Personal Entity, Concept, and Named Entity Linking in Conversations
This addresses the need for conversational agents to better understand user utterances by linking to external knowledge, though it is incremental as it builds on existing entity linking methods with adaptations for conversations.
The paper tackles the problem of entity linking in conversations, where existing methods are suboptimal for personal entities and concepts, by introducing a dataset of 1327 annotated utterances and a toolkit called CREL that outperforms state-of-the-art baselines.
Building conversational agents that can have natural and knowledge-grounded interactions with humans requires understanding user utterances. Entity Linking (EL) is an effective and widely used method for understanding natural language text and connecting it to external knowledge. It is, however, shown that existing EL methods developed for annotating documents are suboptimal for conversations, where personal entities (e.g., "my cars") and concepts are essential for understanding user utterances. In this paper, we introduce a collection and a tool for entity linking in conversations. We collect EL annotations for 1327 conversational utterances, consisting of links to named entities, concepts, and personal entities. The dataset is used for training our toolkit for conversational entity linking, CREL. Unlike existing EL methods, CREL is developed to identify both named entities and concepts. It also utilizes coreference resolution techniques to identify personal entities and references to the explicit entity mentions in the conversations. We compare CREL with state-of-the-art techniques and show that it outperforms all existing baselines.