Luu Hoai Linh, Nguyen Hai Long, Nguyen Hai Yen, Thi-Hai-Yen Vuong
{"title":"Vietnamese Legal Question Answering with combined features and deep learning","authors":"Luu Hoai Linh, Nguyen Hai Long, Nguyen Hai Yen, Thi-Hai-Yen Vuong","doi":"10.1109/KSE53942.2021.9648797","DOIUrl":null,"url":null,"abstract":"Legal Question Answering is an arduous problem that is divided into certain phases, each with its own set of challenges. In this work, we have accomplished three tasks given by the ALQAC 2021 competition, which are aimed at addressing the aforementioned problem, by proposing the combined features (cosine similarity of TF-IDF, an average of word embedding; and Jaccard distance) accompanied by a classification model for task 1; ensemble learning multiple deep learning models for task 2. Finally, we employed a specifically modified mechanism for long documents to undertake task 3. All three methods perform satisfactory results and have profuse potential improvements.","PeriodicalId":130986,"journal":{"name":"2021 13th International Conference on Knowledge and Systems Engineering (KSE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 13th International Conference on Knowledge and Systems Engineering (KSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KSE53942.2021.9648797","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Legal Question Answering is an arduous problem that is divided into certain phases, each with its own set of challenges. In this work, we have accomplished three tasks given by the ALQAC 2021 competition, which are aimed at addressing the aforementioned problem, by proposing the combined features (cosine similarity of TF-IDF, an average of word embedding; and Jaccard distance) accompanied by a classification model for task 1; ensemble learning multiple deep learning models for task 2. Finally, we employed a specifically modified mechanism for long documents to undertake task 3. All three methods perform satisfactory results and have profuse potential improvements.