KERS : A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021)

Abstract: Recommendation dialogs require the system to build a social bond with users to gain trust and develop affinity in order to increase the chance of a successful recommendation. It is beneficial to divide up, such conversations with multiple subgoals (such as social chat, question answering, recommendation, etc.), so that the system can retrieve appropriate knowledge with better accuracy under different subgoals. In this paper, we propose a unified framework for common knowledge-based multi-subgoal dialog: knowledge-enhanced multi-subgoal driven recommender system (KERS). We first predict a sequence of subgoals and use them to guide the dialog model to select knowledge from a sub-set of existing knowledge graph. We then propose three new mechanisms to filter noisy knowledge and to enhance the inclusion of cleaned knowledge in the dialog response generation process. Experiments show that our method obtains state-of-the-art results on DuRecDial dataset in both automatic and human evaluation.

## introduction recommendation dialog systems recently attract much attention due to their significant commercial potential @xcite . such systems first elicit user preferences through conversations and then provide high-quality recommendations based on elicited preferences. many real-world recommendation applications usually involve chitchat, question answering, and recommendation dialogs working together @xcite . various social interactions build rapport with users and gain trust. to provide more sociable recommendations, @xcite proposed a conversational recommendation dialog dataset durecdial annotated with 21 subgoals, where the dialog system starts the conversation with some non-recommendation subgoals, such as chitchat and question answering to collect user information and build social relationships and finally progresses into a recommendation subgoal. subgoals can be seen as different dialog phases. figure 1 shows an example dialog with multiple subgoals. all the subgoals are designed to complete the final recommendation. an rnn-based multi-goal driven conversation generation framework (mgcg) was proposed to address this task by @xcite . mgcg first models the subgoals separately to plan appropriate subgoal sequences for topic transitions and final recommendations. then mgcg extracts knowledge features from the whole knowledge graph and produces responses to complete each subgoal. however, mgcg did not investigate how to effectively use knowledge in different subgoals. as shown in figure 1 , a conversation often involves a relatively large knowledge graph and multiple subgoals. both the question answering and the recommendation processes require assistance from accurate knowledge information. therefore, having rich and accurate knowledge is essential in generating engaging conversations. since taking all possible knowledge as input will lead to more noise and high computation, how to select useful knowledge in different subgoals is important. we propose kers to use knowledge effectively in multi-subgoal conversational recommendation tasks. in order to control the flow of the conversation, we develop a dialog guidance module that predicts a sequence of subgoals and selects useful external knowledge information with respect to each subgoal to improve generation performance. in addition, we propose a sequential attention mechanism, a noise filter, and a knowledge enhancement module to make generated responses more informative. specifically, the sequential attention mechanism enhances subgoal guidance, the noise filter eliminates unrelated and unnecessary knowledge, ## related work most previous work in recommendation dialog systems focused on slot-filling methods to collect user preferences and recommend items @xcite @xcite @xcite @xcite . to study more sociable and informative recommendation conversations, @xcite al. (2018 recently, @xcite proposed utilizing subgoal sequences to plan dialog paths and presented a new recommendation dialog dataset durecdial. they demonstrated that establishing a subgoal sequence is crucial for natural transitions and successful recommendations. some previous works @xcite @xcite also introduced topic transition approaches similar to the subgoal transition to improve the quality of open-domain dialogs. they built the topic path by either traversing on a knowledge graph or predicting knowledge items directly. similar to @xcite , hayati et al. (2020) utilized sentence-level sociable recommendation strategy labels in the inspired dataset to improve the recommendation success rate. however, the inspired dataset was not annotated with specific dialog subgoals. some relevant works for our project focused on obtaining knowledge information from all the related knowledge triples @xcite , or enhancing the semantic representations by incorporating both word-oriented and entity-oriented knowledge graphs @xcite . however, our work differs because it has fine-grained knowledge planning and accurate knowledge incorporation in generation. moreover, we deal with more complex knowledge graphs, including both sentences and entities. ## experimental results we first evaluate the effectiveness of subgoal prediction and knowledge prediction. although our knowledge prediction has a relatively high accuracy of 75.6%, there are still 24.4% incorrect cases -some of them do not need knowledge, and some of them receive the wrong knowledge. the noise filter is designed to address these cases, which improves all the metrics, especially improving f1 by 3.0%. in addition, we find removing the knowledge enhancement module sharply decreases kers's dist-2. we also observe the sequential attention mechanism performs better than both the reverse attention and monolayer structure. this indicates that a reasonable attention sequence enables the model to utilize subgoals and knowledge information better. furthermore, kers has better results than kers+allknowledge, especially improving knowledge f1 by 6.3%, and only requires half of its training time. this suggests that rather than improving performance, incorporating all the knowledge introduces noise and leads to more training time. our model can filter unnecessary information and is more efficient and effective. ## human evaluation automatic metrics evaluate the model on several specific aspects, while humans can give a holistic evaluation. we conduct human evaluations on both turn level and dialog level to compare three models, kers, mgcg_g, and trans.+kg. in addition, we run a pair-wise preference test among these models. ## case study to show the models' recommendation quality, we provide some examples. as shown in finally, kers successfully recommends the movie women who know how to flirt are the luckiest starring xiaoming huang to users. compared to kers, mgcg_g recommends the inappropriate movie the bullet vanishes that is unrelated to the user's preferred star xiaoming huang. trans.+kg recommends the correct movie title but mistakenly thinks women who know how to flirt are the luckiest is a song. we can also find that without the precise control of knowledge-aware response generation, both mgcg_g and trans.+kg usually give wrong answers to questions. these observations indicate that accurate and rich knowledge is significant for the recommendation process. ## conclusions it is vital to provide an informative and appropriate recommendation process in conversational recommendation with multiple dialog types. to improve recommendation quality, we present kers to enhance the generated knowledge's accuracy and richness in responses. our model uses a dialog guidance module to provide the proper subgoals and candidate knowledge, ensuring that the model interacts with the user in a planned way. in addition, we propose three new mechanisms: a sequential attention mechanism, a noise filter, and a knowledge enhancement module in the decoder. these mechanisms work together to increase the amount and accuracy of knowledge in responses. experimental results show that kers completes various subgoals and obtains state-of-the-art results compared to previous models. in the future, we plan to further leverage knowledge graph's path to enhance natural topic transitions in dialogs.