Construction of Hierarchical Structured Knowledge-based Recommendation Dialogue Dataset and Dialogue System (2022)

Abstract: We work on a recommendation dialogue system to help a user understand the appealing points of some target (e.g., a movie). In such dialogues, the recommendation system needs to utilize structured external knowledge to make informative and detailed recommendations. However, there is no dialogue dataset with structured external knowledge designed to make detailed recommendations for the target. Therefore, we construct a dialogue dataset, Japanese Movie Recommendation Dialogue (JMRD), in which the recommender recommends one movie in a long dialogue (23 turns on average). The external knowledge used in this dataset is hierarchically structured, including title, casts, reviews, and plots. Every recommender’s utterance is associated with the external knowledge related to the utterance. We then create a movie recommendation dialogue system that considers the structure of the external knowledge and the history of the knowledge used. Experimental results show that the proposed model is superior in knowledge selection to the baseline models.

## introduction in recent years, research on recommendation dialogue systems, which systems recommend something to users through dialogues, has attracted much attention. here, we focus on movie recommendations. a recommendation dialogue consists of two phases: (1) the user's preferences are elicited, and a movie is selected from several candidates, and (2) in-depth information is provided for the selected movie. we focus on the latter phase in this study. to provide in-depth information, the use of external knowledge is crucial. there has been much research on incorporating external knowledge in dialogue, and many kinds of knowledge-grounded dialogue datasets have been proposed @xcite . these datasets often use plain texts or knowledge graphs as external knowledge. if the hierarchically structured knowledge is available in recommendation dialogues, it allows for more appropriate knowledge selection and informative response generation. however, there is no dialogue dataset with hierarchically structured knowledge to provide rich information for a single target (e.g., a movie). to address the aforementioned problem, we propose a dialogue dataset, japanese movie recommendation dialogue (jmrd), in which recommendation dialogues are paired with the corresponding external knowledge. this dialogue dataset consists of about 5,200 dialogues between crowd workers. each dialogue has 23 turns on average. we can say that our dataset provides in-depth movie recommendations utilizing various knowledge about a movie, with relatively a large number of dialogue turns. specifically, as shown in figure 1 , one speaker (recommender) recommends a movie to the other speaker (seeker). only the recommenders can have access to the knowledge about the movie, and they should use the external knowledge as much as possible in their utterances. the recommenders are asked to annotate the knowledge they used when sending their utterance. this procedure enables us to associate every recommenders' utterances with the corresponding external knowledge. the external knowledge is hierarchically structured into knowledge types common to all movies (e.g., "title", "released year") and giving knowledge contents for each movie (e.g., "rise of planet of the apes", " @xcite . we also propose a strong baseline model for the constructed dataset. this model considers the history of knowledge types/contents, noting that the order in which each piece of knowledge is used is essential in recommendation dialogues. the exper-imental results show that our proposed model can select appropriate knowledge with higher accuracy than the baseline method. our contributions are three-fold. ## related work recommendation dialogue has long attracted attention. however, most of them are goal-oriented dialogues in which the user's preferences are elicited from multiple recommendation candidates, and a recommendation target is decided according to that preferences @xcite . @xcite propose redial, a human-tohuman movie recommendation dialogue dataset. the recommender presents several movies in one dialogue while inquiring about the seeker's preferences. @xcite collect gorecdial dataset in a gamified setting where experts decide on a movie similar to the seekers' preference among a small set of movies (= five movies) in a minimal number of turns. opendialkg @xcite is a recommendation and chit-chat dataset linking open-ended dialogues to knowledge graphs. in this study, we focus on the recommendation dialogue, which provides in-depth information about a movie rather than deciding which movie to recommend. research on the knowledge-grounded dialogue has also been growing in the last few years. @xcite collect a human-to-human chit-chat dialogue dataset by utilizing wikipedia articles of 30 famous movies. this dataset is unique in that it has two dialogue settings: either only one of the participants can see the knowledge, or both of them can see it. @xcite also collect chit-chat dialogues about movies based on multiple types of knowledge: plot, review, reddit comments, and fact table. wizard of wikipedia @xcite is an open-domain chit-chat dialogue dataset based on wikipedia articles on 1,365 topics. it has become a standard benchmark in this research field. @xcite collect a large chinese chit-chat dialogue dataset (246,141 dialogues with 3,010,650 turns) about movies. other dialogue datasets with external knowledge in chinese are duconv @xcite , kdconv @xcite , and durecdial @xcite . duconv @xcite combines dialogues with knowledge graphs to track the progress of the dialogue topic. kdconv @xcite is also a chit-chat dialogue corpus that consists of relatively long dialogues to allow deep discussions in multiple domains (movies, music, and travel). @xcite focus on multiple dialogue types (e.g., qa, chit-chat, recommendation) and collect a multi-domain dialogue dataset associated with a knowledge graph. compared to these studies, our work differs in that it uses hierarchically structured knowledge that contains both factoid (e.g., title) and non-factoid (e.g., review) information to make recommendations. ## japanese movie recommendation dialogue we choose movies as the domain for the recommendation dialogue because movies are interesting to everyone and facilitate smooth dialogue. in addition, movie recommendation dialogue is opendomain in nature according to the variety of movie topics, and it is a preferable property for nlp research. in this section, we explain the construction method of the jmrd. ## conclusion we proposed jmrd, a hierarchically structured knowledge-based movie recommendation dialogue dataset. we also proposed an end-to-end dialogue system that utilizes the hierarchically structured knowledge of knowledge types and contents to perform knowledge selection and generate responses as a strong baseline for our dataset. the experimental results show that our model can select more appropriate knowledge than baselines. as far as we know, this is the first japanese dialogue dataset associated with external knowledge. we hope our dataset facilitates further research on movie recommendation dialogue based on structured external knowledge (especially in japanese dialogue research). in response generation, we can observe that the utterances do not reflect the knowledge in some cases, even when the knowledge is selected correctly. there is still much room for improvement in knowledge reflection, and we leave this as future work.