Paypal, Inc.

Rajesh Virupaksha Munavalli

Xin Li

Yuehao Wu

Junhua Zhao

Meng Zang

A system performs operations that include receiving, via first computing environment, a request to process text data using a first natural language processing (NLP) model. The operations further include accessing configuration data associated with the NLP model, where the configuration data generated using a domain specific language that supports a plurality of preprocessing modules in a plurality of programming languages. The operations also include selecting, based on the configuration data, one or more preprocessing modules of the plurality of preprocessing modules, generating, based on the configuration data, a preprocessing pipeline using the one or more preprocessing modules, and generating preprocessed text data by inputting the text data into the preprocessing pipeline. The preprocessed text data is provided to the first NLP model.

Framework for managing natural language processing tools

Techniques are disclosed herein relating to using reinforcement learning to generate a dialogue policy. A computer system may perform an iterative training operation to train a deep Q-learning network (DQN) based on conversation logs from prior conversations. In various embodiments, the DQN may include an input layer to receive an input value indicative of a current state of a given conversation, one or more hidden layers, and an output layer that includes a set of nodes corresponding to available responses. During the iterative training operation, the disclosed techniques may analyze utterances from a conversation log and, based on the utterances, use the DQN to determine appropriate responses. Reward values may be determined based on the selected responses and, based on the reward values, the DQN may be updated. Once generated, the dialogue policy may be used by a chatbot system to guide conversations with users.

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San Jose, CA, United States of America

Rajesh Virupaksha Munavalli