Knowledge-grounded Dialog State Tracking (2022)

Abstract: Knowledge (including structured knowledge such as schema and ontology and unstructured knowledge such as web corpus) is a critical part of dialog understanding, especially for unseen tasks and domains. Traditionally, such domain-specific knowledge is encoded implicitly into model parameters for the execution of downstream tasks, which makes training inefficient. In addition , such models are not easily transferable to new tasks with different schemas. In this work, we propose to perform dialog state tracking grounded on knowledge encoded externally. We query relevant knowledge of various forms based on the dialog context where such information can grounds the prediction of dialog states. We demonstrate superior performance of our proposed method over strong baselines, especially in the few-shot learning setting.

## introduction pre-trained language models (lms, @xcite are the backbone of contemporary task-oriented dialog (tod) models @xcite . however, the models are pre-trained on large generic corpora so that they do not contain task-specific knowledge. previous work primarily suggests further pre-training or fine-tuning the lms on in-domain data for adaptation @xcite , but it cannot consider information above the surface level. this makes it challenging for downstream tasks especially in the few-shot learning setting because mapping representation to the output space and encoding knowledge into the model parameters are entangled, while the latter may require more training data. some more recent research proposes to incorporate external knowledge for response generation tasks @xcite @xcite 2022; * work done while at university of california, davis encoder decoder attraction-location: west; hotel-price range: moderate; hotel-parking: free [context] … user: can i also book a not expensive hotel with free parking? moderate: hotel-price range yes: hotel-parking moderate: restaurant-price range … top1 top2 top7 mips … yes: hotel-parking … moderate: restaurant-price range moderate: hotel-price range [context] user: can i also book a not expensive hotel with free parking? kg embeddings query embeddings 1 3 4 moderate: hotel-price range yes: hotel-parking moderate: restaurant-price range … 2 figure 1 : model architecture for our proposed knowledge-grounded dst. the encoder first encodes the query and knowledge into representations, and we find the top-k most relevant knowledge elements to the context in step 2. we flatten the retrieved elements in step 3 and append to the query context as the input to the encoder-decoder model. the retrieved elements serve as a prior for dst. @xcite , but it is not clear how to utilize such information for language understanding. in tod settings, because the api call structure is restricted to certain intents, slots, and values, the schema is often provided. for example, in a flight booking system, queries like departure location and airlines are pre-defined. users, even though not bounded directly by what they can say to agents, have a limited and predictable vocabulary set to some extent. if the schema information is utilized, a model does not need to learn that "san francisco" represents a departure place, rather than a general city name from the lm. this is particularly important for new information, such as movie titles or locations that do not appear in the lm training corpus. similar to human annotators, grounding a dialog model on such knowledge makes it easier and more accurate in understanding conversations. in this paper, we investigate knowledge-grounded understanding for dialog state tracking (dst). in addition to using structured knowledge such as the ontology of slot type-value pairs, we also consider unstructured knowledge from the raw training data. we train a tod model to query relevant knowledge for each turn in the context, and leverage the retrieved knowledge to predict dialog state. we evaluate our method on multiwoz @xcite for both the full-data and few-shot settings, and show superior performance compared to previous methods. ## methodology our proposed method is illustrated in figure 1 . given the context x, we first retrieve k relevant knowledge entries e by the similarity between enc(x) and enc(e) using an encoder enc. then we integrate the retrieved entries e 1 , e 2 , ..., e k with the original context to form x , where x is used as the input for the target dst task. knowledge retrieval different from previous work (such as question answering) where there is only one ground-truth knowledge for each query, multiple entries of the form slot-value pairs may exist in the ontology base that match the conversation context. importantly, unlike passage retrieval where the query (e.g., a sentence) and the target (e.g., another sentence or passage) are similar to the pre-training corpus, structured knowledge such as schema pairs may have different representation distribution. thus, an off-the-shelf encoder may retrieve noisy elements and degrade final performance, especially when training with the target task optimized on dst generation. moreover, nonparametric retrieval methods such as tf-idf and bm25 @xcite rely on lexical overlapping, which could be detrimental when entries in schemas contain high word overlapping (e.g., same value for different slots). we therefore train our knowledge retriever to promote similar representations between a query and its ground truth knowledge. we started with optimizing the marginal likelihood over all positive knowledge entries, but found that it resulted in peaky distribution centered around specific elements in our preliminary studies. instead, we mini-mize binary cross-entropy with contrastive loss: knowledge integration once most relevant knowledge elements are retrieved by the model, this extra information can serve as a strong inductive bias to the downstream, knowledge-sensitive tasks. one common approach for knowledge integration is fusion-in-decoder @xcite . although efficient, it has been shown that retrieved information is likely to be ignored by a pre-trained model @xcite . hence, we concatenate retrieved knowledge with the context @xmath0, e k-1 , ..., e 1 , x, where the entries are ordered from the least similar (e k ) to the most similar (e 1 ). the similarity can also be considered as the confidence an element e i is relevant to the current context. we take the x as the context to the dst task. therefore, our method is unified for knowledge of any format, and a bounded number of elements can solve the problem of memory constraint in previous research @xcite . ## conclusion in this paper, we propose to disentangle domain knowledge and encode knowledge as a prior to dialog state tracking. compared to previous research of grounding on knowledge for factual generation, our method can be applied to multiple sources of knowledge in the task-oriented dialog understanding setting. we conduct experiments on the multi-woz dataset and show superior performance especially in the few-shot learning setting. we plan to apply our method on more general natural language understanding tasks in the future.