Clash between Segment-level MT Error Analysis and Selected Lexical Similarity Metrics

Combining Confidence Estimation and Reference-based Metrics for Segment-level MT Evaluation

We describe an effort to improve standard reference-based metrics for Machine Translation (MT) evaluation by enriching them with Confidence Estimation (CE) features and using a learning mechanism trained on human annotations. Reference-based MT evaluation metrics compare the system output against reference translations looking for overlaps at different levels (lexical, syntactic, and semantic)....

Augmenting Noun Taxonomies by Combining Lexical Similarity Metrics

the relationship between topic familiarity and guessing lexical meaning and retention among iranian efl learners

the skill of reading in english as a foreign language is an important and challenging one which is affected both by linguistic and extra linguistic factors. since vocabularies are part and parcel of every reading comprehension text, knowing enough vocabulary always facilitates this process. however, guessing strategy as one of the most important strategies has consistently ignored by language l...

Towards Heterogeneous Automatic MT Error Analysis

This work studies the viability of performing heterogeneous automatic MT error analyses. Error analysis is, undoubtly, one of the most crucial stages in the development cycle of an MT system. However, often not enough attention is paid to this process. The reason is that performing an accurate error analysis requires intensive human labor. In order to speed up the error analysis process, we sug...

Reordering Metrics for MT

One of the major challenges facing statistical machine translation is how to model differences in word order between languages. Although a great deal of research has focussed on this problem, progress is hampered by the lack of reliable metrics. Most current metrics are based on matching lexical items in the translation and the reference, and their ability to measure the quality of word order h...