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SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts 任务7:识别教学文本中隐含和未明确短语的合理解释
International Workshop on Semantic Evaluation Pub Date : 2023-09-21 DOI: 10.18653/v1/2022.semeval-1.146
Michael Roth, Talita Anthonio, Anna Sauer
{"title":"SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts","authors":"Michael Roth, Talita Anthonio, Anna Sauer","doi":"10.18653/v1/2022.semeval-1.146","DOIUrl":"https://doi.org/10.18653/v1/2022.semeval-1.146","url":null,"abstract":"We describe SemEval-2022 Task 7, a shared task on rating the plausibility of clarifications in instructional texts. The dataset for this task consists of manually clarified how-to guides for which we generated alternative clarifications and collected human plausibility judgements. The task of participating systems was to automatically determine the plausibility of a clarification in the respective context. In total, 21 participants took part in this task, with the best system achieving an accuracy of 68.9%. This report summarizes the results and findings from 8 teams and their system descriptions. Finally, we show in an additional evaluation that predictions by the top participating team make it possible to identify contexts with multiple plausible clarifications with an accuracy of 75.2%.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131129656","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: 12
Mao-Zedong at SemEval-2023 Task 4: Label Represention Multi-Head Attention Model with Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification 任务4:基于对比学习增强最近邻机制的标签表示多头注意模型在多标签文本分类中的应用
International Workshop on Semantic Evaluation Pub Date : 2023-07-11 DOI: 10.48550/arXiv.2307.05174
Che Zhang, Ping'an Liu, Zhenyang Xiao, Haojun Fei
{"title":"Mao-Zedong at SemEval-2023 Task 4: Label Represention Multi-Head Attention Model with Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification","authors":"Che Zhang, Ping'an Liu, Zhenyang Xiao, Haojun Fei","doi":"10.48550/arXiv.2307.05174","DOIUrl":"https://doi.org/10.48550/arXiv.2307.05174","url":null,"abstract":"This is our system description paper for ValueEval task.The title is:Mao-Zedong At SemEval-2023 Task 4: Label Represention Multi-Head Attention Model With Contrastive Learning-Enhanced Nearest Neighbor Mechanism For Multi-Label Text Classification,and the author is Che Zhang and Pingan Liu and ZhenyangXiao and HaojunFei. In this paper, we propose a model that combinesthe label-specific attention network with the contrastive learning-enhanced nearest neighbor mechanism.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127551853","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
UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation 任务1:多语言视觉词义消歧的语境增强与翻译
International Workshop on Semantic Evaluation Pub Date : 2023-06-24 DOI: 10.48550/arXiv.2306.14067
Michael Ogezi, B. Hauer, Talgat Omarov, Ning Shi, Grzegorz Kondrak
{"title":"UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation","authors":"Michael Ogezi, B. Hauer, Talgat Omarov, Ning Shi, Grzegorz Kondrak","doi":"10.48550/arXiv.2306.14067","DOIUrl":"https://doi.org/10.48550/arXiv.2306.14067","url":null,"abstract":"We describe the systems of the University of Alberta team for the SemEval-2023 Visual Word Sense Disambiguation (V-WSD) Task. We present a novel algorithm that leverages glosses retrieved from BabelNet, in combination with text and image encoders. Furthermore, we compare language-specific encoders against the application of English encoders to translated texts. As the contexts given in the task datasets are extremely short, we also experiment with augmenting these contexts with descriptions generated by a language model. This yields substantial improvements in accuracy. We describe and evaluate additional V-WSD methods which use image generation and text-conditioned image segmentation. Some of our experimental results exceed those of our official submissions on the test set. Our code is publicly available at https://github.com/UAlberta-NLP/v-wsd.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133233771","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
LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for Sexism Detection and Classification 任务10:性别歧视检测和分类的预训练和多任务学习
International Workshop on Semantic Evaluation Pub Date : 2023-06-08 DOI: 10.48550/arXiv.2306.05075
K. Chernyshev, E. Garanina, Duygu Bayram, Qiankun Zheng, Lukas Edman
