Amazon Technologies, Inc.

Felix Hieber

Hagen Fuerstenau

Loris Bazzani

Maksim Lapin

Tobias Domhan

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

Techniques are generally described for automatic scoring of alt-text for image data. In various examples, first image data and first text data describing the first image data may be received. A feature representation of the first image data may be determined using an encoder machine learning model. A hidden state representation may be determined using a decoder machine learning model based on the feature representation and a first word of the first text data. In some examples, a first score may be determined using the hidden state representation. The first score may include an indication of a descriptive capability of the first text data with respect to the first image data.

Machine learning system to score alt-text in image data

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

Felix Hieber