Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph (2023)

Abstract: In the target-oriented dialogue, the representation and achievement of targets are two interrelated essential issues. In current approaches, the target is typically supposed to be a single object represented as a word, which makes it relatively easy to achieve the target through dialogue with the help of a knowledge graph (KG). However, when the target has complex semantics, the existing knowledge graph is often incomplete in tracking complex semantic relations. This paper studies target-oriented dialog where the target is a topic sentence. We combine the methods of knowledge retrieval and relationship prediction to construct a context-related dynamic KG. On dynamic KG, we can track the implicit semantic paths in the speaker’s mind that may not exist in the existing KGs. In addition, we also designed a novel metric to evaluate the tracked path automatically. The experimental results show that our method can control the agent more logically and smoothly toward the complex target.

## introduction different from the open-domain and task-oriented dialog, the target-oriented dialog is a more challenging task that aims to achieve a global target through the dialog. this process cannot be decomposed into subtasks as in a task-oriented dialogue and is excepted to be semantically coherent and effective with fewer turns. target-oriented dialog agents have a broad-based demand, e.g., psychotherapy @xcite , conversational recommendation @xcite , and education @xcite , where the agent is expected to guide the dialog to a global target, e.g., a mental state, an item, and a knowledge point, respectively. in general, the target of target-oriented dialog can be an entity (e.g., an item) or a topic (e.g., a knowledge point). the topic target is more challenging because of complex semantics, often simplified as keywords in existing works @xcite : an example of using kg path in transferring topic to a target sentence. the key phrase "learn different languages" in the context is missed in the kg, and only "translator" appears in the human transition response. this happens because some relations in the human speaker's mind do not exist in the kg. if missed concepts and relations can be completed in kg, we can link the context and target with the transition response. however, there are still issues for knowledgebased approaches to target-oriented dialogue: (1) the keywords are often ambiguous to represent complex target semantics. (2) kg knowledge is often insufficient. concepts and relations for targetoriented processes in specific dialogue are often missed in widely used common kg @xcite , which results in failed or redundant long processes. for example, in figure 1 , the key phrase "learn different languages" can reflect contextual semantics better than a single concept. but it is not a node in the kg. in addition, the critical two-hop logic used by the speaker (language-translator-job) is missed in kg, while in the alternative long path, the concepts (e.g., "english", "worker") are redundant for the response generation. (3) kg path acquisition is challenging due to the large search space. furthermore, com-plex target semantics requires more precise control over the space of knowledge selection, which is different from current works that use knowledge to enrich response generation without target restriction @xcite or only use the keyword as the target @xcite . to address these issues, in this work, we represent the target topic with a sentence instead of keywords. subsequently, instead of using a static kg, we achieve the target sentence by reasoning on a dynamic kg. before the response generation, the dynamic kg is generated based on static kg according to the dialogue context and the target sentence. this dynamic kg is expected to involve a more context-relevant and shorter path toward the target sentence. specifically, besides the node and edges in the static kg, the additional dynamic nodes include key phrases in the dialog context. a relationship prediction model predicts the additional dynamic edges. to control the space of kg path selection more reasonably, in constructing the dynamic node, we use an extended "phrases bag" and a trained model respectively to ensure the diversity and relevance of nodes in the dynamic graph. in addition, we design an automatic metric for knowledge path evaluation, considering the convergence of path semantics with the context and target semantics. our main contributions are as follows: commonsense reasoning recent approaches have realized the importance of commonsense reasoning in language generation, e.g., @xcite ) studied commonsense explanation generation. in this work, we follow the researches that utilize commonsense reasoning in generation models @xcite @xcite . @xcite select next-turn concepts from the static kg, conditioned on the dialogue context. different from this kind of knowledge retrieval method, @xcite generates implicit knowledge using a language model. @xcite combines relation classification and target prediction for generating commonsense knowledge representations over text. similarly to it, we also used the relation prediction method. still, we use it to complement the knowledge graph to obtain multi-hop paths and combined knowledge retrieval to enhance controllability. commonsense path evaluation most research that involves utilizing commonsense knowledge for tasks such as question answering @xcite and commonsense reasoning @xcite tend to use paths extracted from static knowledge graphs. however, the effectiveness of the knowledge paths is evaluated indirectly through the performance of downstream tasks in these work. @xcite automatically evaluates the knowledge path through the similarity with the implicit knowledge in the dataset. still, this method only works when annotated golden paths are provided in the dataset. we will address this issue with a novel automatic evaluation metric based on the semantic connection between dialogues and paths. ## conclusion for effectively guiding the dialog to a target sentence, we propose to make full use of discontinuous or even non-existent paths in the knowledge graph. we combine knowledge retrieval and relationship prediction by building a dynamic kg, which helps to obtain a path closer to the implicit logic in the speaker's utterances when transferring topics to the target sentence. in addition, we also designed an automatic metric to evaluate the quality of the knowledge path for semantic transfer. both automatic and human evaluation verify the superiority of the proposed method in searching knowledge paths and subsequently generating transition responses compared with sota baselines, benefiting from the better logic and contextual relevance of the paths from the dynamic graph. in the future, we will explore the application of our method to multi-turn target-oriented dialogue.