A Simple Dual-decoder Model for Generating Response with Sentiment
This addresses the problem of generating diverse, sentiment-controlled responses in conversational AI, though it appears incremental as it builds on existing encoder-decoder frameworks.
The paper tackles generating human-like responses with specific sentiments by proposing a dual-decoder model that connects two sentiment decoders to one encoder, and it shows significant performance gains in sentiment accuracy and word diversity over traditional single-decoder models.
How to generate human like response is one of the most challenging tasks for artificial intelligence. In a real application, after reading the same post different people might write responses with positive or negative sentiment according to their own experiences and attitudes. To simulate this procedure, we propose a simple but effective dual-decoder model to generate response with a particular sentiment, by connecting two sentiment decoders to one encoder. To support this model training, we construct a new conversation dataset with the form of (post, resp1, resp2) where two responses contain opposite sentiment. Experiment results show that our dual-decoder model can generate diverse responses with target sentiment, which obtains significant performance gain in sentiment accuracy and word diversity over the traditional single-decoder model. We will make our data and code publicly available for further study.