Arafat Ahsan


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Assessing Post-editing Effort in the English-Hindi Direction
Arafat Ahsan | Vandan Mujadia | Dipti Misra Sharma
Proceedings of the 18th International Conference on Natural Language Processing (ICON)

We present findings from a first in-depth post-editing effort estimation study in the English-Hindi direction along multiple effort indicators. We conduct a controlled experiment involving professional translators, who complete assigned tasks alternately, in a translation from scratch and a post-edit condition. We find that post-editing reduces translation time (by 63%), utilizes fewer keystrokes (by 59%), and decreases the number of pauses (by 63%) when compared to translating from scratch. We further verify the quality of translations thus produced via a human evaluation task in which we do not detect any discernible quality differences.


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Coupling Statistical Machine Translation with Rule-based Transfer and Generation
Arafat Ahsan | Prasanth Kolachina | Sudheer Kolachina | Dipti Misra | Rajeev Sangal
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

In this paper, we present the insights gained from a detailed study of coupling a highly modular English-Hindi RBMT system with a standard phrase-based SMT system. Coupling the RBMT and SMT systems at various stages in the RBMT pipeline, we observe the effects of the source transformations at each stage on the performance of the coupled MT system. We propose an architecture that systematically exploits the structural transfer and robust generation capabilities of the RBMT system. Working with the English-Hindi language pair, we show that the coupling configurations explored in our experiments help address different aspects of the typological divergence between these languages. In spite of working with very small datasets, we report significant improvements both in terms of BLEU (7.14 and 0.87 over the RBMT and the SMT baselines respectively) and subjective evaluation (relative decrease of 17% in SSER).