updating it from previous state.
  arXiv  Detail & Related papers  (2021-05-08T03:18:13Z)
• Rethinking Dialogue State Tracking with Reasoning [76.0991910623001]
  This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method significantly outperforms the state-of-the-art methods by 38.6% in terms of joint belief accuracy for MultiWOZ 2.1.
  arXiv  Detail & Related papers  (2020-05-27T02:05:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.