Ankur Gandhe


2019

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Neural Text Normalization with Subword Units
Courtney Mansfield | Ming Sun | Yuzong Liu | Ankur Gandhe | Björn Hoffmeister
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)

Text normalization (TN) is an important step in conversational systems. It converts written text to its spoken form to facilitate speech recognition, natural language understanding and text-to-speech synthesis. Finite state transducers (FSTs) are commonly used to build grammars that handle text normalization. However, translating linguistic knowledge into grammars requires extensive effort. In this paper, we frame TN as a machine translation task and tackle it with sequence-to-sequence (seq2seq) models. Previous research focuses on normalizing a word (or phrase) with the help of limited word-level context, while our approach directly normalizes full sentences. We find subword models with additional linguistic features yield the best performance (with a word error rate of 0.17%).

2013

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Hypothesis Refinement Using Agreement Constraints in Machine Translation
Ankur Gandhe | Rashmi Gangadharaiah
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2011

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A Word Reordering Model for Improved Machine Translation
Karthik Visweswariah | Rajakrishnan Rajkumar | Ankur Gandhe | Ananthakrishnan Ramanathan | Jiri Navratil
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Handling verb phrase morphology in highly inflected Indian languages for Machine Translation
Ankur Gandhe | Rashmi Gangadharaiah | Karthik Visweswariah | Ananthakrishnan Ramanathan
Proceedings of 5th International Joint Conference on Natural Language Processing

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Clause-Based Reordering Constraints to Improve Statistical Machine Translation
Ananthakrishnan Ramanathan | Pushpak Bhattacharyya | Karthik Visweswariah | Kushal Ladha | Ankur Gandhe
Proceedings of 5th International Joint Conference on Natural Language Processing