CASIA OpenIR  > 综合信息系统研究中心  > 视知觉融合及其应用
Learning to Correct Erroneous Words for Document Grounded Conversations
Junyan Qiu1,2; Haitao Wang3; Yiping Yang2
2023-06
Conference NameInternational Conference on Software and Computer Applications
Conference Date2023.02.23-2023.02.25
Conference PlaceKuantan, Malaysia
PublisherACM
Abstract

Document grounded conversation (DGC) aims to generate informative responses when talking about a document. It is normally formulated as a sequence-to-sequence (Seq2seq) learning problem, which directly maps source sequences, i.e., the context and background documents, to the target sequence, i.e., the response. These responses are normally used as the final output without further polishing, which may suffer from the global information loss owing to the auto-regression paradigm. To tackle this problem, some researches designed two-pass generation to improve the quality of responses. However, these approaches lack the capability of distinguishing inappropriate words in the first pass, which may maintain the erroneous words while rewrite the correct ones. In this paper, we design a scheduled error correction network (SECN) with multiple generation passes to explicitly locate and rewrite the erroneous words in previous passes. Specifically, a discriminator is employed to distinguish erroneous words which are further revised by a refiner. Moreover, we also apply curriculum learning with reasonable learning schedule to train our model from easy to hard conversations, where the complexity is measured by the number of decoding passes. We conduct comprehensive experiments on a public document grounded conversation dataset, Wizard-of-Wikipedia, and the results demonstrate significant promotions over several strong benchmarks.

KeywordDeep Learning Natural Language Generation Dialogue System Curriculum Learning
MOST Discipline Catalogue工学::计算机科学与技术(可授工学、理学学位)
Indexed ByEI
Language英语
IS Representative Paper
Sub direction classification自然语言处理
planning direction of the national heavy laboratory语音语言处理
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57412
Collection综合信息系统研究中心_视知觉融合及其应用
Corresponding AuthorJunyan Qiu
Affiliation1.University of Chinese Academy of Sciences
2.Institute of Automation, Chinese Academy of Sciences
3.Meituan
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Junyan Qiu,Haitao Wang,Yiping Yang. Learning to Correct Erroneous Words for Document Grounded Conversations[C]:ACM,2023.
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