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IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection 任务5:有毒跨度检测的半监督学习和骰子损失
International Workshop on Semantic Evaluation Pub Date : 2021-04-04 DOI: 10.18653/v1/2021.semeval-1.24
Archit Bansal, Abhay Kaushik, Ashutosh Modi
{"title":"IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection","authors":"Archit Bansal, Abhay Kaushik, Ashutosh Modi","doi":"10.18653/v1/2021.semeval-1.24","DOIUrl":"https://doi.org/10.18653/v1/2021.semeval-1.24","url":null,"abstract":"In this work, we present our approach and findings for SemEval-2021 Task 5 - Toxic Spans Detection. The task’s main aim was to identify spans to which a given text’s toxicity could be attributed. The task is challenging mainly due to two constraints: the small training dataset and imbalanced class distribution. Our paper investigates two techniques, semi-supervised learning and learning with Self-Adjusting Dice Loss, for tackling these challenges. Our submitted system (ranked ninth on the leader board) consisted of an ensemble of various pre-trained Transformer Language Models trained using either of the above-proposed techniques.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128477672","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
Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness 任务7:量化幽默和冒犯性的大型语言模型
International Workshop on Semantic Evaluation Pub Date : 2021-04-02 DOI: 10.18653/v1/2021.semeval-1.36
Aishwarya Gupta, Avik Pal, Bholeshwar Khurana, Lakshay Tyagi, Ashutosh Modi
{"title":"Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness","authors":"Aishwarya Gupta, Avik Pal, Bholeshwar Khurana, Lakshay Tyagi, Ashutosh Modi","doi":"10.18653/v1/2021.semeval-1.36","DOIUrl":"https://doi.org/10.18653/v1/2021.semeval-1.36","url":null,"abstract":"Humor and Offense are highly subjective due to multiple word senses, cultural knowledge, and pragmatic competence. Hence, accurately detecting humorous and offensive texts has several compelling use cases in Recommendation Systems and Personalized Content Moderation. However, due to the lack of an extensive labeled dataset, most prior works in this domain haven’t explored large neural models for subjective humor understanding. This paper explores whether large neural models and their ensembles can capture the intricacies associated with humor/offense detection and rating. Our experiments on the SemEval-2021 Task 7: HaHackathon show that we can develop reasonable humor and offense detection systems with such models. Our models are ranked 3rd in subtask 1b and consistently ranked around the top 33% of the leaderboard for the remaining subtasks.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121627329","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: 4
TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training 任务9:中级预训练的表推理
International Workshop on Semantic Evaluation Pub Date : 2021-04-02 DOI: 10.18653/v1/2021.semeval-1.51
Thomas Müller, Julian Martin Eisenschlos, Syrine Krichene
{"title":"TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training","authors":"Thomas Müller, Julian Martin Eisenschlos, Syrine Krichene","doi":"10.18653/v1/2021.semeval-1.51","DOIUrl":"https://doi.org/10.18653/v1/2021.semeval-1.51","url":null,"abstract":"We present the TAPAS contribution to the Shared Task on Statement Verification and Evidence Finding with Tables (SemEval 2021 Task 9, Wang et al. (2021)). SEM TAB FACT Task A is a classification task of recognizing if a statement is entailed, neutral or refuted by the content of a given table. We adopt the binary TAPAS model of Eisenschlos et al. (2020) to this task. We learn two binary classification models: A first model to predict if a statement is neutral or non-neutral and a second one to predict if it is entailed or refuted. As the shared task training set contains only entailed or refuted examples, we generate artificial neutral examples to train the first model. Both models are pre-trained using a MASKLM objective, intermediate counter-factual and synthetic data (Eisenschlos et al., 2020) and TABFACT (Chen et al., 2020), a large table entailment dataset. We find that the artificial neutral examples are somewhat effective at training the first model, achieving 68.03 test F1 versus the 60.47 of a majority baseline. For the second stage, we find that the pre-training on the intermediate data and TABFACT improves the results over MASKLM pre-training (68.03 vs 57.01).","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126256469","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: 13
Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction 任务6:用于定义抽取的概念和关系的联合抽取
International Workshop on Semantic Evaluation Pub Date : 2021-03-31 DOI: 10.18653/v1/2020.semeval-1.92
Marc Hübner, Christoph Alt, Robert Schwarzenberg, Leonhard Hennig
{"title":"Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction","authors":"Marc Hübner, Christoph Alt, Robert Schwarzenberg, Leonhard Hennig","doi":"10.18653/v1/2020.semeval-1.92","DOIUrl":"https://doi.org/10.18653/v1/2020.semeval-1.92","url":null,"abstract":"Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"80 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124267919","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
UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words Model UniParma在SemEval-2021任务5:使用CharacterBERT和Bag-of-Words模型检测毒性跨度
International Workshop on Semantic Evaluation Pub Date : 2021-03-17 DOI: 10.18653/v1/2021.semeval-1.25
Akbar Karimi, L. Rossi, A. Prati
{"title":"UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words Model","authors":"Akbar Karimi, L. Rossi, A. Prati","doi":"10.18653/v1/2021.semeval-1.25","DOIUrl":"https://doi.org/10.18653/v1/2021.semeval-1.25","url":null,"abstract":"With the ever-increasing availability of digital information, toxic content is also on the rise. Therefore, the detection of this type of language is of paramount importance. We tackle this problem utilizing a combination of a state-of-the-art pre-trained language model (CharacterBERT) and a traditional bag-of-words technique. Since the content is full of toxic words that have not been written according to their dictionary spelling, attendance to individual characters is crucial. Therefore, we use CharacterBERT to extract features based on the word characters. It consists of a CharacterCNN module that learns character embeddings from the context. These are, then, fed into the well-known BERT architecture. The bag-of-words method, on the other hand, further improves upon that by making sure that some frequently used toxic words get labeled accordingly. With a ∼4 percent difference from the first team, our system ranked 36 th in the competition. The code is available for further research and reproduction of the results.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127496271","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: 4
ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning 任务4:基于语言模型的抽象意义阅读理解负增强
International Workshop on Semantic Evaluation Pub Date : 2021-02-25 DOI: 10.18653/v1/2021.semeval-1.108
Xin Xie, Xiangnan Chen, Xiang Chen, Yong Wang, Ningyu Zhang, Shumin Deng, Huajun Chen
{"title":"ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning","authors":"Xin Xie, Xiangnan Chen, Xiang Chen, Yong Wang, Ningyu Zhang, Shumin Deng, Huajun Chen","doi":"10.18653/v1/2021.semeval-1.108","DOIUrl":"https://doi.org/10.18653/v1/2021.semeval-1.108","url":null,"abstract":"This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively. We further conduct comprehensive model analysis and observe interesting error cases, which may promote future researches. The code and dataset used in our paper can be found at https://github.com/CheaSim/SemEval2021. The leaderboard can be found at https://competitions.codalab.org/competitions/26153.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127278498","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
MeisterMorxrc at SemEval-2020 Task 9: Fine-Tune Bert and Multitask Learning for Sentiment Analysis of Code-Mixed Tweets 代码混合推文情感分析的微调伯特和多任务学习
International Workshop on Semantic Evaluation Pub Date : 2020-12-15 DOI: 10.18653/v1/2020.semeval-1.174
Qi Wu, Peng Wang, Chenghao Huang
{"title":"MeisterMorxrc at SemEval-2020 Task 9: Fine-Tune Bert and Multitask Learning for Sentiment Analysis of Code-Mixed Tweets","authors":"Qi Wu, Peng Wang, Chenghao Huang","doi":"10.18653/v1/2020.semeval-1.174","DOIUrl":"https://doi.org/10.18653/v1/2020.semeval-1.174","url":null,"abstract":"Natural language processing (NLP) has been applied to various fields including text classification and sentiment analysis. In the shared task of sentiment analysis of code-mixed tweets, which is a part of the SemEval-2020 competition, we preprocess datasets by replacing emoji and deleting uncommon characters and so on, and then fine-tune the Bidirectional Encoder Representation from Transformers(BERT) to perform the best. After exhausting top3 submissions, Our team MeisterMorxrc achieves an averaged F1 score of 0.730 in this task, and and our codalab username is MeisterMorxrc","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115202006","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: 5
