DLRG @ T amil NLP - ACL 2022: Offensive Span Identification in T amil using B i LSTM - CRF approach (2022)

Abstract: Identifying offensive speech is an exciting andessential area of research, with ample tractionin recent times. This paper presents our sys-tem submission to the subtask 1, focusing onusing supervised approaches for extracting Of-fensive spans from code-mixed Tamil-Englishcomments. To identify offensive spans, wedeveloped the Bidirectional Long Short-TermMemory (BiLSTM) model with Glove Em-bedding. To this end, the developed systemachieved an overall F1 of 0.1728. Addition-ally, for comments with less than 30 characters,the developed system shows an F1 of 0.3890,competitive with other submissions.

## introduction offensive speech, in general, is defined as the speech that causes an individual/group to feel displeased, upset, angry, or annoyed @xcite . often offensive speech is intended to vilify, humiliate, or incite hatred against a group or a class of persons based on race, religion, skin color, sexual identity, gender identity, ethnicity, disability, or national origin @xcite . predominantly with social media outreach, this is more prevalent. accordingly, pinpointing such offensive speech is vital to encourage healthy conversation across users. moreover, such systems are essential in automatic content moderation, with minimal human involvement @xcite . code-mixing is yet another social media phenomenon that has crept into daily speech across all languages, including @xcite . often, we see the usage of more than one language like tamil-english, kannada-english, etc., which adds a layer of complexity in identifying offensive contents @xcite . code-mixing and code-borrowing have become common among the multi-lingual people @xcite . even though offensive content classification on code-mixed language has been studied by few researchers by applying machine learning (ratnavel rajalakshmi, 2020) and deep learning algorithms @xcite , the span identification of offensive contents are not explored much. dictionary learning approaches were proposed for short text classification and url based classification applying machine learning techniques (r. and aravindan, 2018; @xcite , but the research work in tamil is limited. tamil is a member of the southern branch of the dravidian languages, a group of about 26 languages indigenous to the indian subcontinent @xcite @xcite . it is also classed as a member of the tamil language family, which contains the languages of around 35 ethnolinguistic groups, including the irula and yerukula languages @xcite @xcite @xcite . malayalam is tamil's closest significant cousin; the two began splitting during the 9th century ad. although several variations between tamil and malayalam indicate a pre-historic break of the western dialect, the process of separating into a different language, malayalam, did not occur until the 13th or 14th century. this work, the shared task on offensive span identification handles the code-mixed tamil-english comments and focuses on identification of character offsets of the offensive parts (? @xcite @xcite . there are multiple approaches for extracting spans. in this work, we treat the task of removing offensive span as an approach to token labeling. in this regard, we evaluated bi-lstm + crf-based token labeling system for extracting offensive spans. the rest of the paper is organized as follows. first, section 2 briefly discusses the literature on offensive span identification-related works. then, in section 3, our system is described in detail, followed by section 4, in which the experiments and results are presented. finally, we conclude with possible implications for future work. ## related works offensive span can be solved in multiple ways ranging from token labeling to extracting spans using interpretability approaches. unfortunately, the overall work is still developing for english and code-mixed languages, with very few wellestablished data sets and methods. @xcite . interesting works related to offensive spans include @xcite that employs token labeling using language models with a mixture of conditional random fields (crf). usually, token labeling systems use bio encoding of the text corresponding to offensive spans. lexicon-based models (burtenshaw and kestemont, 2021) and statistical analysis @xcite are also widely explored. finally, a few strategies utilize custom loss functions tailored explicitly for managing wrong spans. for code-mixed tamil-english to date, we find there is only by @xcite that again uses token level labeling with language models. ## problem and system description an example of offensive span identification is shown in figure 1 . given the input sentence, the task is to extract the range of spans corresponding to offensive content. in the above example, the word poramboku contributes to offensiveness which corresponds to character offset of 47-56. a dataset with offensive span annotations details was released as part of the shared task on toxic span identification @xcite . the description of this dataset is presented in section 3.1. ## experiments and results we have conducted various experiments to study the performance of the model and submitted the best performing version of our model. the results obtained are as shown in ## conclusion offensive span identification is still a challenging task with multiple challenges including the need of learning less data and long range contexts. in this work, we studied bi-lstm + crf model to predict offensive spans from code-mixed tamil-english comments. accordingly our system obtained the overall f 1 of 0.1728 which is significantly lower. however we found that the developed method is suitable for shorter sequences where we can see higher results. in the future we plan to revisit the architecture in detail with a study on effect of embeddings types, number of layers and advanced architectures.