J. Scott McCarley

Also published as: J.Scott McCarley, Scott McCarley


2020

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The TechQA Dataset
Vittorio Castelli | Rishav Chakravarti | Saswati Dana | Anthony Ferritto | Radu Florian | Martin Franz | Dinesh Garg | Dinesh Khandelwal | Scott McCarley | Michael McCawley | Mohamed Nasr | Lin Pan | Cezar Pendus | John Pitrelli | Saurabh Pujar | Salim Roukos | Andrzej Sakrajda | Avi Sil | Rosario Uceda-Sosa | Todd Ward | Rong Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We introduce TECHQA, a domain-adaptation question answering dataset for the technical support domain. The TECHQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size – 600 training, 310 dev, and 490 evaluation question/answer pairs – thus reflecting the cost of creating large labeled datasets with actual data. Hence, TECHQA is meant to stimulate research in domain adaptation rather than as a resource to build QA systems from scratch. TECHQA was obtained by crawling the IBMDeveloper and DeveloperWorks forums for questions with accepted answers provided in an IBM Technote—a technical document that addresses a specific technical issue. We also release a collection of the 801,998 Technotes available on the web as of April 4, 2019 as a companion resource that can be used to learn representations of the IT domain language.

2011

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A Correction Model for Word Alignments
J. Scott McCarley | Abraham Ittycheriah | Salim Roukos | Bing Xiang | Jian-ming Xu
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2003

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TIPS: A Translingual Information Processing System
Yaser Al-Onaizan | Radu Florian | Martin Franz | Hany Hassan | Young-Suk Lee | J. Scott McCarley | Kishore Papineni | Salim Roukos | Jeffrey Sorensen | Christoph Tillmann | Todd Ward | Fei Xia
Companion Volume of the Proceedings of HLT-NAACL 2003 - Demonstrations

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Unsupervised Learning of Arabic Stemming Using a Parallel Corpus
Monica Rogati | Scott McCarley | Yiming Yang
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

1999

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Should we Translate the Documents or the Queries in Cross-language Information Retrieval?
J. Scott McCarley
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics

1998

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Fast document translation for cross-language information retrieval
J.Scott McCarley | Salim Roukos
Proceedings of the Third Conference of the Association for Machine Translation in the Americas: Technical Papers

We describe a statistical algorithm for machine translation intended to provide translations of large document collections at speeds far in excess of traditional machine translation systems, and of sufficiently high quality to perform information retrieval on the translated document collections. The model is trained from a parallel corpus and is capable of disambiguating senses of words. Information retrieval (IR) experiments on a French language dataset from a recent cross-language information retrieval evaluation yields results superior to those obtained by participants in the evaluation, and confirm the importance of word sense disambiugation in cross-language information retrieval.