A Reading Comprehension Corpus for Machine Translation Evaluation

Effectively assessing Natural Language Processing output tasks is a challenge for research in the area. In the case of Machine Translation (MT), automatic metrics are usually preferred over human evaluation, given time and budget constraints.However, traditional automatic metrics (such as BLEU) are not reliable for absolute quality assessment of documents, often producing similar scores for documents translated by the same MT system.For scenarios where absolute labels are necessary for building models, such as document-level Quality Estimation, these metrics can not be fully trusted... (read more)

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