Referring to entities in situated dialog is a collaborative process, whereby interlocutors often expand, repair and/or replace referring expressions in an iterative process, converging on conceptual pacts of referring language use in doing so.Nevertheless, much work on exophoric reference resolution (i.e.resolution of references to entities outside of a given text) follows a literary model, whereby individual referring expressions are interpreted as unique identifiers of their referents given the state of the dialog the referring expression is initiated.In this paper, we address this collaborative nature to improve dialogic reference resolution in two ways: First, we trained a words-asclassifiers logistic regression model of word semantics and incrementally adapt the model to idiosyncratic language between dyad partners during evaluation of the dialog.We then used these semantic models to learn the general referring ability of each word, which is independent of referent features.These methods facilitate accurate automatic reference resolution in situated dialog without annotation of referring expressions, even with little background data.

Recent advances in automatic speech recognition (ASR) technology continue to be based heavily on data-driven methods, meaning that the full benefits of such research are often not enjoyed in domains for which there is little training data. Moreover, tractability is often an issue with these methods when conditioning for long-distance dependencies, entailing that many higher-level knowledge sources such as situational knowledge cannot be easily utilized in classification. This paper describes an effort to circumvent this problem by using dynamic contextual knowledge to rescore ASR lattice output using a dynamic weighted constraint satisfaction function. With this method, it was possible to achieve a roughly 80 % reduction in WER for ASR in the context of an air traffic control scenario. Index Terms: lattice rescoring, knowledge-based, contextsensitivity 1.