Abstract: Disclosed is a technical solution to summarize a multimodal conferencing environment. The solution is designed to improve efficiency and accuracy of computing systems as a summarization tool by incorporating memory, machine readable instructions, and processor circuitry. The solution executes the functions of adjusting a language model based on a terminology utilized in a first context data; generating a conversation summary from a transcription and a human controlled variable; extracting a semantic entity from the conversation summary and second context data, where the second context data is indicative of an input associated with a conferencing environment; and summarize the semantic entity and the second context data using the adjusted language model.

Abstract: Disclosed is a technical solution to process a video that captures actions to be performed for completing a task based on a chronological sequence of stages within the task. An example system may identify an action sequence from an instruction for the task. The system inputs the action sequence into a trained model (e.g., a recurrent neural network), which outputs the chronological sequence of stages. The RNN may be trained through self-supervised learning. The system may input the video and the chronological sequence of stages into another trained model, e.g., a temporal convolutional network. The other trained model may include hidden layers arranged before an attention layer. The hidden layers may extract features from the video and feed the features into the attention layer. The attention layer may determine attention weights of the features based on the chronological sequence of stages.

Abstract: Domain-specific speech recognizer generation with crowd sourcing is described. The domain-specific speech recognizers are generated for voice user interfaces (VUIs) configured to replace or supplement application interfaces. In accordance with the described techniques, the speech recognizers are generated for a respective such application interface and are domain-specific because they are each generated based on language data that corresponds to the respective application interface. This domain-specific language data is used to build a domain-specific language model. The domain-specific language data is also used to collect acoustic data for building an acoustic model. In particular, the domain-specific language data is used to generate user interfaces that prompt crowd-sourcing participants to say selected words represented by the language data for recording. The recordings of these selected words are then used to build the acoustic model.

Abstract: A framework for annotating image edit requests includes a structure for identifying natural language request as either comments or image edit requests and for identifying the text of a request that maps to an executable action in an image editing program, as well as to identify other entities from the text related to the action. The annotation framework can be used to aid in the creation of artificial intelligence networks that carry out the requested action. An example method includes displaying a test image, displaying a natural language input with selectable text, and providing a plurality of selectable action tag controls and entity tag controls. The method may also include receiving selection of the text, receiving selection of an action tag control for the selected text, generating a labeled pair, and storing the labeled pair with the natural language input as an annotated natural language image edit request.

Abstract: A technique for multiple turn conversational task assistance includes receiving data representing a conversation between a user and an agent. The conversation includes a digitally recorded video portion and a digitally recorded audio portion, where the audio portion corresponds to the video portion. Next, the audio portion is segmented into a plurality of audio chunks. For each of the audio chunks, a transcript of the respective audio chunk is received. Each of the audio chunks is grouped into one or more dialog acts, where each dialog act includes at least one of the respective audio chunks, the validated transcript corresponds to the respective audio chunks, and a portion of the video portion corresponds to the respective audio chunk. Each of the dialog acts is stored in a data corpus.

Abstract: A framework for annotating image edit requests includes a structure for identifying natural language request as either comments or image edit requests and for identifying the text of a request that maps to an executable action in an image editing program, as well as to identify other entities from the text related to the action. The annotation framework can be used to aid in the creation of artificial intelligence networks that carry out the requested action. An example method includes displaying a test image, displaying a natural language input with selectable text, and providing a plurality of selectable action tag controls and entity tag controls. The method may also include receiving selection of the text, receiving selection of an action tag control for the selected text, generating a labeled pair, and storing the labeled pair with the natural language input as an annotated natural language image edit request.

Abstract: A technique for multiple turn conversational task assistance includes receiving data representing a conversation between a user and an agent. The conversation includes a digitally recorded video portion and a digitally recorded audio portion, where the audio portion corresponds to the video portion. Next, the audio portion is segmented into a plurality of audio chunks. For each of the audio chunks, a transcript of the respective audio chunk is received. Each of the audio chunks is grouped into one or more dialog acts, where each dialog act includes at least one of the respective audio chunks, the validated transcript corresponds to the respective audio chunks, and a portion of the video portion corresponds to the respective audio chunk. Each of the dialog acts is stored in a data corpus.

Abstract: Domain-specific speech recognizer generation with crowd sourcing is described. The domain-specific speech recognizers are generated for voice user interfaces (VUIs) configured to replace or supplement application interfaces. In accordance with the described techniques, the speech recognizers are generated for a respective such application interface and are domain-specific because they are each generated based on language data that corresponds to the respective application interface. This domain-specific language data is used to build a domain-specific language model. The domain-specific language data is also used to collect acoustic data for building an acoustic model. In particular, the domain-specific language data is used to generate user interfaces that prompt crowd-sourcing participants to say selected words represented by the language data for recording. The recordings of these selected words are then used to build the acoustic model.