Learning to Match Representations is Better for End-to-End Task-Oriented Dialog System (2024)

Abstract: Due to the rapid development with pre-trained language models, fully end-to-end Task-Oriented Dialogue (TOD) systems exhibit superior performance. How to achieve the ability to efficiently retrieve entities in cross-domain large-scale databases is a key issue. Most existing end-to-end Task-Oriented Dialogue systems suffer from the following problems: The ability to handle erroneous but easily confused entities needs to be improved; Matching information between contexts and entities is not captured, leading to weak modeling of domain-invariant and interpretable features, making it difficult to generalize to unseen domains. In this paper, we propose a method for knowledge retrieval driven by matching representations. The approach consists of a matching signal extractor for extracting matching representations between contexts and entities that have generic conceptual features and hence domain invariant properties, and an Attribute Filter for filtering irrelevant information to facilitate the re-selection of entities. Experiments on three standard benchmarks at the dialogue level and on large knowledge bases show that our retriever performs knowledge retrieval more efficiently than existing approaches.

## introduction task-oriented dialogue systems @xcite are designed to help users complete certain specific tasks, such as table booking, hotel booking, ticket booking, and online shopping. traditional taskoriented dialogue systems are developed through dialogue state tracking @xcite , dialogue strategies @xcite and natural language generation @xcite . these modules require annotations to train and need to generate belief states to query the database to generate response. in contrast, fully end-to-end task-oriented dialog system directly encodes kb and uses a neural network to query the kb in a differentiable manner, and it can directly generate system response given only dialogue history and the corresponding kb. although the end-to-end paradigm gains increasing attention, retrieving the correct knowledge from external databases becomes a key limiting factor for the performance of such models due to the lack of belief states as supervisory signals. existing end-to-end tod systems can be divided into two categories based on the relationship between knowledge retrieval and corresponding generation. the first class of approaches typically integrates the processes of knowledge retrieval and response generation and trains them under the supervision of reference responses @xcite @xcite @xcite @xcite .the second class of approaches decouples knowledge retrieval from the corresponding generation and explicitly extracts the supervisory signals from the response generation to improve the retrieval process, which alleviates the above problem to some extent. q-tod @xcite extracts the essential information from the dialogue context into a query, which is further employed to retrieve relevant knowledge records for response generation. however, although these methods are able to provide supervisory signals for retrieval, they only use rough dot product interactions to compute the similarity when filtering the candidate entities for generation, which is vulnerable to receiving interference from irrelevant attributes and has poor domain generalization, as shown in fig. 1 . whereas the information extracted by response generation originally comes from the candidate entities, the effectiveness of this supervisory signal is therefore closely related to the accuracy of the candidate entities' input to the response generator. hello, i am looking for an expensive restaurant on the south side of town. peking restaurant is in the south part, and the price range is expensive. can i help you with anything else? kb name food address postcode area pricerange 1 frankie and bennys italian cambridge leisure park clifton way cherry hinton cb17dy south expensive 2 peking restaurant chinese 10 homerton street city centre cb28nx south expensive 3 taj tandoori indian 64 cherry hinton road cherry hinton cb17aa south expensive retrieved entities what is the address for peking restaurant? peking restaurant address is cambridge leisure park clifton way cherry hinton. ## method as shown in fig. 2 , our system first retrieves an initial rough batch of candidate entities from the knowledge base. then, the matching signal extractor performs global information decentralization and local information sharpening on the retrieved candidate entities to extract the relevant informa-tion of their attributes. then, the entity enters the attribute filter to eliminate irrelevant attributes. after filtering out the irrelevant attributes, the entities are reordered to select the top-k candidate entities. these candidate entities along with the dialogue context are then fed into the generator model to generate replies. ## conclusion we propose a knowledge retrieval method that is sensitive to matching attributes to improve general-isation and interpretability by capturing matching information. matching information can be used to filter mismatched parts of entities to improve entity selection. we first filter a batch of candidate entities by calculating the rough similarity between the context and the entity, and then calculate the match representation between the context and the entity and compute its match score with each attribute in the entity to obtain the relevance of the attribute to the context. based on this relevance score, we filter out irrelevant attributes and recalculate the entities used to generate the response. we compare our system with other systems on three benchmark datasets and the results show that our retriever performs knowledge retrieval more effectively than existing methods.