Knowledge Diffusion for Neural Dialogue Generation

Liu, Shuman; Chen, Hongshen; Zhang, Ren; Feng, Yang; Li, Qun; Yin, Dawei

doi:10.18653/v1/p18-1138

Cited by 166 publications

(124 citation statements)

References 19 publications

Supporting

Mentioning

123

Contrasting

Order By: Relevance

“…This is opposite to the conclusion achieved by Liu et al [23]. One possible reason is that the tasks between ours and Liu et al's [23] are different, i.e., Liu et al aim at producing texts based on code, while we focus on generating texts for dialogues and modeling code is different from modeling dialogue texts [53], [54]. The higher BLEU-4 score of the proposed RRGen model than that of the NMT model explains that the response generated by the RRGen model is more similar to developers' response than the response generated by the NMT model.…”

Section: Evaluation Using An Automatic Metriccontrasting

confidence: 68%

Automating App Review Response Generation

Gao

Zeng

Xia

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

View full text Add to dashboard Cite

Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app. For example, Hassan et al. found that responding to a review increases the chances of a user updating their given rating by up to six times compared to not responding. To alleviate the labor burden in replying to the bulk of user reviews, developers usually adopt a template-based strategy where the templates can express appreciation for using the app or mention the company email address for users to follow up. However, reading a large number of user reviews every day is not an easy task for developers. Thus, there is a need for more automation to help developers respond to user reviews.Addressing the aforementioned need, in this work we propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses. RRGen explicitly incorporates review attributes, such as user rating and review length, and learns the relations between reviews and corresponding responses in a supervised way from the available training data. Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU-4 (an accuracy measure that is widely used to evaluate dialogue response generation systems). Qualitative analysis also confirms the effectiveness of RRGen in generating relevant and accurate responses.

show abstract

Section: Evaluation Using An Automatic Metriccontrasting

confidence: 68%

Automating App Review Response Generation

Gao

Zeng

Xia

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

View full text Add to dashboard Cite

show abstract

“…This is part of the semantics challenge we discussed in Section 3. Since natural language understanding in open domains is extremely challenging, knowledge grounding provides to some degree the ability of understanding language in dialog context, as shown in several preliminary studies [47,130,135].…”

Section: Discussion and Future Trendsmentioning

confidence: 99%

Challenges in Building Intelligent Open-domain Dialog Systems

Huang

Zhu

Gao

2020

ACM Trans. Inf. Syst.

201

122

View full text Add to dashboard Cite

There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI [17]. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This paper reviews the recent works on neural approaches that are devoted to addressing three challenges in developing such systems: semantics, consistency, and interactiveness. Semantics requires a dialog system to not only understand the content of the dialog but also identify user's social needs during the conversation. Consistency requires the system to demonstrate a consistent personality to win users trust and gain their long-term confidence. Interactiveness refers to the system's ability to generate interpersonal responses to achieve particular social goals such as entertainment, conforming, and task completion.The works we select to present here is based on our unique views and are by no means complete. Nevertheless, we hope that the discussion will inspire new research in developing more intelligent dialog systems.

show abstract

“…Recent works on KB-based end-to-end QA systems such as (Yin et al, 2015;He et al, 2017a;Liu et al, 2018a) generate full-length answers with neural pointer networks (Gülçehre et al, 2016;Vinyals et al, 2015;He et al, 2017b) after retrieving facts from a knowledge base (KB). Dialogue systems such as (Liu et al, 2018b;Lian et al, 2019) extract information from knowledge bases to formulate a response. Systems such as (Fu and Feng, 2018) uses KB based key-value memory after extracting information from documents or external KBs.…”

Section: Related Workmentioning

confidence: 99%

Answering Naturally : Factoid to Full length Answer Generation

Pal¹,

Shrivastava²,

Bhat³

2019

Proceedings of the 2nd Workshop on New Frontiers in Summarization

View full text Add to dashboard Cite

In recent years, the task of Question Answering over passages, also pitched as a reading comprehension, has evolved into a very active research area. A reading comprehension system extracts a span of text, comprising of named entities, dates, small phrases, etc., which serve as the answer to a given question. However, these spans of text would result in an unnatural reading experience in a conversational system. Usually, dialogue systems solve this issue by using template-based language generation. These systems, though adequate for a domain specific task, are too restrictive and predefined for a domain independent system. In order to present the user with a more conversational experience, we propose a pointer generator based full-length answer generator which can be used with most QA systems. Our system generates a full-length answer given a question and the extracted factoid/span answer without relying on the passage from where the answer was extracted. We also present a dataset of 315,000 question, factoid answer and full-length answer triples. We have evaluated our system using ROUGE-1,2,L and BLEU and achieved 74.05 BLEU score and 86.25 Rogue-L score.

show abstract

Knowledge Diffusion for Neural Dialogue Generation

Cited by 166 publications

References 19 publications

Automating App Review Response Generation

Automating App Review Response Generation

Challenges in Building Intelligent Open-domain Dialog Systems

Answering Naturally : Factoid to Full length Answer Generation

Contact Info

Product

Resources

About