A Diagnostic Evaluation Approach for English to Hindi MT Using Linguistic Checkpoints and Error Rates

This paper addresses diagnostic evaluation of machine translation (MT) systems for Indian languages, English to Hindi MT in particular, assessing the performance of MT systems on relevant linguistic phenomena (checkpoints). We use the diagnostic evaluation tool DELiC4MT to analyze the performance of MT systems on various PoS categories (e.g. nouns, verbs). The current system supports only word level checkpoints which might not be as helpful in evaluating the translation quality as compared to using checkpoints at phrase level and checkpoints that deal with named entities (NE), inflections, word order, etc. We therefore suggest phrase level checkpoints and NEs as additional checkpoints for DELiC4MT. We further use Hjerson to evaluate checkpoints based on word order and inflections that are relevant for evaluation of MT with Hindi as the target language. The experiments conducted using Hjerson generate overall (document level) error counts and error rates for five error classes (inflectional errors, reordering errors, missing words, extra words, and lexical errors) to take into account the evaluation based on word order and inflections. The effectiveness of the approaches was tested on five English to Hindi MT systems.