Amazon Technologies, Inc.

Fabian Triefenbach

Edward C Bueche

Toby Ray Latin-Stoermer

Brandon Scott Durham

Fabian Moerchen

Panagiota Karanasou

Jonathan Barnett Feinstein

Alok Verma

Catherine Breslin

Amina Shabbeer

Judith Gaspers

Klaus Reiter

Wael Hamza

Chandana Satya Prakash

Jonathan Jakob Hueser

Jin Cao

Mariusz Momotko

Techniques for using a shared encoder and multiple different decoders for natural language understanding (NLU) tasks are described. The individual decoders are configured to perform different tasks using the output from one shared encoder. The decoders can process with respect to different domains and different languages. Using the shared encoder can reduce computation time during runtime. Using the shared encoder can reduce training costs (e.g., time and resources) when the system is updated to incorporate additional intents and entities. The system employs an attention mechanism to extract encoded representation data that can be used by the different decoders for its specific task.

Shared encoder for natural language understanding processing

Techniques are provided for training a target language model based at least in part on data associated with a reference language model. For example, language data utilized to train an English language model may be translated and provided as training data to train a German language model to recognize utterances provided in German. By utilizing the techniques herein, the efficiency of training a new language model may be improved due at least in part to replacing labor-intensive operations conventionally performed by specialized personnel with machine-generated data. Additionally, techniques discussed herein provide for reducing the time required for training a new language model by leveraging information associated with utterances of one language to train the new language model associated with a different language.

Techniques for model training for voice features

Techniques are provided for training a language recognition model. For example, a language recognition model may be maintained and associated with a reference language (e.g., English). The language recognition model may be configured to accept as input an utterance in the reference language and to identify a feature to be executed in response to receiving the utterance. New language data (e.g., other utterances) provided in a different language (e.g., German) may be obtained. This new language data may be translated to English and utilized to retrain the model to recognize reference language data as well as language data translated to the reference language. Subsequent utterances (e.g., English utterances, or German utterances translated to English) may be provided to the updated model and a feature may be identified. One or more instructions may be sent to a user device to execute a set of instructions associated with the feature.

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Meschede, Germany

Fabian Triefenbach