Paper Info
Title
Using the TED Talks to Evaluate Spoken Post-editing of Machine Translation.
Abstract
This paper presents a solution to evaluate spoken post-editing of imperfect machine translation output by a human translator. We compare two approaches to the combination of machine translation (MT) and automatic speech recognition (ASR): a heuristic algorithm and a machine learning method. To obtain a data set with spoken post-editing information, we use the French version of TED talks as the source texts submitted to MT, and the spoken English counterparts as their corrections, which are submitted to an ASR system. We experiment with various levels of artificial ASR noise and also with a state-of-the-art ASR system. The results show that the combination of MT with ASR improves over both individual outputs of MT and ASR in terms of BLEU scores, especially when ASR performance is low.
Year
Venue
Keywords
2016
LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
machine translation,spoken post-editing,evaluation
Field
DocType
Citations 
Heuristic (computer science),Computer science,Machine translation,Speech recognition,Natural language processing,Artificial intelligence
Conference
0
PageRank 
References 
Authors
0.34
19
2
Name
Order
Citations
PageRank
Jeevanthi Liyanapathirana1111.14
Andrei Popescu-Belis257364.13