Jeffrey Flanigan


2021

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Avoiding Overlap in Data Augmentation for AMR-to-Text Generation
Wenchao Du | Jeffrey Flanigan
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Leveraging additional unlabeled data to boost model performance is common practice in machine learning and natural language processing. For generation tasks, if there is overlap between the additional data and the target text evaluation data, then training on the additional data is training on answers of the test set. This leads to overly-inflated scores with the additional data compared to real-world testing scenarios and problems when comparing models. We study the AMR dataset and Gigaword, which is popularly used for improving AMR-to-text generators, and find significant overlap between Gigaword and a subset of the AMR dataset. We propose methods for excluding parts of Gigaword to remove this overlap, and show that our approach leads to a more realistic evaluation of the task of AMR-to-text generation. Going forward, we give simple best-practice recommendations for leveraging additional data in AMR-to-text generation.

2019

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The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Sheshera Mysore | Zachary Jensen | Edward Kim | Kevin Huang | Haw-Shiuan Chang | Emma Strubell | Jeffrey Flanigan | Andrew McCallum | Elsa Olivetti
Proceedings of the 13th Linguistic Annotation Workshop

Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures would facilitate deeper scientific understanding of materials synthesis and enable automated synthesis planning. Such analysis requires extracting structured representations of synthesis procedures from the raw text as a first step. To facilitate the training and evaluation of synthesis extraction models, we introduce a dataset of 230 synthesis procedures annotated by domain experts with labeled graphs that express the semantics of the synthesis sentences. The nodes in this graph are synthesis operations and their typed arguments, and labeled edges specify relations between the nodes. We describe this new resource in detail and highlight some specific challenges to annotating scientific text with shallow semantic structure. We make the corpus available to the community to promote further research and development of scientific information extraction systems.

2016

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Generation from Abstract Meaning Representation using Tree Transducers
Jeffrey Flanigan | Chris Dyer | Noah A. Smith | Jaime Carbonell
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss
Jeffrey Flanigan | Chris Dyer | Noah A. Smith | Jaime Carbonell
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Toward Abstractive Summarization Using Semantic Representations
Fei Liu | Jeffrey Flanigan | Sam Thomson | Norman Sadeh | Noah A. Smith
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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The Logic of AMR: Practical, Unified, Graph-Based Sentence Semantics for NLP
Nathan Schneider | Jeffrey Flanigan | Tim O’Gorman
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

2014

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CMU: Arc-Factored, Discriminative Semantic Dependency Parsing
Sam Thomson | Brendan O’Connor | Jeffrey Flanigan | David Bamman | Jesse Dodge | Swabha Swayamdipta | Nathan Schneider | Chris Dyer | Noah A. Smith
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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A Discriminative Graph-Based Parser for the Abstract Meaning Representation
Jeffrey Flanigan | Sam Thomson | Jaime Carbonell | Chris Dyer | Noah A. Smith
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Large-Scale Discriminative Training for Statistical Machine Translation Using Held-Out Line Search
Jeffrey Flanigan | Chris Dyer | Jaime Carbonell
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
Kevin Gimpel | Nathan Schneider | Brendan O’Connor | Dipanjan Das | Daniel Mills | Jacob Eisenstein | Michael Heilman | Dani Yogatama | Jeffrey Flanigan | Noah A. Smith
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies