Scott Martin


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MuDoCo: Corpus for Multidomain Coreference Resolution and Referring Expression Generation
Scott Martin | Shivani Poddar | Kartikeya Upasani
Proceedings of the Twelfth Language Resources and Evaluation Conference

This paper proposes a new dataset, MuDoCo, composed of authored dialogs between a fictional user and a system who are given tasks to perform within six task domains. These dialogs are given rich linguistic annotations by expert linguists for several types of reference mentions and named entity mentions, either of which can span multiple words, as well as for coreference links between mentions. The dialogs sometimes cross and blend domains, and the users exhibit complex task switching behavior such as re-initiating a previous task in the dialog by referencing the entities within it. The dataset contains a total of 8,429 dialogs with an average of 5.36 turns per dialog. We are releasing this dataset to encourage research in the field of coreference resolution, referring expression generation and identification within realistic, deep dialogs involving multiple domains. To demonstrate its utility, we also propose two baseline models for the downstream tasks: coreference resolution and referring expression generation.


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A Joint Phrasal and Dependency Model for Paraphrase Alignment
Kapil Thadani | Scott Martin | Michael White
Proceedings of COLING 2012: Posters


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Creating Disjunctive Logical Forms from Aligned Sentences for Grammar-Based Paraphrase Generation
Scott Martin | Michael White
Proceedings of the Workshop on Monolingual Text-To-Text Generation


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Grammar Engineering for CCG using Ant and XSLT
Scott Martin | Rajakrishnan Rajkumar | Michael White
Proceedings of the Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing (SETQA-NLP 2009)


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Towards broad coverage surface realization with CCG
Michael White | Rajakrishnan Rajkumar | Scott Martin
Proceedings of the Workshop on Using corpora for natural language generation