Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning

Abstract

Customer support via chat requires agents to resolve customer queries with minimum wait time and maximum customer satisfaction. Given that the agents as well as the customers can have varying levels of literacy, the overall quality of responses provided by the agents tend to be poor if they are not predefined. But using only static responses can lead to customer detraction as the customers tend to feel that they are no longer interacting with a human. Hence, it is vital to have variations of the static responses to reduce monotonicity of the responses. However, maintaining a list of such variations can be expensive. Given the conversation context and the agent response, we propose an unsupervised frame-work to generate contextual paraphrases using autoregressive models. We also propose an automated metric based on Semantic Similarity, Textual Entailment, Expression Diversity and Fluency to evaluate the quality of contextual paraphrases and demonstrate performance improvement with Reinforcement Learning (RL) fine-tuning using the automated metric as the reward function.

References in corpus (8)

• CTRL: A Conditional Transformer Language Model for Controllable Generation
• Fine-Tuning Language Models from Human Preferences
• Neural Paraphrase Generation with Stacked Residual LSTM Networks
• Efficient Natural Language Response Suggestion for Smart Reply
• A Deep Generative Framework for Paraphrase Generation
• Joint Copying and Restricted Generation for Paraphrase
• A Study of Reinforcement Learning for Neural Machine Translation
• PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification

Cited by in corpus (3)

• LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning
• SMCLM: Semantically Meaningful Causal Language Modeling for Autoregressive Paraphrase Generation
• Learning Natural Language Generation from Scratch