探索对话响应选择的密集检索

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Tian Lan, Deng Cai, Yan Wang, Yixuan Su, Heyan Huang, Xian-Ling Mao
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引用次数: 11

摘要

深度学习的最新进展不断提高了对话响应选择的准确性。然而,在现实场景中,高计算成本迫使现有的对话响应选择模型仅对少量候选对象进行排名,由粗粒度模型召回,从而排除了许多高质量的候选对象。为了克服这一问题,我们提出了一种新颖有效的响应选择模型和一套量身定制的学习策略来有效地训练它。该模型由密集检索模块和交互层组成,可以直接从大量语料库中选择合适的响应。我们对广泛使用的基准进行重新排序和全排序评估,以评估我们提出的模型。广泛的实验结果表明,我们提出的模型在重新排序和全排序评估上都明显优于最先进的基线。此外,人工评价结果表明,使用非并行语料库扩大候选语料库可以进一步提高响应质量。此外,我们还发布了经过仔细注释的高质量基准,以便更准确地评估对话响应选择。所有源代码、数据集、模型参数和其他相关资源都已公开。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Dense Retrieval for Dialogue Response Selection
Recent progress in deep learning has continuously improved the accuracy of dialogue response selection. However, in real-world scenarios, the high computation cost forces existing dialogue response selection models to rank only a small number of candidates, recalled by a coarse-grained model, precluding many high-quality candidates. To overcome this problem, we present a novel and efficient response selection model and a set of tailor-designed learning strategies to train it effectively. The proposed model consists of a dense retrieval module and an interaction layer, which could directly select the proper response from a large corpus. We conduct re-rank and full-rank evaluations on widely used benchmarks to evaluate our proposed model. Extensive experimental results demonstrate that our proposed model notably outperforms the state-of-the-art baselines on both re-rank and full-rank evaluations. Moreover, human evaluation results show that the response quality could be improved further by enlarging the candidate pool with nonparallel corpora. In addition, we also release high-quality benchmarks that are carefully annotated for more accurate dialogue response selection evaluation. All source codes, datasets, model parameters, and other related resources have been publicly available.
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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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