Search Results for author: Adithya Pratapa

Found 11 papers, 5 papers with code

Constrained Fact Verification for FEVER

no code implementations EMNLP 2020 Adithya Pratapa, Sai Muralidhar Jayanthi, Kavya Nerella

Fact-verification systems are well explored in the NLP literature with growing attention owing to shared tasks like FEVER.

Fact Verification

Comparing Grammatical Theories of Code-Mixing

no code implementations WNUT (ACL) 2021 Adithya Pratapa, Monojit Choudhury

Code-mixed text generation systems have found applications in many downstream tasks, including speech recognition, translation and dialogue.

Speech Recognition Text Generation +1

Multilingual Event Linking to Wikidata

1 code implementation13 Apr 2022 Adithya Pratapa, Rishubh Gupta, Teruko Mitamura

On the two proposed tasks, we compare multiple event linking systems including BM25+ (Lv and Zhai, 2011) and multilingual adaptations of the biencoder and crossencoder architectures from BLINK (Wu et al., 2020).

Domain Generalization

Cross-document Event Identity via Dense Annotation

1 code implementation CoNLL (EMNLP) 2021 Adithya Pratapa, Zhengzhong Liu, Kimihiro Hasegawa, Linwei Li, Yukari Yamakawa, Shikun Zhang, Teruko Mitamura

To this end, we design a new annotation workflow with careful quality control and an easy-to-use annotation interface.

Automatic Extraction of Rules Governing Morphological Agreement

1 code implementation EMNLP 2020 Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa, David R. Mortensen, Zaid Sheikh, Yulia Tsvetkov, Graham Neubig

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.

Cross-Lingual Transfer

Word Embeddings for Code-Mixed Language Processing

no code implementations EMNLP 2018 Adithya Pratapa, Monojit Choudhury, Sunayana Sitaram

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.

Machine Translation POS +2

Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data

no code implementations ACL 2018 Adithya Pratapa, Gayatri Bhat, Monojit Choudhury, Sunayana Sitaram, D, S apat, ipan, Kalika Bali

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.

Automatic Speech Recognition Language Identification +1

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