We present our works on SemEval-2021 Task 5 about Toxic Spans Detection. This task aims to build a model for identifying toxic words in whole posts. We use the BiLSTM-CRF model combining with ToxicBERT Classification to train the detection model for identifying toxic words in posts. Our model achieves 62.23% by F1-score on the Toxic Spans Detection task.

Over 97 million inhabitants speak Vietnamese as the native language in the world. However, there are few research studies on machine reading comprehension (MRC) in Vietnamese, the task of understanding a document or text, and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning like word matching and demands complicate reasoning such as single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC methods in English and Chinese as the first experimental models on UIT-ViQuAD, which will be compared to further models. We also estimate human performances on the dataset and compare it to the experimental results of several powerful machine models. As a result, the substantial differences between humans and the best model performances on the dataset indicate that improvements can be explored on UIT-ViQuAD through future research. Our dataset is freely available to encourage the research community to overcome challenges in Vietnamese MRC.

This paper describes the system of NLP@UIT that participated in Task 4 of SemEval-2019. We developed a system that predicts whether an English news article follows a hyperpartisan argumentation. Paparazzo is the name of our system and is also the code name of our team in Task 4 of SemEval-2019. The Paparazzo system, in which we use tri-grams of words and hepta-grams of characters, officially ranks thirteen with an accuracy of 0.747. Another system of ours, which utilizes trigrams of words, tri-grams of characters, trigrams of part-of-speech, syntactic dependency sub-trees, and named-entity recognition tags, achieved an accuracy of 0.787 and is proposed after the deadline of Task 4.

Treebanks are important resources for researchers in natural language processing, speech recognition, theoretical linguistics, etc. To strengthen the automatic processing of the Vietnamese language, a Vietnamese treebank has been built. However, the quality of this treebank is not satisfactory and is a possible source for the low performance of Vietnamese language processing. We have been building a new treebank for Vietnamese with about 40,000 sentences annotated with three layers: word segmentation, part-of-speech tagging, and bracketing. In this paper, we describe several challenges of Vietnamese language and how we solve them in developing annotation guidelines. We also present our methods to improve the quality of the annotation guidelines and ensure annotation accuracy and consistency. Experiment results show that inter-annotator agreement ratios and accuracy are higher than 90% which is satisfactory.

This paper presents our findings on the feasibility of doing pronoun resolution for biomedical texts, in comparison with conducting pronoun resolution for the newswire domain. In our experiments, we built a simple machine learning-based pronoun resolution system, and evaluated the system on three different corpora: MUC, ACE, and GENIA. Comparative statistics not only reveal the noticeable issues in constructing an effective pronoun resolution system for a new domain, but also provides a comprehensive view of those corpora often used for this task.