Paper: Tackling Sparse Data Issue in Machine Translation Evaluation

ACL ID P10-2016
Title Tackling Sparse Data Issue in Machine Translation Evaluation
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

We illustrate and explain problems of n-grams-based machine translation (MT) metrics (e.g. BLEU) when applied to morphologically rich languages such as Czech. A novel metric SemPOS based on the deep-syntactic representation of the sentence tackles the issue and retains the performance for translation to English as well.