Amazon Technologies, Inc.

Hagen Fuerstenau

Felix Hieber

Based on a candidate set of translations produced by a neural network based machine learning model, a mapping data structure such as a statistical phrase table is generated. The mapping data structure is analyzed to obtain a quality metric of the neural network based model. One or more operations are initiated based on the quality metric.

Artificial intelligence system using phrase tables to evaluate and improve neural network based machine translation

A mechanism is disclosed for evaluating the quality of a machine translation system. Propositions are identified within a source language string and within a translation of the source language string generated by a machine translation system. The propositions can be identified using an automated mechanism or using human translators through the use of a human intelligence task site. Once the propositions have been identified, the propositions identified in the source language string can be compared to the propositions identified in the translated target language string. The results of the comparison can be utilized to compute a quality score for the translation. A final quality score can be generated for the machine translation system by repeating this process for multiple source language strings. The final quality score can then be utilized to improve the quality of models utilized by the machine translation system.

Machine intelligence system for machine translation quality evaluation by identifying matching propositions in source and translated text strings

Machine learning models can determine whether post-edits to machine translated content are corrective post-edits, which are edits made to correct translation errors caused during machine translation, or content improvement post-edits, which are post-edits that have been made to improve source language content. The corrective post-edits can be utilized to generate or modify labels for strings utilized to train a translation quality estimation system. The content improvement post-edits can be utilized to improve the quality of source content prior to providing the source content to the machine translation system for translation.

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Berlin, Germany

Hagen Fuerstenau