Pratik Mehta


2019

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The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval
Constantine Lignos | Daniel Cohen | Yen-Chieh Lien | Pratik Mehta | W. Bruce Croft | Scott Miller
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

When performing cross-language information retrieval (CLIR) for lower-resourced languages, a common approach is to retrieve over the output of machine translation (MT). However, there is no established guidance on how to optimize the resulting MT-IR system. In this paper, we examine the relationship between the performance of MT systems and both neural and term frequency-based IR models to identify how CLIR performance can be best predicted from MT quality. We explore performance at varying amounts of MT training data, byte pair encoding (BPE) merge operations, and across two IR collections and retrieval models. We find that the choice of IR collection can substantially affect the predictive power of MT tuning decisions and evaluation, potentially introducing dissociations between MT-only and overall CLIR performance.

2018

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The IIT Bombay English-Hindi Parallel Corpus
Anoop Kunchukuttan | Pratik Mehta | Pushpak Bhattacharyya
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2015

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Investigating the potential of post-ordering SMT output to improve translation quality
Pratik Mehta | Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the 12th International Conference on Natural Language Processing