Paper: Evaluating the Word Sense Disambiguation Performance of Statistical Machine Translation

ACL ID I05-2021
Title Evaluating the Word Sense Disambiguation Performance of Statistical Machine Translation
Venue International Joint Conference on Natural Language Processing
Session poster-demo-tutorial
Year 2005
Authors

We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to- English SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sam- ple task. Much effort has been put in de- signing and evaluating dedicated word sense disambiguation (WSD) models, in particu- lar with the Senseval series of workshops. At the same time, the recent improvements in the BLEU scores of statistical machine translation (SMT) suggests that SMT mod- els are good at predicting the right transla- tion of the words in source language sen- tences. Surprisingly however, the WSD ac- curacy of SMT models has never been eval- uated and compared with that of the de...