Annual Meeting of the Association for Computational Linguistics最新文献_第9页

Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data 不完全标注训练数据关系抽取的类自适应自训练

Annual Meeting of the Association for Computational Linguistics Pub Date : 2023-06-16 DOI: 10.48550/arXiv.2306.09697

Qingyu Tan, Lu Xu, Lidong Bing, H. Ng

{"title":"Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data","authors":"Qingyu Tan, Lu Xu, Lidong Bing, H. Ng","doi":"10.48550/arXiv.2306.09697","DOIUrl":"https://doi.org/10.48550/arXiv.2306.09697","url":null,"abstract":"Relation extraction (RE) aims to extract relations from sentences and documents. Existing relation extraction models typically rely on supervised machine learning. However, recent studies showed that many RE datasets are incompletely annotated. This is known as the false negative problem in which valid relations are falsely annotated as 'no_relation'. Models trained with such data inevitably make similar mistakes during the inference stage. Self-training has been proven effective in alleviating the false negative problem. However, traditional self-training is vulnerable to confirmation bias and exhibits poor performance in minority classes. To overcome this limitation, we proposed a novel class-adaptive re-sampling self-training framework. Specifically, we re-sampled the pseudo-labels for each class by precision and recall scores. Our re-sampling strategy favored the pseudo-labels of classes with high precision and low recall, which improved the overall recall without significantly compromising precision. We conducted experiments on document-level and biomedical relation extraction datasets, and the results showed that our proposed self-training framework consistently outperforms existing competitive methods on the Re-DocRED and ChemDisgene datasets when the training data are incompletely annotated. Our code is released at https://github.com/DAMO-NLP-SG/CAST.","PeriodicalId":352845,"journal":{"name":"Annual Meeting of the Association for Computational Linguistics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123285478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Differentiable Instruction Optimization for Cross-Task Generalization 跨任务泛化的可微指令优化

Annual Meeting of the Association for Computational Linguistics Pub Date : 2023-06-16 DOI: 10.48550/arXiv.2306.10098

Masaru Isonuma, Junichiro Mori, I. Sakata

引用次数: 0

Listener Model for the PhotoBook Referential Game with CLIPScores as Implicit Reference Chain 以CLIPScores为隐式参考链的PhotoBook参考博弈的听者模型

Annual Meeting of the Association for Computational Linguistics Pub Date : 2023-06-16 DOI: 10.48550/arXiv.2306.09607

Shih-Lun Wu, Yi-Hui Chou, Liang Li

引用次数: 0

AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets 八月:合成会话推荐数据集的自动生成替代研究

Annual Meeting of the Association for Computational Linguistics Pub Date : 2023-06-16 DOI: 10.48550/arXiv.2306.09631

Yu Lu, Junwei Bao, Zichen Ma, Xiaoguang Han, Youzheng Wu, Shuguang Cui, Xiaodong He

{"title":"AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets","authors":"Yu Lu, Junwei Bao, Zichen Ma, Xiaoguang Han, Youzheng Wu, Shuguang Cui, Xiaodong He","doi":"10.48550/arXiv.2306.09631","DOIUrl":"https://doi.org/10.48550/arXiv.2306.09631","url":null,"abstract":"High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design. Existing works contribute heavy human efforts to manually labeling or designing and extending recommender dialogue templates. However, they suffer from (i) the limited number of human annotators results in that datasets can hardly capture rich and large-scale cases in the real world, (ii) the limited experience and knowledge of annotators account for the uninformative corpus and inappropriate recommendations. In this paper, we propose a novel automatic dataset synthesis approach that can generate both large-scale and high-quality recommendation dialogues through a data2text generation process, where unstructured recommendation conversations are generated from structured graphs based on user-item information from the real world. In doing so, we comprehensively exploit: (i) rich personalized user profiles from traditional recommendation datasets, (ii) rich external knowledge from knowledge graphs, and (iii) the conversation ability contained in human-to-human conversational recommendation datasets. Extensive experiments validate the benefit brought by the automatically synthesized data under low-resource scenarios and demonstrate the promising potential to facilitate the development of a more effective conversational recommendation system.","PeriodicalId":352845,"journal":{"name":"Annual Meeting of the Association for Computational Linguistics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133956999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Opinion Tree Parsing for Aspect-based Sentiment Analysis 基于方面的情感分析的意见树解析

Annual Meeting of the Association for Computational Linguistics Pub Date : 2023-06-15 DOI: 10.48550/arXiv.2306.08925

Xiaoyi Bao, Xiaotong Jiang, Zhongqing Wang, Yue Zhang, Guodong Zhou

引用次数: 0

Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models 大型语言模型时间推理能力的标杆化与改进

Annual Meeting of the Association for Computational Linguistics Pub Date : 2023-06-15 DOI: 10.48550/arXiv.2306.08952

