Adapting automated assistants for use with multiple languages

A method of training encoder-decoder machine translation model to map a plurality of syntactically-distinct but semantically-similar phrases in a first language to a single canonical second language translation, comprising:

• processing the plurality of syntactically-distinct but semantically-similar phrases in the first language based on the encoder-decoder machine translation model to generate a plurality of syntactically-distinct but semantically-similar second language translations;
• comparing the plurality of syntactically-distinct but semantically-similar second language translations to the single canonical second language translation to generate corresponding errors;
• and
• training the encoder-decoder machine translation model based on the corresponding errors.

A system comprising one or more processors and memory storing instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to:

• capture a spoken utterance at a microphone to generate audio data, wherein the spoken utterance is spoken in a first language;
• perform speech recognition processing on the audio data to generate speech-recognition output;
• apply the speech-recognition output as input across a trained encoder-decoder machine translation model to generate output, wherein the output comprises a canonical second language translation of the speech-recognition output from the first language;
• determine an intent conveyed by the spoken utterance based on the canonical second language translation;
• and
• perform one or more responsive actions based on the intent;
• wherein the encoder-decoder machine translation model was trained previously to map a plurality of syntactically-distinct but semantically-similar phrases in the first language to the same canonical second language translation, wherein the encoder-decoder machine translation model was trained previously by:
• processing the plurality of phrases in the first language based on the encoder-decoder machine translation model to generate a plurality of syntactically-distinct but semantically-similar second language translations;
• and
• comparing the plurality of syntactically-distinct but semantically-similar second language translations to the canonical second language translation to generate corresponding errors, whereby the encoder-decoder machine translation model was trained previously based on the corresponding errors.