Enhancing ASR by MT using Semantic Information from HindiWordNet

Aniruddha  Tammewar; Karan  Singla; Srinivas  Bangalore; Michael Carl

Enhancing ASR by MT using Semantic Information from HindiWordNet

Aniruddha Tammewar, Karan Singla, Srinivas Bangalore, Michael Carl

Publikation: Konferencebidrag › Poster › Forskning › peer review

Abstract

In a conventional CAT (Computer Assisted Translation) system a human translator post-edits an automatically generated target language text using the keyboard. In this paper we extend a CAT system with speech input by which the translator speaks the translation, a process refered to as sight translation. We report several experiments to improve the performance of an automatic speech recognition system, taking advantage of machine translation output and information fromWordNet. Overall we outperform a baseline system which has no semantic information by an increased 1.6% word accuracy for the English to Hindi translation.

Originalsprog	Engelsk
Publikationsdato	2013
Status	Udgivet - 2013
Begivenhed	10th International Conference on Natural Language Processing - Centre for Development of Advanced Computing, Noida, Indien Varighed: 18 dec. 2013 → 20 dec. 2013 Konferencens nummer: 10 http://ltrc.iiit.ac.in/icon/2013/index.php

Konference

Konference	10th International Conference on Natural Language Processing
Nummer	10
Lokation	Centre for Development of Advanced Computing
Land/Område	Indien
By	Noida
Periode	18/12/2013 → 20/12/2013
Internetadresse	http://ltrc.iiit.ac.in/icon/2013/index.php

Handle.net

Persistent link

Citationsformater

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title = "Enhancing ASR by MT using Semantic Information from HindiWordNet",

abstract = "In a conventional CAT (Computer Assisted Translation) system a human translator post-edits an automatically generated target language text using the keyboard. In this paper we extend a CAT system with speech input by which the translator speaks the translation, a process refered to as sight translation. We report several experiments to improve the performance of an automatic speech recognition system, taking advantage of machine translation output and information fromWordNet. Overall we outperform a baseline system which has no semantic information by an increased 1.6% word accuracy for the English to Hindi translation.",

author = "Aniruddha Tammewar and Karan Singla and Srinivas Bangalore and Michael Carl",

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note = "10th International Conference on Natural Language Processing ; Conference date: 18-12-2013 Through 20-12-2013",

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TY - CONF

T1 - Enhancing ASR by MT using Semantic Information from HindiWordNet

AU - Tammewar, Aniruddha

AU - Singla, Karan

AU - Bangalore, Srinivas

AU - Carl, Michael

N1 - Conference code: 10

PY - 2013

Y1 - 2013

N2 - In a conventional CAT (Computer Assisted Translation) system a human translator post-edits an automatically generated target language text using the keyboard. In this paper we extend a CAT system with speech input by which the translator speaks the translation, a process refered to as sight translation. We report several experiments to improve the performance of an automatic speech recognition system, taking advantage of machine translation output and information fromWordNet. Overall we outperform a baseline system which has no semantic information by an increased 1.6% word accuracy for the English to Hindi translation.

AB - In a conventional CAT (Computer Assisted Translation) system a human translator post-edits an automatically generated target language text using the keyboard. In this paper we extend a CAT system with speech input by which the translator speaks the translation, a process refered to as sight translation. We report several experiments to improve the performance of an automatic speech recognition system, taking advantage of machine translation output and information fromWordNet. Overall we outperform a baseline system which has no semantic information by an increased 1.6% word accuracy for the English to Hindi translation.

M3 - Poster

T2 - 10th International Conference on Natural Language Processing

Y2 - 18 December 2013 through 20 December 2013

ER -