A Compare Aggregate Transformer for Understanding Document-grounded Dialogue

• URL: http://arxiv.org/abs/2010.00190v1
• Date: Thu, 1 Oct 2020 03:44:44 GMT
• Title: A Compare Aggregate Transformer for Understanding Document-grounded Dialogue
• Authors: Longxuan Ma and Weinan Zhang and Runxin Sun and Ting Liu
• Abstract summary: We propose a Compare Aggregate Transformer (CAT) to jointly denoise the dialogue context and aggregate the document information for response generation. Experimental results on the CMUDoG dataset show that the proposed CAT model outperforms the state-of-the-art approach and strong baselines.
• Score: 27.04964963480175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unstructured documents serving as external knowledge of the dialogues help to generate more informative responses. Previous research focused on knowledge selection (KS) in the document with dialogue. However, dialogue history that is not related to the current dialogue may introduce noise in the KS processing. In this paper, we propose a Compare Aggregate Transformer (CAT) to jointly denoise the dialogue context and aggregate the document information for response generation. We designed two different comparison mechanisms to reduce noise (before and during decoding). In addition, we propose two metrics for evaluating document utilization efficiency based on word overlap. Experimental results on the CMUDoG dataset show that the proposed CAT model outperforms the state-of-the-art approach and strong baselines.

Related papers

• Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation [13.322409682814827]
  We tackle the challenge of inadequate and costly training data for conversational question answering systems. In this paper, we propose a robust dialog synthesising method. We learn the segmentation of data for the dialog task instead of using segmenting at sentence boundaries.
  arXiv  Detail & Related papers  (2024-06-06T02:52:45Z)
• Evaluating Large Language Models for Document-grounded Response Generation in Information-Seeking Dialogues [17.41334279810008]
  We investigate the use of large language models (LLMs) like ChatGPT for document-grounded response generation in the context of information-seeking dialogues. For evaluation, we use the MultiDoc2Dial corpus of task-oriented dialogues in four social service domains. While both ChatGPT variants are more likely to include information not present in the relevant segments, possibly including a presence of hallucinations, they are rated higher than both the shared task winning system and human responses.
  arXiv  Detail & Related papers  (2023-09-21T07:28:03Z)
• Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation [51.22712675266523]
  Dialogue Topic (DTS) plays an essential role in a variety of dialogue modeling tasks. We propose a novel unsupervised DTS framework, which learns topic-aware utterance representations from unlabeled dialogue data.
  arXiv  Detail & Related papers  (2023-05-04T11:35:23Z)
• FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems [53.89014188309486]
  We present a flexible neural framework that can integrate contextual information from multiple channels. We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks.
  arXiv  Detail & Related papers  (2023-03-31T23:58:28Z)
• We've had this conversation before: A Novel Approach to Measuring Dialog Similarity [9.218829323265371]
  We propose a novel adaptation of the edit distance metric to the scenario of dialog similarity. Our approach takes into account various conversation aspects such as utterance semantics, conversation flow, and the participants.
  arXiv  Detail & Related papers  (2021-10-12T07:24:12Z)
• DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization [41.21012318918167]
  We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings. We demonstrate the effectiveness of our model on two document-grounded conversational datasets.
  arXiv  Detail & Related papers  (2021-09-10T05:40:37Z)
• Ranking Enhanced Dialogue Generation [77.8321855074999]
  How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures to model the history. This paper proposes a Ranking Enhanced Dialogue generation framework.
  arXiv  Detail & Related papers  (2020-08-13T01:49:56Z)
• 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)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)
• Dialogue-Based Relation Extraction [53.2896545819799]
  We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
  arXiv  Detail & Related papers  (2020-04-17T03:51:57Z)

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.