Kavya Nerella


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CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing
Sai Muralidhar Jayanthi | Kavya Nerella | Khyathi Raghavi Chandu | Alan W Black
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching

The NLP community has witnessed steep progress in a variety of tasks across the realms of monolingual and multilingual language processing recently. These successes, in conjunction with the proliferating mixed language interactions on social media, have boosted interest in modeling code-mixed texts. In this work, we present CodemixedNLP, an open-source library with the goals of bringing together the advances in code-mixed NLP and opening it up to a wider machine learning community. The library consists of tools to develop and benchmark versatile model architectures that are tailored for mixed texts, methods to expand training sets, techniques to quantify mixing styles, and fine-tuned state-of-the-art models for 7 tasks in Hinglish. We believe this work has the potential to foster a distributed yet collaborative and sustainable ecosystem in an otherwise dispersed space of code-mixing research. The toolkit is designed to be simple, easily extensible, and resourceful to both researchers as well as practitioners. Demo: <http://k-ikkees.pc.cs.cmu.edu:5000> and Library: <https://github.com/murali1996/CodemixedNLP>


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Constrained Fact Verification for FEVER
Adithya Pratapa | Sai Muralidhar Jayanthi | Kavya Nerella
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Fact-verification systems are well explored in the NLP literature with growing attention owing to shared tasks like FEVER. Though the task requires reasoning on extracted evidence to verify a claim’s factuality, there is little work on understanding the reasoning process. In this work, we propose a new methodology for fact-verification, specifically FEVER, that enforces a closed-world reliance on extracted evidence. We present an extensive evaluation of state-of-the-art verification models under these constraints.