Discriminative Language Models as a Tool for Machine Translation Error Analysis
Koichi Akabe, Graham Neubig, Sakriani Sakti, Tomoki Toda, Satoshi Nakamura · 2014
In this paper, we propose a new method for effective error analysis of machine translation (MT) systems. In previous work on error analysis of MT, error trends are often shown by frequency. However, if we attempt to perform a more detailed analysis based on frequently erroneous word strings, the word strings also often occur in correct translations, and analyzing these correct sen-tences decreases the overall efficiency of error analysis. In this paper, we propose the use of regularized discriminative language models (LMs) to allow for more focused MT error analysis. In experiments, we demonstrate that our method is more efficient than frequency-based analysis, and examine differences across systems, language pairs, and evaluation measures. 1 1