Rohit Kumar


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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
Anastassia Loukina | Michelle Morales | Rohit Kumar
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)


Phonetically Balanced Code-Mixed Speech Corpus for Hindi-English Automatic Speech Recognition
Ayushi Pandey | Brij Mohan Lal Srivastava | Rohit Kumar | Bhanu Teja Nellore | Kasi Sai Teja | Suryakanth V. Gangashetty
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


Error-tolerant speech-to-speech translation
Rohit Kumar | Sanjika Hewavitharana | Nina Zinovieva | Matthew E. Roy | Edward Pattison-Gordon
Proceedings of Machine Translation Summit XV: Papers


Anticipatory translation model adaptation for bilingual conversations
Sanjika Hewavitharana | Dennis Mehay | Sankaranarayanan Ananthakrishnan | Rohit Kumar | John Makhoul
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

Conversational spoken language translation (CSLT) systems facilitate bilingual conversations in which the two participants speak different languages. Bilingual conversations provide additional contextual information that can be used to improve the underlying machine translation system. In this paper, we describe a novel translation model adaptation method that anticipates a participant’s response in the target language, based on his counterpart’s prior turn in the source language. Our proposed strategy uses the source language utterance to perform cross-language retrieval on a large corpus of bilingual conversations in order to obtain a set of potentially relevant target responses. The responses retrieved are used to bias translation choices towards anticipated responses. On an Iraqi-to-English CSLT task, our method achieves a significant improvement over the baseline system in terms of BLEU, TER and METEOR metrics.


Interactive Error Resolution Strategies for Speech-to-Speech Translation Systems
Rohit Kumar | Matthew Roy | Sankaranarayanan Ananthakrishnan | Sanjika Hewavitharana | Frederick Choi
Proceedings of the SIGDIAL 2013 Conference

Semi-Supervised Word Sense Disambiguation for Mixed-Initiative Conversational Spoken Language Translation
Sankaranarayanan Ananthakrishnan | Sanjika Hewavitharana | Rohit Kumar | Enoch Kan | Rohit Prasad | Prem Natarajan
Proceedings of Machine Translation Summit XIV: Papers

Source aware phrase-based decoding for robust conversational spoken language translation
Sankaranarayanan Ananthakrishnan | Wei Chen | Rohit Kumar | Dennis Mehay
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

Spoken language translation (SLT) systems typically follow a pipeline architecture, in which the best automatic speech recognition (ASR) hypothesis of an input utterance is fed into a statistical machine translation (SMT) system. Conversational speech often generates unrecoverable ASR errors owing to its rich vocabulary (e.g. out-of-vocabulary (OOV) named entities). In this paper, we study the possibility of alleviating the impact of unrecoverable ASR errors on translation performance by minimizing the contextual effects of incorrect source words in target hypotheses. Our approach is driven by locally-derived penalties applied to bilingual phrase pairs as well as target language model (LM) likelihoods in the vicinity of source errors. With oracle word error labels on an OOV word-rich English-to-Iraqi Arabic translation task, we show statistically significant relative improvements of 3.2% BLEU and 2.0% METEOR over an error-agnostic baseline SMT system. We then investigate the impact of imperfect source error labels on error-aware translation performance. Simulation experiments reveal that modest translation improvements are to be gained with this approach even when the source error labels are noisy.


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Active error detection and resolution for speech-to-speech translation
Rohit Prasad | Rohit Kumar | Sankaranarayanan Ananthakrishnan | Wei Chen | Sanjika Hewavitharana | Matthew Roy | Frederick Choi | Aaron Challenner | Enoch Kan | Arvid Neelakantan | Prem Natarajan
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

We describe a novel two-way speech-to-speech (S2S) translation system that actively detects a wide variety of common error types and resolves them through user-friendly dialog with the user(s). We present algorithms for detecting out-of-vocabulary (OOV) named entities and terms, sense ambiguities, homophones, idioms, ill-formed input, etc. and discuss novel, interactive strategies for recovering from such errors. We also describe our approach for prioritizing different error types and an extensible architecture for implementing these decisions. We demonstrate the efficacy of our system by presenting analysis on live interactions in the English-to-Iraqi Arabic direction that are designed to invoke different error types for spoken language translation. Our analysis shows that the system can successfully resolve 47% of the errors, resulting in a dramatic improvement in the transfer of problematic concepts.


Comparing Triggering Policies for Social Behaviors
Rohit Kumar | Carolyn Rosé
Proceedings of the SIGDIAL 2011 Conference


Engaging learning groups using Social Interaction Strategies
Rohit Kumar | Carolyn P. Rosé
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics


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Building Conversational Agents with Basilica
Rohit Kumar | Carolyn P. Rosé | Michael J. Witbrock
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Demonstration Session


Conquest—An Open-Source Dialog System for Conferences
Dan Bohus | Sergio Grau Puerto | David Huggins-Daines | Venkatesh Keri | Gopala Krishna | Rohit Kumar | Antoine Raux | Stefanie Tomko
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers