In aiming at research and development on machine translation, we produced a test collection for Japanese-English machine translation in the seventh NTCIR Workshop. This paper desc...
A new approach to handle unknown words in machine translation is presented. The basic idea is to find definitions for the unknown words on the source language side and translate t...
When users communicate with each other via machine translation, it is important to improve the quality of the translations. The “Back Translation” technique can improve the tra...
The performance of machine translation systems varies greatly depending on the source and target languages involved. Determining the contribution of different characteristics of l...
Recent developments on hybrid systems that combine rule-based machine translation (RBMT) systems with statistical machine translation (SMT) generally neglect the fact that RBMT sy...
This paper presents an empirical study on how different selections of input translation systems affect translation quality in system combination. We give empirical evidence that t...
We describe a dataset containing 16,000 translations produced by four machine translation systems and manually annotated for quality by professional translators. This dataset can ...
We describe a focused effort to investigate the performance of phrase-based, human evaluation of machine translation output achieving a high annotator agreement. We define phrase-...
In recent years, corpus based approaches to machine translation have become predominant, with Statistical Machine Translation (SMT) being the most actively progressing area. Succe...
We present the methodology that underlies new metrics for semantic machine translation evaluation that we are developing. Unlike widely-used lexical and n-gram based MT evaluation...