Qingyu Tan, H. Ng, Lidong Bing

引用次数: 6

Learning by Analogy: Diverse Questions Generation in Math Word Problem 类比学习:数学应用题中不同问题的生成

Annual Meeting of the Association for Computational Linguistics Pub Date : 2023-06-15 DOI: 10.48550/arXiv.2306.09064

Zihao Zhou, Maizhen Ning, Qiufeng Wang, Jie Yao, Wei Wang, Xiaowei Huang, Kaizhu Huang

{"title":"Learning by Analogy: Diverse Questions Generation in Math Word Problem","authors":"Zihao Zhou, Maizhen Ning, Qiufeng Wang, Jie Yao, Wei Wang, Xiaowei Huang, Kaizhu Huang","doi":"10.48550/arXiv.2306.09064","DOIUrl":"https://doi.org/10.48550/arXiv.2306.09064","url":null,"abstract":"Solving math word problem (MWP) with AI techniques has recently made great progress with the success of deep neural networks (DNN), but it is far from being solved. We argue that the ability of learning by analogy is essential for an MWP solver to better understand same problems which may typically be formulated in diverse ways. However most existing works exploit the shortcut learning to train MWP solvers simply based on samples with a single question. In lack of diverse questions, these methods merely learn shallow heuristics. In this paper, we make a first attempt to solve MWPs by generating diverse yet consistent questions/equations. Given a typical MWP including the scenario description, question, and equation (i.e., answer), we first generate multiple consistent equations via a group of heuristic rules. We then feed them to a question generator together with the scenario to obtain the corresponding diverse questions, forming a new MWP with a variety of questions and equations. Finally we engage a data filter to remove those unreasonable MWPs, keeping the high-quality augmented ones. To evaluate the ability of learning by analogy for an MWP solver, we generate a new MWP dataset (called DiverseMath23K) with diverse questions by extending the current benchmark Math23K. Extensive experimental results demonstrate that our proposed method can generate high-quality diverse questions with corresponding equations, further leading to performance improvement on Diverse-Math23K. The code and dataset is available at: https://github.com/zhouzihao501/DiverseMWP","PeriodicalId":352845,"journal":{"name":"Annual Meeting of the Association for Computational Linguistics","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117053429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Multi-target Backdoor Attacks for Code Pre-trained Models 代码预训练模型的多目标后门攻击

Annual Meeting of the Association for Computational Linguistics Pub Date : 2023-06-14 DOI: 10.48550/arXiv.2306.08350

Yanzhou Li, Shangqing Liu, Kangjie Chen, Xiaofei Xie, Tianwei Zhang, Yang Liu

引用次数: 3

Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models 多语言编码器和Seq2Seq模型的顺序预训练方法

Annual Meeting of the Association for Computational Linguistics Pub Date : 2023-06-14 DOI: 10.48550/arXiv.2306.08756

Saleh Soltan, Andrew Rosenbaum, Tobias Falke, Qin Lu, Anna Rumshisky, Wael Hamza

引用次数: 0

LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming LiveChat:一个从直播自动构建的大规模个性化对话数据集

Annual Meeting of the Association for Computational Linguistics Pub Date : 2023-06-14 DOI: 10.48550/arXiv.2306.08401

Jingsheng Gao, Yixin Lian, Ziyi Zhou, Yuzhuo Fu, Baoyuan Wang

{"title":"LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming","authors":"Jingsheng Gao, Yixin Lian, Ziyi Zhou, Yuzhuo Fu, Baoyuan Wang","doi":"10.48550/arXiv.2306.08401","DOIUrl":"https://doi.org/10.48550/arXiv.2306.08401","url":null,"abstract":"Open-domain dialogue systems have made promising progress in recent years. While the state-of-the-art dialogue agents are built upon large-scale social media data and large pre-trained models, there is no guarantee these agents could also perform well in fast-growing scenarios, such as live streaming, due to the bounded transferability of pre-trained models and biased distributions of public datasets from Reddit and Weibo, etc. To improve the essential capability of responding and establish a benchmark in the live open-domain scenario, we introduce the LiveChat dataset, composed of 1.33 million real-life Chinese dialogues with almost 3800 average sessions across 351 personas and fine-grained profiles for each persona. LiveChat is automatically constructed by processing numerous live videos on the Internet and naturally falls within the scope of multi-party conversations, where the issues of Who says What to Whom should be considered. Therefore, we target two critical tasks of response modeling and addressee recognition and propose retrieval-based baselines grounded on advanced techniques. Experimental results have validated the positive effects of leveraging persona profiles and larger average sessions per persona. In addition, we also benchmark the transferability of advanced generation-based models on LiveChat and pose some future directions for current challenges.","PeriodicalId":352845,"journal":{"name":"Annual Meeting of the Association for Computational Linguistics","volume":"344 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123351644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1