Google Inc.

Sasha Goldshtein

Asaf Aharoni

Yaniv Leviathan

Eyal Segalis

Ofer Ron

Tomer Amiaz

Yoav Tzur

Razvan Mathias

Deborah Cohen

Gal Elidan

Yossi Matias

Tal Cohen

Shoji Ogura

Sivan Eiger

Gadi Ben Amram

Ran Dahan

Adva Wolf

Tal Snir

Zahi Akiva

Implementations are directed to receiving unstructured free-form natural language input, generating a chatbot based on the unstructured free-form natural language input and in response to receiving the unstructured free-form natural language input, and causing the chatbot to perform engage in corresponding conversations with additional users. In various implementations, the unstructured free-form natural language input implicitly defines a corresponding dialog state map (e.g., defines corresponding dialog states and/or corresponding dialog state transitions) without defining any explicit dialog states and/or explicit dialog state transitions. In other implementations, the unstructured free-form natural language input is assigned to explicit dialog states and/or explicit dialog state transitions. Nonetheless, the unstructured free-form natural language input may be utilized to fine-tune and/or primed a machine learning model that is already capable of being utilized in conducting generalized conversations. As a result, the chatbot can be generated and deployed in a quick and efficient manner.

Structured description-based chatbot development techniques

Implementations are directed to receiving unstructured free-form natural language input, generating a chatbot based on the unstructured free-form natural language input and in response to receiving the unstructured free-form natural language input, and causing the chatbot to perform task(s) associated with an entity and on behalf of the user. In various implementations, the unstructured free-form natural language input conveys details of the task(s) to be performed, but does not define any corresponding dialog state map (e.g., does not define any dialog states or any dialog state transitions). Nonetheless, the unstructured free-form natural language input may be utilized to fine-tune and/or prime a machine learning model that is already capable of being utilized in conducting generalized conversations. As a result, the chatbot can be generated and deployed in a quick and efficient manner for performance of the task(s) on behalf of the user.

Unstructured description-based chatbot development techniques

Implementations are directed to simulating automated telephone call(s) to be performed by an automated assistant. Processor(s) can receive user input and determine, based on the user input, that the user input includes: a request to cause the automated assistant to initiate an automated telephone call with an entity, and a task to be performed during the automated telephone call. In some implementations, the processor(s) can cause a simulation of the automated telephone call to be performed to simulate the task, and, based on a result of the simulation, determine whether to initiate the automated telephone call or to refrain from initiating the automated telephone call. In additional or alternative implementations, the processor(s) can determine whether to cause the simulation of the automated telephone call to be performed based on a type of the entity, a type of the task, and/or whether a prior simulation of the task has been performed.

Simulation of automated telephone call(s)

Implementations are directed to providing a voice bot development platform that enables a third-party developer to train a voice bot based on training instance(s). The training instance(s) can each include training input and training output. The training input can include a portion of a corresponding conversation and a prior context of the corresponding conversation. The training output can include a corresponding ground truth response to the portion of the corresponding conversation. Subsequent to training, the voice bot can be deployed for conducting conversations on behalf of a third-party. In some implementations, the voice bot is further trained based on a corresponding feature emphasis input that attentions the voice bot to a particular feature of the portion of the corresponding conversation. In some additional or alternative implementations, the voice bot is further trained to interact with third-party system(s) via remote procedure calls (RPCs).

Example-based voice bot development techniques

Implementations are directed to updating a trained voice bot that is deployed for conducting conversations on behalf of a third-party. A third-party developer can interact with a voice bot development system that enables the third-party developer to train, update, validate, and monitor performance of the trained voice bot. In various implementations, the trained voice bot can be updated by updating a corpus of training instances that was initially utilized to train the voice bot, and updating the trained voice bot based on the updated corpus. In some implementations, the corpus of training instances may be updated in response to identifying occurrence(s) of behavioral error(s) of the trained voice bot while the conversations are being conducted on behalf of the third-party. In additional or alternative implementations, the corpus of training instances may be updated in response to determining the trained voice bot does not include a desired behavior.

Updating trained voice bot(s) utilizing example-based voice bot development techniques

Implementations are provided for automatically mining corpus(es) of electronic video files for video clips that contain spoken utterances that are suitable usage examples to accompany or compliment dictionary definitions. These video clips may then be associated with target n-grams in a searchable database, such as a database underlying an online dictionary. In various implementations, a set of candidate video clips in which a target n-gram is uttered in a target context may be identified from a corpus of electronic video files. For each candidate video clip of the set, pre-existing manual subtitles associated with the candidate video clip may be compared to text generated based on speech recognition processing of an audio portion of the candidate video clip. Based at least in part on the comparing, a measure of suitability as a dictionary usage example may be calculated for the candidate video clip.

Using video clips as dictionary usage examples

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Tel Aviv, Israel

Sasha Goldshtein