Markov Logic Networks for Spatial Language in Reference Resolution

This paper presents an approach for automatically learning reference resolution, which involves using natural language expressions, including spatial language descriptions, to refer to an object in a context. This is useful in conversational systems that need to understand the context of an utterance, like multi-modal or embodied dialogue systems. Markov Logic Networks are explored as a way of jointly inferring a reference object from an utterance with some simple utterance structure, and properties from the real world context. An introduction to mlns, with a small example, is given. Reference resolution and the role of spatial language are introduced. Different aspects of combining an utterance with properties of a context are explored. It is concluded that mlns are promising in resolving a contextual reference object.