Todd Shore


2018

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KTH Tangrams: A Dataset for Research on Alignment and Conceptual Pacts in Task-Oriented Dialogue
Todd Shore | Theofronia Androulakaki | Gabriel Skantze
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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FARMI: A FrAmework for Recording Multi-Modal Interactions
Patrik Jonell | Mattias Bystedt | Per Fallgren | Dimosthenis Kontogiorgos | José Lopes | Zofia Malisz | Samuel Mascarenhas | Catharine Oertel | Eran Raveh | Todd Shore
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Using Lexical Alignment and Referring Ability to Address Data Sparsity in Situated Dialog Reference Resolution
Todd Shore | Gabriel Skantze
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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-as-classifiers 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.