Aditya Vyawahare


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Trinity at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages using Twitter Dataset
Shashank Rathi | Siddhesh Pande | Harshwardhan Atkare | Rahul Tangsali | Aditya Vyawahare | Dipali Kadam
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

In this paper, we have performed sentiment analysis on three African languages (Hausa, Swahili, and Yoruba). We used various deep learning and traditional models paired with a vectorizer for classification and data -preprocessing. We have also used a few data oversampling methods to handle the imbalanced text data. Thus, we could analyze the performance of those models in all the languages by using weighted and macro F1 scores as evaluation metrics.

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L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models
Aabha Pingle | Aditya Vyawahare | Isha Joshi | Rahul Tangsali | Raviraj Joshi
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation


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PICT@DravidianLangTech-ACL2022: Neural Machine Translation On Dravidian Languages
Aditya Vyawahare | Rahul Tangsali | Aditya Mandke | Onkar Litake | Dipali Kadam
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

This paper presents a summary of the findings that we obtained based on the shared task on machine translation of Dravidian languages. As a part of this shared task, we carried out neural machine translations for the following five language pairs: Kannada to Tamil, Kannada to Telugu, Kannada to Malayalam, Kannada to Sanskrit, and Kannada to Tulu. The datasets for each of the five language pairs were used to train various translation models, including Seq2Seq models such as LSTM, bidirectional LSTM, Conv Seq2Seq, and training state-of-the-art as transformers from scratch, and fine-tuning already pre-trained models. For some models involving monolingual corpora, we implemented backtranslation as well. These models’ accuracy was later tested with a part of the same dataset using BLEU score as an evaluation metric.

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Unsupervised and Very-Low Resource Supervised Translation on German and Sorbian Variant Languages
Rahul Tangsali | Aditya Vyawahare | Aditya Mandke | Onkar Litake | Dipali Kadam
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper presents the work of team PICT-NLP for the shared task on unsupervised and very low-resource supervised machine translation, organized by the Workshop on Machine Translation, a workshop in collocation with the Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). The paper delineates the approaches we implemented for supervised and unsupervised translation between the following 6 language pairs: German-Lower Sorbian (de-dsb), Lower Sorbian-German (dsb-de), Lower Sorbian-Upper Sorbian (dsb-hsb), Upper Sorbian-Lower Sorbian (hsb-dsb), German-Upper Sorbian (de-hsb), and Upper Sorbian-German (hsb-de). For supervised learning, we implemented the transformer architecture from scratch using the Fairseq library. Whereas for unsupervised learning, we implemented Facebook’s XLM masked language modeling approach. We discuss the training details for the models we used, and the results obtained from our approaches. We used the BLEU and chrF metrics for evaluating the accuracies of the generated translations on our systems.