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.
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.
no code implementations • ACL (dialdoc) 2021 • Sopan Khosla, Justin Lovelace, Ritam Dutt, Adithya Pratapa
In this paper, we discuss our submission for DialDoc subtask 1.
1 code implementation • ACL (SIGMORPHON) 2021 • Sai Muralidhar Jayanthi, Adithya Pratapa
In this work, we analyze the robustness of neural machine translation systems towards grammatical perturbations in the source.
1 code implementation • 1 Nov 2023 • Yanlin Feng, Adithya Pratapa, David R Mortensen
In this paper, we present CASENT, a seq2seq model designed for ultra-fine entity typing that predicts ultra-fine types with calibrated confidence scores.
1 code implementation • 24 Oct 2023 • Adithya Pratapa, Kevin Small, Markus Dreyer
Generating concise summaries of news events is a challenging natural language processing task.
no code implementations • 8 Feb 2023 • Jiefu Ou, Adithya Pratapa, Rishubh Gupta, Teruko Mitamura
In this work, we present an extension to the event grounding task that requires tackling hierarchical event structures from the KB.
1 code implementation • NAACL (MIA) 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).
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.
1 code implementation • EMNLP 2021 • Adithya Pratapa, Antonios Anastasopoulos, Shruti Rijhwani, Aditi Chaudhary, David R. Mortensen, Graham Neubig, Yulia Tsvetkov
Text generation systems are ubiquitous in natural language processing applications.
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.
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.
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 (ASR)
Language Identification
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