Abstract: Devices and techniques are generally described for machine translation. In various examples, first text in a first spoken language may be received. A first candidate translation of the first text in a second spoken language may be determined based at least in part on the first text from among a candidate pool of non-translated text associated with a first domain. Combined data representing the first text and the first candidate translation may be generated. A first machine learning model may generate second text by modifying at least one word of the first candidate translation. The second text representing a machine translation of the first text may be output.

Abstract: A pipeline is provided for management of a pool of chat message templates for an automated dialog system. The pool of chat messages may be managed using machine learning-based clustering and feedback-based modifications. A set of chat messages may be analyzed using a machine learning model to generate different clusters of messages that are semantically related. Representative messages may be selected from each cluster and used in chat sessions according to the semantic context of the chat sessions. Based on feedback obtained during the chat sessions, metrics generated based on the feedback, and/or other data, modifications may be made to the clusters and/or the representative messages to improve the performance of the automated dialog system.