Steve DeNeefe


AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents
Melissa Roemmele | Deep Sidhpura | Steve DeNeefe | Ling Tsou
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

One strategy for facilitating reading comprehension is to present information in a question-and-answer format. We demo a system that integrates the tasks of question answering (QA) and question generation (QG) in order to produce Q&A items that convey the content of multi-paragraph documents. We report some experiments for QA and QG that yield improvements on both tasks, and assess how they interact to produce a list of Q&A items for a text. The demo is accessible at


Two Easy Improvements to Lexical Weighting
David Chiang | Steve DeNeefe | Michael Pust
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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A Decoder for Probabilistic Synchronous Tree Insertion Grammars
Steve DeNeefe | Kevin Knight | Heiko Vogler
Proceedings of the 2010 Workshop on Applications of Tree Automata in Natural Language Processing


Synchronous Tree Adjoining Machine Translation
Steve DeNeefe | Kevin Knight
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing


Overcoming Vocabulary Sparsity in MT Using Lattices
Steve DeNeefe | Ulf Hermjakob | Kevin Knight
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers

Source languages with complex word-formation rules present a challenge for statistical machine translation (SMT). In this paper, we take on three facets of this challenge: (1) common stems are fragmented into many different forms in training data, (2) rare and unknown words are frequent in test data, and (3) spelling variation creates additional sparseness problems. We present a novel, lightweight technique for dealing with this fragmentation, based on bilingual data, and we also present a combination of linguistic and statistical techniques for dealing with rare and unknown words. Taking these techniques together, we demonstrate +1.3 and +1.6 BLEU increases on top of strong baselines for Arabic-English machine translation.

Decomposability of Translation Metrics for Improved Evaluation and Efficient Algorithms
David Chiang | Steve DeNeefe | Yee Seng Chan | Hwee Tou Ng
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing


What Can Syntax-Based MT Learn from Phrase-Based MT?
Steve DeNeefe | Kevin Knight | Wei Wang | Daniel Marcu
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)


Scalable Inference and Training of Context-Rich Syntactic Translation Models
Michel Galley | Jonathan Graehl | Kevin Knight | Daniel Marcu | Steve DeNeefe | Wei Wang | Ignacio Thayer
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics


Interactively Exploring a Machine Translation Model
Steve DeNeefe | Kevin Knight | Hayward H. Chan
Proceedings of the ACL Interactive Poster and Demonstration Sessions

ISI’s 2005 Statistical Machine Translation Entries
Steve DeNeefe | Kevin Knight
Proceedings of the Second International Workshop on Spoken Language Translation