Burcu Karagol-Ayan

Also published as: Burcu Karagol Ayan


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Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering
Najoung Kim | Ellie Pavlick | Burcu Karagol Ayan | Deepak Ramachandran
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Many Question-Answering (QA) datasets contain unanswerable questions, but their treatment in QA systems remains primitive. Our analysis of the Natural Questions (Kwiatkowski et al. 2019) dataset reveals that a substantial portion of unanswerable questions (~21%) can be explained based on the presence of unverifiable presuppositions. Through a user preference study, we demonstrate that the oracle behavior of our proposed system—which provides responses based on presupposition failure—is preferred over the oracle behavior of existing QA systems. Then, we present a novel framework for implementing such a system in three steps: presupposition generation, presupposition verification, and explanation generation, reporting progress on each. Finally, we show that a simple modification of adding presuppositions and their verifiability to the input of a competitive end-to-end QA system yields modest gains in QA performance and unanswerability detection, demonstrating the promise of our approach.


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Text Classification with Few Examples using Controlled Generalization
Abhijit Mahabal | Jason Baldridge | Burcu Karagol Ayan | Vincent Perot | Dan Roth
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Training data for text classification is often limited in practice, especially for applications with many output classes or involving many related classification problems. This means classifiers must generalize from limited evidence, but the manner and extent of generalization is task dependent. Current practice primarily relies on pre-trained word embeddings to map words unseen in training to similar seen ones. Unfortunately, this squishes many components of meaning into highly restricted capacity. Our alternative begins with sparse pre-trained representations derived from unlabeled parsed corpora; based on the available training data, we select features that offers the relevant generalizations. This produces task-specific semantic vectors; here, we show that a feed-forward network over these vectors is especially effective in low-data scenarios, compared to existing state-of-the-art methods. By further pairing this network with a convolutional neural network, we keep this edge in low data scenarios and remain competitive when using full training sets.


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Morphology Induction from Limited Noisy Data Using Approximate String Matching
Burcu Karagol-Ayan | David Doermann | Amy Weinberg
Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology at HLT-NAACL 2006

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Adaptive Transformation-Based Learning for Improving Dictionary Tagging
Burcu Karagol-Ayan | David Doermann | Amy Weinberg
11th Conference of the European Chapter of the Association for Computational Linguistics


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Acquisition of bilingual MT lexicons from OCRed dictionaries
Burcu Karagol-Ayan | David Doermann | Bonnie J. Dorr
Proceedings of Machine Translation Summit IX: Papers

This paper describes an approach to analyzing the lexical structure of OCRed bilingual dictionaries to construct resources suited for machine translation of low-density languages, where online resources are limited. A rule-based, an HMM-based, and a post-processed HMM-based method are used for rapid construction of MT lexicons based on systematic structural clues provided in the original dictionary. We evaluate the effectiveness of our techniques, concluding that: (1) the rule-based method performs better with dictionaries where the font is not an important distinguishing feature for determining information types; (2) the post-processed stochastic method improves the results of the stochastic method for phrasal entries; and (3) Our resulting bilingual lexicons are comprehensive enough to provide the basis for reasonable translation results when compared to human translations.