Abstract: Systems and methods are provided for use of use of fuzzy-match-based translation suggestions to augment machine translation of input sentences or other texts. A machine translation system may use a model trained to translate a source language input to a target language output based on pseudo-randomly selected translation suggestions in the target language, while at inference time the machine translation system may use translation selections associated with source language samples that have a high degree of similarity to the source language input to be translated. To efficiently use the translation suggestions, they may be encoded in context with the source language input to be translated, and the machine translation system may use the encoded translation suggestions with to generate a translation in the target language.

Abstract: Systems and methods are provided for use of use of fuzzy-match-based translation suggestions to augment machine translation of input sentences or other texts. A machine translation system may use a model trained to translate a source language input to a target language output based on pseudo-randomly selected translation suggestions in the target language, while at inference time the machine translation system may use translation selections associated with source language samples that have a high degree of similarity to the source language input to be translated. To efficiently use the translation suggestions, they may be encoded in context with the source language input to be translated, and the machine translation system may use the encoded translation suggestions with to generate a translation in the target language.

Abstract: Machine learning translation models may be selectively tuned to provide custom machine translations. A request to translate input text from an input language to a target language may be received. A tuning data set for translating the input text to the target language may be identified and searched to select pairs of texts in the tuning data according to comparisons with the input text. A machine learning model used to translate into the target language may be tuned using only second texts in the target language in the selected pairs of texts. The tuned machine learning model may then be used to translate the input text into the target language.

Abstract: A translation system receives examples of translations between a first language and a second language. In response to receiving request to translate a source text from the first language to the second language, the system ranks the examples based on the example's applicability to one or more portions of the source text. The system performs additional training of a neural network that was pre-trained to translate from the first language to the second language, where the additional training is based on one or more top-ranking examples. The system translates the source text to the second language using the additionally trained neural network.

Abstract: A system and method for estimating parameters for features of a translation scoring function for scoring candidate translations in a target domain are provided. Given a source language corpus for a target domain, a similarity measure is computed between the source corpus and a target domain multi-model, which may be a phrase table derived from phrase tables of comparative domains, weighted as a function of similarity with the source corpus. The parameters of the log-linear function for these comparative domains are known. A mapping function is learned between similarity measure and parameters of the scoring function for the comparative domains. Given the mapping function and the target corpus similarity measure, the parameters of the translation scoring function for the target domain are estimated. For parameters where a mapping function with a threshold correlation is not found, another method for obtaining the target domain parameter can be used.

Abstract: A method for generating a phrase table for a target domain includes receiving a source corpus for a target domain and, for each of a set of comparative domain phrase tables, computing a measure of similarity between the source corpus and the comparative domain phrase table. Based on the computed similarity measures, a subset of the comparative domain phrase tables may be identified from the set of comparative domain phrase tables, and/or weights for combining them, and a phrase table is generated for the target domain based on the at least a subset of phrase tables.

Abstract: A system and method for estimating parameters for features of a translation scoring function for scoring candidate translations in a target domain are provided. Given a source language corpus for a target domain, a similarity measure is computed between the source corpus and a target domain multi-model, which may be a phrase table derived from phrase tables of comparative domains, weighted as a function of similarity with the source corpus. The parameters of the log-linear function for these comparative domains are known. A mapping function is learned between similarity measure and parameters of the scoring function for the comparative domains. Given the mapping function and the target corpus similarity measure, the parameters of the translation scoring function for the target domain are estimated. For parameters where a mapping function with a threshold correlation is not found, another method for obtaining the target domain parameter can be used.

Abstract: A method for generating a phrase table for a target domain includes receiving a source corpus for a target domain and, for each of a set of comparative domain phrase tables, computing a measure of similarity between the source corpus and the comparative domain phrase table. Based on the computed similarity measures, a subset of the comparative domain phrase tables may be identified from the set of comparative domain phrase tables, and/or weights for combining them, and a phrase table is generated for the target domain based on the at least a subset of phrase tables.