System and method for training multilingual machine translation evaluation models

An estimator-based architecture for training a multilingual machine translation evaluation model, comprising:

• an encoder system for transforming text input into token-level embedding representations suitable for encoding an original source language text segment, a machine translation of the original source language text segment and a reference translation of the original source language text segment into their corresponding d-dimensional numeric embedding space representations;
• a pooling layer system for pooling and combining the token-level embedding representations into segment-level embedding representations;
• an embeddings concatenation system for extracting multiple contrastive feature vector representations from the segment-level embedding representations and combine the vector representations into a single vector representation;
• and
• a neural feed-forward regression system for learning a regression function for generating a machine translation quality score via neural weight optimization.

A method for training an estimator-based multilingual machine translation evaluation model by iteratively optimizing weights of the entire neural system, including the encoder system, the layer attention mechanism and/or the feed-forward regression system, via standard neural back-propagation optimization on data collections of MT-generated translations annotated with human quality scores, comprising:

• transforming text input into token-level embedding representations, encoding an original source language text segment, a machine translation of the original source language text segment and a reference translation of the original source language text segment into their corresponding d-dimensional numeric embedding space representations;
• pooling and combining the token-level embedding representations into segment-level embedding representations;
• extracting multiple contrastive feature vector representations from the segment-level embedding representations and combining the vector representations into a single vector representation;
• applying a neural feed-forward regression system designed to generate a predicted translation quality score function for each training example;
• and
• iteratively optimizing the weights of the entire neural system, including the encoder system, the layer attention mechanism and/or the feed-forward regression system, via standard neural weight back-propagation optimization for a given loss-function on data collections of MT-generated translations annotated with human quality scores.

A ranking-based architecture for training a multilingual machine translation evaluation model, comprising:

• an encoder system for transforming text input into token-level embedding representations suitable for encoding an original source language text segment, a machine translation of the original source language text segment and a reference translation of the original source language text segment into their corresponding d-dimensional numeric embedding space representations;
• a pooling layer system for pooling and combining the token-level embedding representations into segment-level embedding representations;
• and
• a triplet-margin-loss system for calculating a triplet margin loss for a training example consisting of the segment-level embedding representations of an original source language text segment, a first “better” machine translation of the original source language text segment, a second “worse” machine translation of the original source language text segment and a reference translation of the original source language text segment.

A method for training a ranking-based multilingual machine translation evaluation model by iteratively optimizing weights of the entire neural system, including the encoder system and/or the layer attention mechanism via standard neural back-propagation triplet-margin-loss optimization on data collections of MT-generated translations annotated with human quality scores, comprising:

• transforming text input into token-level embedding representations, encoding an original source language text segment, a machine translation of the original source language text segment and a reference translation of the original source language text segment into their corresponding d-dimensional numeric embedding space representations;
• pooling and combining the token-level embedding representations into segment-level embedding representations;
• calculating a triplet margin loss for a training example consisting of the segment-level embedding representations of an original source language text segment, a first “better” machine translation of the original source language text segment, a second “worse” machine translation of the original source language text segment and a reference translation of the original source language text segment;
• and
• iteratively optimizing the weights of the entire neural system, including the encoder system and/or the layer attention mechanism via the standard neural weight back-propagation triplet-margin-loss optimization on data collections of MT-generated translations annotated with human quality scores.