Abstract
To generate proper responses to user queries, multi-turn chatbot models should selectively consider dialogue histories. However, previous chatbot models have simply concatenated or averaged vector representations of all previous utterances without considering contextual importance. To mitigate this problem, we propose a multi-turn chatbot model in which previous utterances participate in response generation using different weights. The proposed model calculates the contextual importance of previous utterances by using an attention mechanism. In addition, we propose a training method that uses two types of Wasserstein generative adversarial networks to improve the quality of responses. In experiments with the DailyDialog dataset, the proposed model outperformed the previous state-of-the-art models based on various performance measures.
Highlights
Chatbots are computer systems that feature natural conversation with people
Given a current utterance Un and its dialogue context composed of k utterances, Un−k, . . . , Un−1, the query encoder encodes each utterance by using gated recurrent unit (GRU)
Through the wasserstein auto-encoder (WAE) process based on Wasserstein GAN (WGAN), we expect that the qualities of generated responses will be enhanced because the response decoder refers outputs of the response encoder once again
Summary
Chatbots are computer systems that feature natural conversation with people. Recently, generative chatbots that generate responses directly by the models have been developed with advances in deep learning. Based on the number of dialogue contexts that chatbots should consider to generate responses, chatbot models are divided into two categories: single-turn and multi-turn. Single-turn chatbots generate a response based on an immediately preceding utterance called a user’s query (i.e., a user’s utterance just before the response). Multi-turn chatbots generate a response based on multiple previous utterances, called a dialogue context, as well as a user’s query. Compared with Chatbot-SING, Chatbot-MULT generates the more context-aware response, “Korean foods are healthier.”, because it can look up the full dialogue history. The remainder of this paper is organized as follows: In Section 2, we review the previous studies on generative chatbots, and, we describe the proposed multi-turn chatbot model.
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