Resolving Indirect Referring Expressions for Entity Selection
This work addresses a problem for improving naturalness in dialog, recommendation, and search systems by enabling robust understanding of indirect language, though it is incremental as it builds on existing language modeling advances.
The paper tackled the problem of resolving indirect referring expressions for entity selection, such as interpreting 'the green one' when choosing between two cakes, by creating the AltEntities dataset of 42K entity pairs and expressions. They developed models that achieved 82%-87% accuracy in realistic settings, showing reasonable performance but highlighting room for improvement.
Recent advances in language modeling have enabled new conversational systems. In particular, it is often desirable for people to make choices among specified options when using such systems. We address this problem of reference resolution, when people use natural expressions to choose between the entities. For example, given the choice `Should we make a Simnel cake or a Pandan cake?' a natural response from a dialog participant may be indirect: `let's make the green one'. Such natural expressions have been little studied for reference resolution. We argue that robustly understanding such language has large potential for improving naturalness in dialog, recommendation, and search systems. We create AltEntities (Alternative Entities), a new public dataset of 42K entity pairs and expressions (referring to one entity in the pair), and develop models for the disambiguation problem. Consisting of indirect referring expressions across three domains, our corpus enables for the first time the study of how language models can be adapted to this task. We find they achieve 82%-87% accuracy in realistic settings, which while reasonable also invites further advances.