Rik Koncel-Kedziorski
5 papers in the PaperMetrix corpus
Papers by this author
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DeFINE: DEep Factorized INput Token Embeddings for Neural Sequence Modeling
2019 · arXiv (Cornell University)
For sequence models with large vocabularies, a majority of network parameters lie in the input and output layers. In this work, we describe a new method, DeFINE, for learning deep token representations efficiently. Our architecture …
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Scarecrow: A Framework for Scrutinizing Machine Text.
2021 · arXiv (Cornell University)
Modern neural text generation systems can produce remarkably fluent and grammatical texts. While earlier language models suffered from repetition and syntactic errors, the errors made by contemporary models are often semantic, narrative, or discourse failures. …
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Parsing Algebraic Word Problems into Equations
2015 · Transactions of the Association for Computational Linguistics
This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local …
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MAWPS: A Math Word Problem Repository
2016
Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, Hannaneh Hajishirzi. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.
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A Controllable Model of Grounded Response Generation
2021 · Proceedings of the AAAI Conference on Artificial Intelligence
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses. Attempts to boost informativeness alone come at the expense of factual accuracy, …