{"title":"LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for Sexism Detection and Classification","authors":"K. Chernyshev, E. Garanina, Duygu Bayram, Qiankun Zheng, Lukas Edman","doi":"10.48550/arXiv.2306.05075","DOIUrl":"https://doi.org/10.48550/arXiv.2306.05075","url":null,"abstract":"Misogyny and sexism are growing problems in social media. Advances have been made in online sexism detection but the systems are often uninterpretable. SemEval-2023 Task 10 on Explainable Detection of Online Sexism aims at increasing explainability of the sexism detection, and our team participated in all the proposed subtasks. Our system is based on further domain-adaptive pre-training. Building on the Transformer-based models with the domain adaptation, we compare fine-tuning with multi-task learning and show that each subtask requires a different system configuration. In our experiments, multi-task learning performs on par with standard fine-tuning for sexism detection and noticeably better for coarse-grained sexism classification, while fine-tuning is preferable for fine-grained classification.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122263819","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: 0
CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions 基于标签描述的增量微调和多任务学习的性别歧视检测
International Workshop on Semantic Evaluation Pub Date : 2023-06-06 DOI: 10.48550/arXiv.2306.03907
Janis Goldzycher
{"title":"CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions","authors":"Janis Goldzycher","doi":"10.48550/arXiv.2306.03907","DOIUrl":"https://doi.org/10.48550/arXiv.2306.03907","url":null,"abstract":"The widespread popularity of social media has led to an increase in hateful, abusive, and sexist language, motivating methods for the automatic detection of such phenomena. The goal of the SemEval shared task Towards Explainable Detection of Online Sexism (EDOS 2023) is to detect sexism in English social media posts (subtask A), and to categorize such posts into four coarse-grained sexism categories (subtask B), and eleven fine-grained subcategories (subtask C). In this paper, we present our submitted systems for all three subtasks, based on a multi-task model that has been fine-tuned on a range of related tasks and datasets before being fine-tuned on the specific EDOS subtasks. We implement multi-task learning by formulating each task as binary pairwise text classification, where the dataset and label descriptions are given along with the input text. The results show clear improvements over a fine-tuned DeBERTa-V3 serving as a baseline leading to F1-scores of 85.9% in subtask A (rank 13/84), 64.8% in subtask B (rank 19/69), and 44.9% in subtask C (26/63).","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127497176","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: 0
THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval SemEval-2023任务7:基于文本蕴意和证据检索的多粒度系统
International Workshop on Semantic Evaluation Pub Date : 2023-06-02 DOI: 10.48550/arXiv.2306.01245
Yuxuan Zhou, Ziyun Jin, Meiwei Li, Miao Li, Xien Liu, Xinxin You, Ji Wu
{"title":"THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval","authors":"Yuxuan Zhou, Ziyun Jin, Meiwei Li, Miao Li, Xien Liu, Xinxin You, Ji Wu","doi":"10.48550/arXiv.2306.01245","DOIUrl":"https://doi.org/10.48550/arXiv.2306.01245","url":null,"abstract":"The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports (CTRs) and retrieve the corresponding evidence supporting the justification. This task poses a significant challenge, as verifying hypotheses in the NLI4CT task requires the integration of multiple pieces of evidence from one or two CTR(s) and the application of diverse levels of reasoning, including textual and numerical. To address these problems, we present a multi-granularity system for CTR-based textual entailment and evidence retrieval in this paper. Specifically, we construct a Multi-granularity Inference Network (MGNet) that exploits sentence-level and token-level encoding to handle both textual entailment and evidence retrieval tasks. Moreover, we enhance the numerical inference capability of the system by leveraging a T5-based model, SciFive, which is pre-trained on the medical corpus. Model ensembling and a joint inference method are further utilized in the system to increase the stability and consistency of inference. The system achieves f1-scores of 0.856 and 0.853 on textual entailment and evidence retrieval tasks, resulting in the best performance on both subtasks. The experimental results corroborate the effectiveness of our proposed method.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126389546","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
CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identification 任务8:利用反事实数据增强减轻因果索赔识别中的类不平衡
International Workshop on Semantic Evaluation Pub Date : 2023-06-01 DOI: 10.48550/arXiv.2306.00346
Akbar Karimi, Lucie Flek
