Kancharla Aditya Hari


2023

pdf
WebNLG Challenge 2023: Domain Adaptive Machine Translation for Low-Resource Multilingual RDF-to-Text Generation (WebNLG 2023)
Kancharla Aditya Hari | Bhavyajeet Singh | Anubhav Sharma | Vasudeva Varma
Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)

This paper presents our submission to the WebNLG Challenge 2023 for generating text in several low-resource languages from RDF-triples. Our submission focuses on using machine translation for generating texts in Irish, Maltese, Welsh and Russian. While a simple and straightfoward approach, recent works have shown that using monolingual models for inference for multilingual tasks with the help of machine translation (translate-test) can out-perform multilingual models and training multilingual models on machine-translated data (translate-train) through careful tuning of the MT component. Our results show that this approach demonstrates competitive performance for this task even with limited data.

pdf
Tenzin-Gyatso at SemEval-2023 Task 4: Identifying Human Values behind Arguments Using DeBERTa
Pavan Kandru | Bhavyajeet Singh | Ankita Maity | Kancharla Aditya Hari | Vasudeva Varma
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

Identifying human values behind arguments isa complex task which requires understandingof premise, stance and conclusion together. Wepropose a method that uses a pre-trained lan-guage model, DeBERTa, to tokenize and con-catenate the text before feeding it into a fullyconnected neural network. We also show thatleveraging the hierarchy in values improves theperformance by .14 F1 score.

pdf
IREL at SemEval-2023 Task 11: User Conditioned Modelling for Toxicity Detection in Subjective Tasks
Ankita Maity | Pavan Kandru | Bhavyajeet Singh | Kancharla Aditya Hari | Vasudeva Varma
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper describes our system used in the SemEval-2023 Task 11 Learning With Disagreements (Le-Wi-Di). This is a subjective task since it deals with detecting hate speech, misogyny and offensive language. Thus, disagreement among annotators is expected. We experiment with different settings like loss functions specific for subjective tasks and include anonymized annotator-specific information to help us understand the level of disagreement. We perform an in-depth analysis of the performance discrepancy of these different modelling choices. Our system achieves a cross-entropy of 0.58, 4.01 and 3.70 on the test sets of HS-Brexit, ArMIS and MD-Agreement, respectively. Our code implementation is publicly available.