Adithya Pratapa


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Automatic Extraction of Rules Governing Morphological Agreement
Aditi Chaudhary | Antonios Anastasopoulos | Adithya Pratapa | David R. Mortensen | Zaid Sheikh | Yulia Tsvetkov | Graham Neubig
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Creating a descriptive grammar of a language is an indispensable step for language documentation and preservation. However, at the same time it is a tedious, time-consuming task. In this paper, we take steps towards automating this process by devising an automated framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format. We focus on extracting rules describing agreement, a morphosyntactic phenomenon at the core of the grammars of many of the world’s languages. We apply our framework to all languages included in the Universal Dependencies project, with promising results. Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data. We confirm this finding with human expert evaluations of the rules that our framework produces, which have an average accuracy of 78%. We release an interface demonstrating the extracted rules at

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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.


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Word Embeddings for Code-Mixed Language Processing
Adithya Pratapa | Monojit Choudhury | Sunayana Sitaram
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We compare three existing bilingual word embedding approaches, and a novel approach of training skip-grams on synthetic code-mixed text generated through linguistic models of code-mixing, on two tasks - sentiment analysis and POS tagging for code-mixed text. Our results show that while CVM and CCA based embeddings perform as well as the proposed embedding technique on semantic and syntactic tasks respectively, the proposed approach provides the best performance for both tasks overall. Thus, this study demonstrates that existing bilingual embedding techniques are not ideal for code-mixed text processing and there is a need for learning multilingual word embedding from the code-mixed text.

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Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data
Adithya Pratapa | Gayatri Bhat | Monojit Choudhury | Sunayana Sitaram | Sandipan Dandapat | Kalika Bali
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Training language models for Code-mixed (CM) language is known to be a difficult problem because of lack of data compounded by the increased confusability due to the presence of more than one language. We present a computational technique for creation of grammatically valid artificial CM data based on the Equivalence Constraint Theory. We show that when training examples are sampled appropriately from this synthetic data and presented in certain order (aka training curriculum) along with monolingual and real CM data, it can significantly reduce the perplexity of an RNN-based language model. We also show that randomly generated CM data does not help in decreasing the perplexity of the LMs.


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Quantitative Characterization of Code Switching Patterns in Complex Multi-Party Conversations: A Case Study on Hindi Movie Scripts
Adithya Pratapa | Monojit Choudhury
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)