Abstract: Techniques are described and relate to providing computing services using embeddings of a transformer-based encoder. In an example, a computer system generates, by using a machine learning (ML) transformer, an embedding vector based at least in part on text. The computer system stores the embedding vector and an association between the embedding vector and the text in a data store. Further, the computer system determines that a task is to be performed based at least in part on natural language understanding (NLU) of the text. The computer system receives the embedding vector from the data store based at least in part on the association between the embedding vector and the text. The task is performed based at least in part on the embedding vector after being received from the data store.

Abstract: A multi-tier domain is provided for processing user voice queries and making routing decisions for generating responses, including for user voice queries that include multi-domain trigger words or phrases. When an utterance is recognized as different intents in different domains, a routing system for a domain may consider contextual signals, including those associated with other domains, to determine whether the domain is the proper one to handle the request. This determination can be performed with a statistical model specifically trained to make such determinations using the available contextual data.

Abstract: Features are disclosed for training and using a word embedding model configured to receive textual and context data associated with an utterance of a user. A word embedding model can be trained with text data and context data to account for context associated with the text data. The word embedding model can receive an input vector including text data and one or more sets of context data associated with the text data and perform word embedding based on the input vector. In some embodiments, the input vector can include an automatic speech recognition (“ASR”) confidence score generated by an ASR model and one or more labels generated by an NLU model. In some embodiments, the input vector can include user information associated with the user.

Abstract: A speech processing system uses contextual data to determine the specific domains, subdomains, and applications appropriate for taking action in response to spoken commands and other utterances. Some applications may be given priority over others such that some applications are general request applications to which responsibility for processing an intent is to be assigned as long as contextual criteria are satisfied, while other applications are specific request applications to which responsibility for processing an intent is to be assigned only if the applications are specifically requested, if the contextual criteria of priority applications are not satisfied, and/or if certain contextual criteria associated with the specific request applications are satisfied.

Abstract: A speech processing system uses contextual data to determine the specific domains, subdomains, and applications appropriate for taking action in response to spoken commands and other utterances. The system can use signals and other contextual data associated with an utterance, such as location signals, content catalog data, data regarding historical usage patterns, data regarding content visually presented on a display screen of a computing device when an utterance was made, other data, or some combination thereof.

Abstract: Features are disclosed for training and using named entity recognition models based on gazetteer information. A named entity recognition model can be trained with a gazetteer output at a layer of the model to provide deterministic data in the probabilistic model. The named entity recognition model can recognize named entities based on the word embedding and the gazetteer output. The named entity recognition model can tune the gazetteer output to include false positive name entities such that the gazetteer output is not deterministic of the output of the model. In some embodiments, the named entity recognition model can be tuned so as to adjust the gazetteer output.

Abstract: A speech processing system uses contextual data to determine the specific domains, subdomains, and applications appropriate for taking action in response to spoken commands and other utterances. Some applications may be given priority over others such that some applications are general request applications to which responsibility for processing an intent is to be assigned as long as contextual criteria are satisfied, while other applications are specific request applications to which responsibility for processing an intent is to be assigned only if the applications are specifically requested, if the contextual criteria of priority applications are not satisfied, and/or if certain contextual criteria associated with the specific request applications are satisfied.

Abstract: A multi-tier domain is provided for processing user voice queries and making routing decisions for generating responses, including for user voice queries that include multi-domain trigger words or phrases. When an utterance is recognized as different intents in different domains, a routing system for a domain may consider contextual signals, including those associated with other domains, to determine whether the domain is the proper one to handle the request. This determination can be performed with a statistical model specifically trained to make such determinations using the available contextual data.

Abstract: A speech processing system uses contextual data to determine the specific domains, subdomains, and applications appropriate for taking action in response to spoken commands and other utterances. The system can use signals and other contextual data associated with an utterance, such as location signals, content catalog data, data regarding historical usage patterns, data regarding content visually presented on a display screen of a computing device when an utterance was made, other data, or some combination thereof.

Abstract: Processing methods for forming a silicon nitride film comprising exposing a metal surface to a silicon precursor, a nitrogen-containing reactant and a hydrogen-containing plasma at a temperature less than or equal to about 250° C. to form a silicon nitride film with a low etch rate without damaging the metal surface.

Abstract: Processing methods for forming a silicon nitride film comprising exposing a metal surface to a silicon precursor, a nitrogen-containing reactant and a hydrogen-containing plasma at a temperature less than or equal to about 250° C. to form a silicon nitride film with a low etch rate without damaging the metal surface.

Abstract: Processing platforms having a central transfer station with a robot and an environment having greater than or equal to about 0.1% by weight water vapor, a pre-clean chamber connected to a side of the transfer station and a batch processing chamber connected to a side of the transfer station. The processing platform configured to pre-clean a substrate to remove native oxides from a first surface, form a blocking layer using a alkylsilane and selectively deposit a film. Methods of using the processing platforms and processing a plurality of wafers are also described.

Abstract: Processing methods for forming a silicon nitride film comprising exposing a metal surface to a silicon precursor, a nitrogen-containing reactant and a hydrogen-containing plasma at a temperature less than or equal to about 250° C. to form a silicon nitride film with a low etch rate without damaging the metal surface.

Abstract: Processes for depositing SiO2 films on a wafer surface utilizing an aminosilane compound as a silicon precursor are described.

Abstract: Processing platforms having a central transfer station with a robot and an environment having greater than or equal to about 0.1% by weight water vapor, a pre-clean chamber connected to a side of the transfer station and a batch processing chamber connected to a side of the transfer station. The processing platform configured to pre-clean a substrate to remove native oxides from a first surface, form a blocking layer using a alkylsilane and selectively deposit a film. Methods of using the processing platforms and processing a plurality of wafers are also described.

Abstract: Processes for depositing SiO2 films on a wafer surface utilizing an aminosilane compound as a silicon precursor are described.

Abstract: Processes for depositing SiO2 films on a wafer surface utilizing an aminosilane compound as a silicon precursor are described.

Abstract: Processes for depositing SiO2 films on a wafer surface utilizing an aminosilane compound as a silicon precursor are described.

Abstract: Embodiments disclosed herein generally relate to the processing of substrates, and more particularly, relate to methods for accurate control of film thickness using deposition-etch cycles. Particularly, embodiments of the present disclosure may be used in controlling film thickness during filling high aspect ratio features.

Abstract: Embodiments disclosed herein generally relate to the processing of substrates, and more particularly, relate to methods for accurate control of film thickness using deposition-etch cycles. Particularly, embodiments of the present disclosure may be used in controlling film thickness during filling high aspect ratio features.