{"title":"CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identification","authors":"Akbar Karimi, Lucie Flek","doi":"10.48550/arXiv.2306.00346","DOIUrl":"https://doi.org/10.48550/arXiv.2306.00346","url":null,"abstract":"Class imbalance problem can cause machine learning models to produce an undesirable performance on the minority class as well as the whole dataset. Using data augmentation techniques to increase the number of samples is one way to tackle this problem. We introduce a novel counterfactual data augmentation by verb replacement for the identification of medical claims. In addition, we investigate the impact of this method and compare it with 3 other data augmentation techniques, showing that the proposed method can result in significant (relative) improvement on the minority class.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126145580","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
UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis 任务12:在低资源情感分析中增强多语言BERT的泛化
International Workshop on Semantic Evaluation Pub Date : 2023-06-01 DOI: 10.48550/arXiv.2306.01093
Dou Hu, Lingwei Wei, Yaxin Liu, Wei Zhou, Songlin Hu
{"title":"UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis","authors":"Dou Hu, Lingwei Wei, Yaxin Liu, Wei Zhou, Songlin Hu","doi":"10.48550/arXiv.2306.01093","DOIUrl":"https://doi.org/10.48550/arXiv.2306.01093","url":null,"abstract":"This paper describes our system designed for SemEval-2023 Task 12: Sentiment analysis for African languages. The challenge faced by this task is the scarcity of labeled data and linguistic resources in low-resource settings. To alleviate these, we propose a generalized multilingual system SACL-XLMR for sentiment analysis on low-resource languages. Specifically, we design a lexicon-based multilingual BERT to facilitate language adaptation and sentiment-aware representation learning. Besides, we apply a supervised adversarial contrastive learning technique to learn sentiment-spread structured representations and enhance model generalization. Our system achieved competitive results, largely outperforming baselines on both multilingual and zero-shot sentiment classification subtasks. Notably, the system obtained the 1st rank on the zero-shot classification subtask in the official ranking. Extensive experiments demonstrate the effectiveness of our system.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127734984","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
Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models Adam-Smith在SemEval-2023的任务4:在基于变压器的模型集合的争论中发现人类的价值
International Workshop on Semantic Evaluation Pub Date : 2023-05-15 DOI: 10.48550/arXiv.2305.08625
Daniel Schroter, D. Dementieva, G. Groh
{"title":"Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models","authors":"Daniel Schroter, D. Dementieva, G. Groh","doi":"10.48550/arXiv.2305.08625","DOIUrl":"https://doi.org/10.48550/arXiv.2305.08625","url":null,"abstract":"This paper presents the best-performing approach alias “Adam Smith” for the SemEval-2023 Task 4: “Identification of Human Values behind Arguments”. The goal of the task was to create systems that automatically identify the values within textual arguments. We train transformer-based models until they reach their loss minimum or f1-score maximum. Ensembling the models by selecting one global decision threshold that maximizes the f1-score leads to the best-performing system in the competition. Ensembling based on stacking with logistic regressions shows the best performance on an additional dataset provided to evaluate the robustness (“Nahj al-Balagha”). Apart from outlining the submitted system, we demonstrate that the use of the large ensemble model is not necessary and that the system size can be significantly reduced.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127155113","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
IUST_NLP at SemEval-2023 Task 10: Explainable Detecting Sexism with Transformers and Task-adaptive Pretraining 任务10:基于变形器和任务自适应预训练的可解释的性别歧视检测
International Workshop on Semantic Evaluation Pub Date : 2023-05-11 DOI: 10.48550/arXiv.2305.06892
Hadi Mahmoudi
{"title":"IUST_NLP at SemEval-2023 Task 10: Explainable Detecting Sexism with Transformers and Task-adaptive Pretraining","authors":"Hadi Mahmoudi","doi":"10.48550/arXiv.2305.06892","DOIUrl":"https://doi.org/10.48550/arXiv.2305.06892","url":null,"abstract":"This paper describes our system on SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This work aims to design an automatic system for detecting and classifying sexist content in online spaces. We propose a set of transformer-based pre-trained models with task-adaptive pretraining and ensemble learning. The main contributions of our system include analyzing the performance of different transformer-based pre-trained models and combining these models, as well as providing an efficient method using large amounts of unlabeled data for model adaptive pretraining. We have also explored several other strategies. On the test dataset, our system achieves F1-scores of 83%, 64%, and 47% on subtasks A, B, and C, respectively.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123836260","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: 0
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