一句话：针对软件工程交流中情感识别工具跨渠道性能下降与标注成本高的问题，提出用数据增强改进情感识别。
情感（如喜悦、愤怒）在软件工程日常活动中普遍存在，并被认为是工作效率（如缺陷修复效率）的重要指标。已有研究表明，直接将通用情感分类工具用于软件工程语料效果不佳；即便在软件工程领域内，工具在一个交流渠道上训练后迁移到另一渠道（如 StackOverflow 与 GitHub 评论）时性能也会显著下降。由于人工标注大规模真实标签数据成本高昂，按渠道重新训练工具十分费力。该论文（标题表明）研究使用数据增强来改进软件工程交流中的情感识别，以缓解跨渠道性能下降并降低标注需求；摘要未给出具体增强方法与实验结果的细节。
Emotions (e.g., Joy, Anger) are prevalent in daily software engineering (SE) activities, and are known to be significant indicators of work productivity (e.g., bug fixing efficiency). Recent studies have shown that directly applying general purpose emotion classification tools to SE corpora is not effective. Even within the SE domain, tool performance degrades significantly when trained on one communication channel and evaluated on another (e.g, StackOverflow vs. GitHub comments). Retraining a tool with channel-specific data takes significant effort since manually annotating a large dataset of ground truth data is expensive.
@inproceedings{paperbot7276,
title = {Data Augmentation for Improving Emotion Recognition in Software Engineering Communication},
author = {Mia Mohammad Imran and Yashasvi Jain and Preetha Chatterjee and Kostadin Damevski},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3556925}
}