SIS@IIITH at SemEval-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis SIS@IIITH at SemEval-2020任务8:模因分析的简单文本分类方法概述
International Workshop on Semantic Evaluation Pub Date : 2020-12-01 DOI: 10.18653/v1/2020.semeval-1.157
Sravani Boinepelli, Manish Shrivastava, Vasudeva Varma
{"title":"SIS@IIITH at SemEval-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis","authors":"Sravani Boinepelli, Manish Shrivastava, Vasudeva Varma","doi":"10.18653/v1/2020.semeval-1.157","DOIUrl":"https://doi.org/10.18653/v1/2020.semeval-1.157","url":null,"abstract":"Memes are steadily taking over the feeds of the public on social media. There is always the threat of malicious users on the internet posting offensive content, even through memes. Hence, the automatic detection of offensive images/memes is imperative along with detection of offensive text. However, this is a much more complex task as it involves both visual cues as well as language understanding and cultural/context knowledge. This paper describes our approach to the task of SemEval-2020 Task 8: Memotion Analysis. We chose to participate only in Task A which dealt with Sentiment Classification, which we formulated as a text classification problem. Through our experiments, we explored multiple training models to evaluate the performance of simple text classification algorithms on the raw text obtained after running OCR on meme images. Our submitted model achieved an accuracy of 72.69% and exceeded the existing baseline’s Macro F1 score by 8% on the official test dataset. Apart from describing our official submission, we shall elucidate how different classification models respond to this task.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"506 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116199245","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: 2
TUE at SemEval-2020 Task 1: Detecting Semantic Change by Clustering Contextual Word Embeddings 任务1:通过聚类上下文词嵌入检测语义变化
International Workshop on Semantic Evaluation Pub Date : 2020-12-01 DOI: 10.18653/v1/2020.semeval-1.28
Anna Karnysheva, Pia Schwarz
{"title":"TUE at SemEval-2020 Task 1: Detecting Semantic Change by Clustering Contextual Word Embeddings","authors":"Anna Karnysheva, Pia Schwarz","doi":"10.18653/v1/2020.semeval-1.28","DOIUrl":"https://doi.org/10.18653/v1/2020.semeval-1.28","url":null,"abstract":"This paper describes our system for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. Target words of corpora from two different time periods are classified according to their semantic change. The languages covered are English, German, Latin, and Swedish. Our approach involves clustering ELMo embeddings using DBSCAN and K-means. For a more fine grained detection of semantic change we take the Jensen-Shannon Distance metric and rank the target words from strongest to weakest change. The results show that this is a valid approach for the classification subtask where we rank 13th out of 33 groups with an accuracy score of 61.2%. For the ranking subtask we score a Spearman’s rank-order correlation coefficient of 0.087 which places us on rank 29.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122307225","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: 5
IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language IdentifiCation 任务12:表情符号对攻击性语言识别的影响
International Workshop on Semantic Evaluation Pub Date : 2020-12-01 DOI: 10.18653/v1/2020.semeval-1.263
Sandy Kurniawan, I. Budi, Muhammad Okky Ibrohim
{"title":"IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language IdentifiCation","authors":"Sandy Kurniawan, I. Budi, Muhammad Okky Ibrohim","doi":"10.18653/v1/2020.semeval-1.263","DOIUrl":"https://doi.org/10.18653/v1/2020.semeval-1.263","url":null,"abstract":"In this paper, we present our approach and the results of our participation in OffensEval 2020. There are three sub-tasks in OffensEval 2020 namely offensive language identification (sub-task A), automatic categorization of offense types (sub-task B), and offense target identification (sub-task C). We participated in sub-task A of English OffensEval 2020. Our approach emphasizes on how the emoji affects offensive language identification. Our model used LSTM combined with GloVe pre-trained word vectors to identify offensive language on social media. The best model obtained macro F1-score of 0.88428.","PeriodicalId":444285,"journal":{"name":"International Workshop on Semantic Evaluation","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125345459","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: 2
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