Yiming Huang, Zhenbo Shi, Xin-Cheng Wen, Jichuan Zeng, Cuiyun Gao, Peiyi Han, Chuanyi Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Code summaries help developers comprehend programs and reduce their time to infer the program functionalities during software maintenance. Recent efforts resort to deep learning techniques such as sequence-to-sequence models for generating accurate code summaries, among which Transformer-based approaches have achieved promising performance. However, effectively integrating the code structure information into the Transformer is under-explored in this task domain. In this article, we propose a novel approach named SG-Trans to incorporate code structural properties into Transformer. Specifically, we inject the local symbolic information (e.g., code tokens and statements) and global syntactic structure (e.g., dataflow graph) into the self-attention module of Transformer as inductive bias. To further capture the hierarchical characteristics of code, the local information and global structure are designed to distribute in the attention heads of lower layers and high layers of Transformer. Extensive evaluation shows the superior performance of SG-Trans over the state-of-the-art approaches. Compared with the best-performing baseline, SG-Trans still improves 1.4% and 2.0% on two benchmark datasets, respectively, in terms of METEOR score, a metric widely used for measuring generation quality.

Millions of mobile apps are available in app stores, such as Apple's App Store and Google Play. For a mobile app, it would be increasingly challenging to stand out from the enormous competitors and become prevalent among users. Good user experience and well-designed functionalities are the keys to a successful app. To achieve this, popular apps usually schedule their updates frequently. If we can capture the critical app issues faced by users in a timely and accurate manner, developers can make timely updates, and good user experience can be ensured. There exist prior studies on analyzing reviews for detecting emerging app issues. These studies are usually based on topic modeling or clustering techniques. However, the short-length characteristics and sentiment of user reviews have not been considered. In this paper, we propose a novel emerging issue detection approach named MERIT to take into consideration the two aforementioned characteristics. Specifically, we propose an Adaptive Online Biterm Sentiment-Topic (AOBST) model for jointly modeling topics and corresponding sentiments that takes into consideration app versions. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version, and automatically interpret the meaning of the topics with most relevant phrases and sentences. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT in identifying emerging app issues, improving the state-of-the-art method by 22.3 percent in terms of F1-score. In terms of efficiency, MERIT can return results within acceptable time.

In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the increasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful — topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations.

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.

This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse). 1 Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier. Our data sets and code are available at: http://github.com/zengjichuan/Topic_Disc .

Many classification models work poorly on short texts due to data sparsity.To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels.Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner.Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.* This work was mainly conducted when Jichuan Zeng was an intern in Tencent AI Lab.† Jing Li is the corresponding author.Training instances R1: [SuperBowl] I'll do anything to see the Steelers win.R2: [New.Music.Live] Please give wristbands, she have major Bieber Fever.

Detecting emerging issues (e.g., new bugs) timely and precisely is crucial for developers to update their apps. App reviews provide an opportunity to proactively collect user complaints and promptly improve apps' user experience, in terms of bug fixing and feature refinement. However, the tremendous quantities of reviews and noise words (e.g., misspelled words) increase the difficulties in accurately identifying newly-appearing app issues. In this paper, we propose a novel and automated framework IDEA, which aims to IDentify Emerging App issues effectively based on online review analysis. We evaluate IDEA on six popular apps from Google Play and Apple's App Store, employing the official app changelogs as our ground truth. Experiment results demonstrate the effectiveness of IDEA in identifying emerging app issues. Feedback from engineers and product managers shows that 88.9% of them think that the identified issues can facilitate app development in practice. Moreover, we have successfully applied IDEA to several products of Tencent, which serve hundreds of millions of users.

App reviews play an essential role for users to convey their feedback about using the app. The critical information contained in app reviews can assist app developers for maintaining and updating mobile apps. However, the noisy nature and large-quantity of daily generated app reviews make it difficult to understand essential information carried in app reviews. Several prior studies have proposed methods that can automatically classify or cluster user reviews into a few app topics (e.g., security). These methods usually act on a static collection of user reviews. However, due to the dynamic nature of user feedback (i.e., reviews keep coming as new users register or new app versions being released) and multiple analysis dimensions (e.g., review quantity and user rating), developers still need to spend substantial effort in extracting contrastive information that can only be teased out by comparing data from multiple time periods or analysis dimensions. This is needed to answer questions such as: what kind of issues users are experiencing most? is there an unexpected rise in a particular kind of issue? etc. To address this need, in this paper, we introduce INFAR, a tool that automatically extracts INsights From App Reviews across time periods and analysis dimensions, and presents them in natural language supported by an interactive chart. The insights INFAR extracts include several perspectives: (1) salient topics (i.e., issue topics with significantly lower ratings), (2) abnormal topics (i.e., issue topics that experience a rapid rise in volume during a time period), (3) correlations between two topics, and (4) causal factors to rating or review quantity changes. To evaluate our tool, we conduct an empirical evaluation by involving six popular apps and 12 industrial practitioners, and 92% (11/12) of them approve the practical usefulness of the insights summarized by INFAR.