Aditya Kane


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Two-stage Pipeline for Multilingual Dialect Detection
Ankit Vaidya | Aditya Kane
Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023)

Dialect Identification is a crucial task for localizing various Large Language Models. This paper outlines our approach to the VarDial 2023 shared task. Here we have to identify three or two dialects from three languages each which results in a 9-way classification for Track-1 and 6-way classification for Track-2 respectively. Our proposed approach consists of a two-stage system and outperforms other participants’ systems and previous works in this domain. We achieve a score of 58.54% for Track-1 and 85.61% for Track-2. Our codebase is available publicly (

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My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks
Tanmay Chavan | Omkar Gokhale | Aditya Kane | Shantanu Patankar | Raviraj Joshi
Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)


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Temporal Word Meaning Disambiguation using TimeLMs
Mihir Godbole | Parth Dandavate | Aditya Kane
Proceedings of the First Workshop on Ever Evolving NLP (EvoNLP)

Meaning of words constantly change given the events in modern civilization. Large Language Models use word embeddings, which are often static and thus cannot cope with this semantic change. Thus, it is important to resolve ambiguity in word meanings. This paper is an effort in this direction, where we explore methods for word sense disambiguation for the EvoNLP shared task. We conduct rigorous ablations for two solutions to this problem. We see that an approach using time-aware language models helps this task. Furthermore, we explore possible future directions to this problem.

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Transformer based ensemble for emotion detection
Aditya Kane | Shantanu Patankar | Sahil Khose | Neeraja Kirtane
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis

Detecting emotions in languages is important to accomplish a complete interaction between humans and machines. This paper describes our contribution to the WASSA 2022 shared task which handles this crucial task of emotion detection. We have to identify the following emotions: sadness, surprise, neutral, anger, fear, disgust, joy based on a given essay text. We are using an ensemble of ELECTRA and BERT models to tackle this problem achieving an F1 score of 62.76%. Our codebase ( and our WandB project ( is publicly available.