Q. T. Ngo, Anh Tuan Hoang, Huyen Nguyen, Lien Nguyen
{"title":"VLSP 2021 - vnli挑战:越南语和英越语文本蕴涵","authors":"Q. T. Ngo, Anh Tuan Hoang, Huyen Nguyen, Lien Nguyen","doi":"10.25073/2588-1086/vnucsce.363","DOIUrl":null,"url":null,"abstract":"This paper presents the first challenge on recognizing textual entailment (RTE), also known as natural language inference (NLI), held in a Vietnamese Language and Speech Processing workshop (VLSP 2021).The challenge aims to determine, for a given pair of sentences, whether the two sentences semantically agree, disagree, or are neutral/irrelevant to each other. The input sentences are in English or Vietnamese and may not be in the same language. This task is important in identifying, from different information sources, the evidence that supports or refutes a statement. The identification of such evidence is subsequently useful for many information tracking applications, such as opinion mining, brand and reputation management, and particularly fighting against fake news.Through this challenge, we would like to provide an opportunity for participants who are interested in the problem, to contribute their knowledge to improve the existing techniques and methods for the task, so as to enhance the effectiveness of those applications.In the paper, we introduce a collection of Vietnamese and English sentences in the domain of health that we built to serve as a benchmarking dataset for the task. We also describe the evaluation results of systems participating in the challenge.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"VLSP 2021 - vnNLI Challenge: Vietnamese and English-Vietnamese Textual Entailment\",\"authors\":\"Q. T. Ngo, Anh Tuan Hoang, Huyen Nguyen, Lien Nguyen\",\"doi\":\"10.25073/2588-1086/vnucsce.363\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the first challenge on recognizing textual entailment (RTE), also known as natural language inference (NLI), held in a Vietnamese Language and Speech Processing workshop (VLSP 2021).The challenge aims to determine, for a given pair of sentences, whether the two sentences semantically agree, disagree, or are neutral/irrelevant to each other. The input sentences are in English or Vietnamese and may not be in the same language. This task is important in identifying, from different information sources, the evidence that supports or refutes a statement. The identification of such evidence is subsequently useful for many information tracking applications, such as opinion mining, brand and reputation management, and particularly fighting against fake news.Through this challenge, we would like to provide an opportunity for participants who are interested in the problem, to contribute their knowledge to improve the existing techniques and methods for the task, so as to enhance the effectiveness of those applications.In the paper, we introduce a collection of Vietnamese and English sentences in the domain of health that we built to serve as a benchmarking dataset for the task. We also describe the evaluation results of systems participating in the challenge.\",\"PeriodicalId\":416488,\"journal\":{\"name\":\"VNU Journal of Science: Computer Science and Communication Engineering\",\"volume\":\"84 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"VNU Journal of Science: Computer Science and Communication Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.25073/2588-1086/vnucsce.363\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"VNU Journal of Science: Computer Science and Communication Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25073/2588-1086/vnucsce.363","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
VLSP 2021 - vnNLI Challenge: Vietnamese and English-Vietnamese Textual Entailment
This paper presents the first challenge on recognizing textual entailment (RTE), also known as natural language inference (NLI), held in a Vietnamese Language and Speech Processing workshop (VLSP 2021).The challenge aims to determine, for a given pair of sentences, whether the two sentences semantically agree, disagree, or are neutral/irrelevant to each other. The input sentences are in English or Vietnamese and may not be in the same language. This task is important in identifying, from different information sources, the evidence that supports or refutes a statement. The identification of such evidence is subsequently useful for many information tracking applications, such as opinion mining, brand and reputation management, and particularly fighting against fake news.Through this challenge, we would like to provide an opportunity for participants who are interested in the problem, to contribute their knowledge to improve the existing techniques and methods for the task, so as to enhance the effectiveness of those applications.In the paper, we introduce a collection of Vietnamese and English sentences in the domain of health that we built to serve as a benchmarking dataset for the task. We also describe the evaluation results of systems participating in the challenge.