Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs (2022)

Abstract: Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging. Existing models treat knowledge selection as a sentence ranking or classification problem where each sentence is handled individually, ignoring the internal semantic connection between sentences. In this work, we propose to automatically convert the background knowledge documents into document semantic graphs and then perform knowledge selection over such graphs. Our document semantic graphs preserve sentence-level information through the use of sentence nodes and provide concept connections between sentences. We apply multi-task learning to perform sentence-level knowledge selection and concept-level knowledge selection, showing that it improves sentence-level selection. Our experiments show that our semantic graph-based knowledge selection improves over sentence selection baselines for both the knowledge selection task and the end-to-end response generation task on HollE and improves generalization on unseen topics in WoW.

## introduction natural language generation models have seen great success in their ability to hold open-domain dialogues without the need for manual injection of domain knowledge. however, such models often degenerate to uninteresting and repetitive responses @xcite , or hallucinate false knowledge @xcite . to avoid such phenomena, one solution * work done as an intern at amazon alexa ai. 1 see https://www.amazon.science/publica tions/enhanced-knowledge-selection-for-g rounded-dialogues-via-document-semanticgraphsfor an updated paper with information about code and resources. relevant knowledge is often presented in the form of documents @xcite @xcite @xcite and the task of identifying the appropriate knowledge snippet for each turn is formulated as a sentence classification or ranking task @xcite . although more advanced methods have been proposed by modeling knowledge as a latent variable @xcite , or tracking topic shift @xcite , they abide by the setting of sentencelevel selection. this setting has two inherent drawbacks: (1) it ignores the semantic connections between sentences and (2) it imposes an artificial constraint over the knowledge boundary. a document is not simply a bag of sentences, in fact, it is the underlying semantic connections and structures that make the composition of sentences meaningful. two examples of such connections are coreference links and predicate-argument structures. 2 these connections are vital to the understanding of the document and also beneficial to knowledge selection. in many cases, we can draw information from multiple sentences to create the response, breaking the knowledge boundary. for instance, in figure 2 , the connections among the character "rango", the plot point "water shortage" and the name "django" help us generate a response with a smooth topic transition. a related line of work @xcite @xcite @xcite ) that seemingly overcomes the aforementioned issues is knowledge selection from existing knowledge graphs (kgs) such as what do you think about the movie? i liked the set up for the jaguar shark i wasn't fond of the shark. the sole purpose of the movie was to hunt the shark, but in the process you are also emotionally connected with other characters. this movie did a great job reaching into the viewer and pulling out emotions. wes anderson deserves major credit for this. indeed, it is a great treat! plot: while making a documentary, oceanographer steve zissou loses his dear friend to a jaguar shark. he raises funds for an expedition in his ship wikidata @xcite , dbpedia @xcite , and conceptnet @xcite . if the character "rango" were in the kg, it would have been represented as an entity node and be connected to respective events. on the kg, we are also free to select as many concepts as needed, without being restricted to a single sentence as the source. however, kgs are known to have limited coverage of real world entities, let alone emerging entities in works of fiction such as books and movies @xcite . hence, to bridge these two worlds of sentencebased knowledge selection and kg-based knowledge selection, we introduce knowledge selection using document semantic graphs. these graphs are automatically constructed from documents, aiming to preserve the document content while enhancing the document representation with semantic connections. to create such a document semantic graph, we first obtain the abstract meaning representation (amr) @xcite for each sentence. amr detects entities, captures predicateargument structures, and provides a layer of abstraction from words to concepts. 3 compared to existing knowledge base construction methods, amr covers a wide range of relations and fine-grained se-mantic roles, and can fully reflect the semantics of the source text. since amr graphs only represent single sentences, we utilize coreference resolution tools to detect coreferential entity nodes and merge them to build graphs for documents. on top of this content representation, we also add sentence nodes and passage nodes to reflect the structure of the document. this allows for traversal across the graph by narrative order or concept association. given the document semantic graph, knowledge selection can be seen as identifying relevant nodes on the graph, sentence nodes or concept nodes. as knowledge selection in dialog models is conditioned on the dialog context, for each dialog turn, we create a dialog-aware graph derived from the document graph. it contains context nodes representing contextualized versions of the sentence and concept nodes. we design an edge-aware graph neural network model to propagate information along the dialog-aware graph and finally score the context nodes (or concept nodes) on their relevance to the dialog turn (as shown in figure 4 ). we validate our model on two widely used datasets holle @xcite and wizard of wikipedia @xcite by constructing a semantic graph from relevant background documents foot_2 for each dialog. the use of document graphs improves both knowledge selection and response generation quality on holle and boosts generalization to unseen topics for wow. from our ablation tests we find that in terms of the graph structure, the key component is the use of coreference edges that stitches sentences together. our contributions include: (1) we propose to perform knowledge selection from document semantic graphs that are automatically constructed from source documents and can reflect the implicit semantic connections between sentences without being limited to a pre-defined set of entities and relations as kgs do. our approach bridges the gap between sentence-based knowledge selection and kg-based knowledge selection. (2) we show that joint selection over sentences and concepts can model more complex relations between sentences and boost sentence selection performance. (3) we build a pipeline for converting documents (document collections) into semantic graphs through the use of amr parsing and coreference. we hope that knowledge source dialog context semantic graph selected knowledge 1. the reserves in the vault only has enough water for five more days. 2. the name was a play on the classic western django. yeah, there was only water enough for five days. i also liked how his name is a play on django. i liked rango as well! i mean, he did try to keep the town from the water shortage. knowledge selection response generation document semantic graph construction rango django water shortage louise 5 days figure 2: the pipeline for generating responses based on a given knowledge source. our tool can facilitate future work on graph-based representations of documents. ## method we show an overview of our knowledge-grounded dialog system in figure 2 . the system consists of three modules, namely semantic graph construction, knowledge selection and response generation. ## conclusion and future work in this paper, we introduce document semantic graphs for knowledge selection. compared to existing document-based knowledge selection methods that typically treat sentences independently, our automatically-constructed document semantic graphs explicitly represent the semantic connections between sentences while preserving sentencelevel information. our experiments demonstrate that our semantic graph-based approach shows advantages over various sentence selection baselines in both the knowledge selection task and the endto-end response generation task.