A Syntactic Neural Model for General-Purpose Code Generation

Yin, Pengcheng; Neubig, Graham

doi:10.18653/v1/p17-1041

Cited by 467 publications

(511 citation statements)

References 35 publications

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“…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: 75%

Automating App Review Response Generation

Gao

Zeng

Xia

et al. 2019

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

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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.

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Section: Evaluation Using An Automatic Metriccontrasting

confidence: 75%

Automating App Review Response Generation

Gao

Zeng

Xia

et al. 2019

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

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show abstract

“…Neural semantic parsing approaches have been gaining rising attention in recent years, eschewing the need for extensive feature engineering (Jia and Liang, 2016;Ling et al, 2016;Xiao et al, 2016). Some efforts have been made to utilize the syntax of logical forms (Rabinovich et al, 2017;Krishnamurthy et al, 2017;Cheng et al, 2017;Yin and Neubig, 2017). For example, Dong and Lapata (2016) and Alvarez-Melis and Jaakkola (2017) leverage an attention-based encoder-decoder framework to translate a natural language question to tree-structured logical form.…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base

Shen¹,

Geng²,

Qin³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

101

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We consider the problem of conversational question answering over a large-scale knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent neural semantic parsing based approaches usually decompose the task into several subtasks and then solve them sequentially, which leads to following issues: 1) errors in earlier subtasks will be propagated and negatively affect downstream ones; and 2) each subtask cannot naturally share supervision signals with others. To tackle these issues, we propose an innovative multi-task learning framework where a pointer-equipped semantic parsing model is designed to resolve coreference in conversations, and naturally empower joint learning with a novel type-aware entity detection model. The proposed framework thus enables shared supervisions and alleviates the effect of error propagation. Experiments on a large-scale conversational question answering dataset containing 1.6M question answering pairs over 12.8M entities show that the proposed framework improves overall F1 score from 67% to 79% compared with previous state-of-the-art work.

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“…Particularly for IFTTT rules, Quirk et al collected 114,408 IF-THEN rules and their natural-language descriptions from the IFTTT website, and demonstrated the possibility of producing IF-THEN rules based on corresponding descriptive text (Quirk et al, 2015). Several follow-up work that proposed different approaches such as attention-enhanced encoder-decoder model (Dong and Lapata, 2016), using latent attention (Liu et al, 2016), or syntactic neural model (Yin and Neubig, 2017) further improved the accuracy of IFTTT rule generation. Under the context of conversational assistance, Chaurasia et al created an automated dialog system that generates IFTTT rules by having a conversation with users (Chaurasia and Mooney, 2017).…”

Section: Automatic If-then Rules Generationmentioning

confidence: 99%

InstructableCrowd: Creating IF-THEN Rules for Smartphones via Conversations with the Crowd

Huang¹,

Azaria²,

Romero³

et al. 2019

HCj

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Natural language interfaces have become a common part of modern digital life. Chatbots utilize text-based conversations to communicate with users; personal assistants on smartphones such as Google Assistant take direct speech commands from their users; and speech-controlled devices such as Amazon Echo use voice as their only input mode. In this paper, we introduce InstructableCrowd, a crowd-powered system that allows users to program their devices via conversation. The user verbally expresses a problem to the system, in which a group of crowd workers collectively respond and program relevant multi-part IF-THEN rules to help the user. The IF-THEN rules generated by InstructableCrowd connect relevant sensor combinations (e.g., location, weather, device acceleration, etc.) to useful effectors (e.g., text messages, device alarms, etc.). Our study showed that non-programmers can use the conversational interface of InstructableCrowd to create IF-THEN rules that have similar quality compared with the rules created manually. InstructableCrowd generally illustrates how users may converse with their devices, not only to trigger simple voice commands, but also to personalize their increasingly powerful and complicated devices.arXiv:1909.05725v1 [cs.HC]

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A Syntactic Neural Model for General-Purpose Code Generation

Cited by 467 publications

References 35 publications

Automating App Review Response Generation

Automating App Review Response Generation

Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base

InstructableCrowd: Creating IF-THEN Rules for Smartphones via Conversations with the Crowd

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