Papers by Konthala Yasaswini
ArXiv, 2021
To tackle the conundrum of detecting offensive comments/posts which are considerably informal, un... more To tackle the conundrum of detecting offensive comments/posts which are considerably informal, unstructured, miswritten and code-mixed, we introduce two inventive methods in this research paper. Offensive comments/posts on the social media platforms, can affect an individual, a group or underage alike. In order to classify comments/posts in two popular Dravidian languages, Tamil and Malayalam, as a part of the HASOC DravidianCodeMix FIRE 2021 shared task, we employ two Transformer-based prototypes which successfully stood in the top 8 for all the tasks. The codes for our approach can be viewed and utilized.
This paper demonstrates our work for the shared task on Offensive Language Identification in Drav... more This paper demonstrates our work for the shared task on Offensive Language Identification in Dravidian Languages-EACL 2021. Offensive language detection in the various social media platforms was identified previously. But with the increase in diversity of users, there is a need to identify the offensive language in multilingual posts that are largely code-mixed or written in a non-native script. We approach this challenge with various transfer learning-based models to classify a given post or comment in Dravidian languages (Malayalam, Tamil, and Kannada) into 6 categories. The source codes for our systems are published.
ArXiv, 2021
Social media has effectively become the prime hub of communication and digital marketing. As thes... more Social media has effectively become the prime hub of communication and digital marketing. As these platforms enable the free manifestation of thoughts and facts in text, images and video, there is an extensive need to screen them to protect individuals and groups from offensive content targeted at them. Our work intends to classify code-mixed social media comments/posts in the Dravidian languages of Tamil, Kannada, andMalayalam. We intend to improve offensive language identification by generating pseudo-labels on the dataset. A custom dataset is constructed by transliterating all the code-mixed texts into the respective Dravidian language, either Kannada, Malayalam, or Tamil and then generating pseudo-labels for the transliterated dataset. The two datasets are combined using the generated pseudo-labels to create a custom dataset called CMTRA. As Dravidian languages are under-resourced, our approach increases the amount of training data for the language models. We fine-tune several r...
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Papers by Konthala Yasaswini