This paper describes the submissions by team HWR to the Dravidian Language Identification (DLI) shared task organized at VarDial 2021 workshop. The DLI training set includes 16,674 YouTube comments written in Roman script containing code-mixed text with English and one of the three South Dravidian languages: Kannada, Malayalam, and Tamil. We submitted results generated using two models, a Naive Bayes classifier with adaptive language models, which has shown to obtain competitive performance in many language and dialect identification tasks, and a transformer-based model which is widely regarded as the state-of-the-art in a number of NLP tasks. Our first submission was sent in the closed submission track using only the training set provided by the shared task organisers, whereas the second submission is considered to be open as it used a pretrained model trained with external data. Our team attained shared second position in the shared task with the submission based on Naive Bayes. Our results reinforce the idea that deep learning methods are not as competitive in language identification related tasks as they are in many other text classification tasks.
DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text
National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）；ULTRA Arts and Science College（ULTRA艺术与科学学院）；Cardiff University（卡迪夫大学）；Indian Institute of Information Technology and Management-Kerala（印度喀拉拉邦信息技术与管理学院）
Developing Successful Shared Tasks on Offensive Language Identification for Dravidian Languages
Insight SFI Research Centre for Data Analytics, National University of Ireland Galway（爱尔兰国立大学戈尔韦分校Insight数据分析研究中心）；TR Labs, Thomson Reuters（汤森路透TR实验室）；ULTRA Arts and Science College（ULTRA文理学院）；National Institute of Technology Karnataka Surathkal（卡纳塔克邦苏拉特卡尔国立技术学院）；Eastern University（东方大学）；National Institute of Technology Tiruchirappalli（蒂鲁吉拉帕利国立技术学院）；SRM Institute of Science and Technology（SRM科学技术学院）
Offensive Language Identification in Low-resourced Code-mixed Dravidian languages using Pseudo-labeling
Indian Institute of Information Technology Tiruchirappalli（蒂鲁奇拉帕利印度信息技术学院）；ULTRA Arts and Science College（ULTRA文理学院）；Eastern University（东方大学）；Kongu Engineering College（孔古工程学院）；Sri Sivasubramaniya Nadar College of Engineering（斯里·西瓦苏布拉马尼亚·纳达尔工程学院）；Insight SFI Research Centre for Data Analytics, National University of Ireland Galway（爱尔兰国立大学戈尔韦分校SFI洞察数据分析研究中心）
KBCNMUJAL@HASOC-Dravidian-CodeMix-FIRE2020: Using Machine Learning for Detection of Hate Speech and Offensive Code-Mixed Social Media text
Institute of Management and Research, Jalgaon, Affil- KBC North Maharashtra University（贾尔冈管理与研究学院，隶属于KBC北马哈拉施特拉大学）；School of Computer Sciences, KBC North Maharashtra University（KBC北马哈拉施特拉大学计算机科学学院）；Copenhagen Business School（哥本哈根商学院）；BMCC college of commerce, Pune（BMCC商学院，浦那）
Directed Graph-alignment Approach for Identification of Gaps in Short Answers
Centre for Educational Technology（教育技术中心）；G.S. Sanyal School of Telecommunications（G.S.桑亚尔电信学院）；Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）
Findings of the Shared Task on Offensive Span Identification from Code-Mixed Tamil-English Comments
Georgia Institute of Technology（佐治亚理工学院）；Data Science Institute, National University of Ireland Galway（爱尔兰国立高威大学数据科学研究所）；National Institute of Technology Karnataka Surathkal（卡纳塔克邦苏拉特卡尔国家理工学院）；National Institute of Technology, Trichy（特里奇国家理工学院）；Vellore Institute of Technology（韦洛尔理工学院）；Eastern University, Sri Lanka（斯里兰卡东方大学）；Indian Institute of Information Technology and Management, Kerala（喀拉拉邦印度信息技术与管理学院）；Thiagarajar College of Engineering（蒂亚加拉贾工程学院）
Comparing representations of long clinical texts for the task of patient note-identification
Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；Data Science Platform, Imagine Institute（伊马金研究所数据科学平台）；Hôpital Universitaire Necker-Enfants Malades（内克尔-恩范茨-马拉德大学医院）；Assistance Publique - Hôpitaux de Paris (APHP)（巴黎公共医疗援助医院集团）；Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；LORIA（洛林计算机科学与应用实验室）；INESC-ID（里斯本大学学院计算机与系统工程研究中心）；Instituto Superior Técnico（里斯本高等技术学院）；Universidade de Lisboa（里斯本大学）
Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance
