A conversation is based on internal knowledge that the participants already know or external knowledge that they have gained during the conversation. A chatbot that communicates with humans by using its internal and external knowledge is called a knowledge-grounded chatbot. Although previous studies on knowledge-grounded chatbots have achieved reasonable performance, they may still generate unsuitable responses that are not associated with the given knowledge. To address this problem, we propose a knowledge-grounded chatbot model that effectively reflects the dialogue context and given knowledge by using well-designed attention mechanisms. The proposed model uses three kinds of attention: Query-context attention, query-knowledge attention, and context-knowledge attention. In our experiments with the Wizard-of-Wikipedia dataset, the proposed model showed better performances than the state-of-the-art model in a variety of measures.

A major challenge in education is to provide students with a personalized learning experience. This study aims to address this by developing a dialogue-based intelligent tutoring system (ITS) that imitates human expert tutors. The ITS asks questions, assesses student answers, provides hints, and even chats to encourage student engagement. We constructed the Dialogue-based Reading Comprehension Tutoring (DIRECT) dataset to simulate real-world pedagogical scenarios with the assessment labels and key sentences to support tutoring. The DIRECT dataset is based on RACE, which is a large-scale English reading comprehension dataset. In addition, we propose a neural pipeline approach to model the tutoring tasks and conduct a comprehensive analysis on the results, including a human evaluation. The results show that our model performs well in generating questions, assessing answers, and chatting, showing high potential although some challenges remain. The proposed model provides a good basis for further development of dialogue-based ITSs. The dataset will be publicly available after the publication.

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.