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.
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.
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 • 17 Apr 2025 • Adithya Pratapa, Teruko Mitamura
Our method first estimates the optimal retrieval length as a function of the retriever, summarizer, and dataset.
1 code implementation • 10 Feb 2025 • Adithya Pratapa, Teruko Mitamura
Automatically summarizing large text collections is a valuable tool for document research, with applications in journalism, academic research, legal work, and many other fields.
1 code implementation • 22 May 2024 • Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Minsoo Kang, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff Schneider, Eduard Hovy, Roger Grosse, Eric Xing
Large language models (LLMs) are trained on a vast amount of human-written data, but data providers often remain uncredited.
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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