Kyle Rawlins


2020

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Multi-Sentence Argument Linking
Seth Ebner | Patrick Xia | Ryan Culkin | Kyle Rawlins | Benjamin Van Durme
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We present a novel document-level model for finding argument spans that fill an event’s roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution. Because existing datasets for cross-sentence linking are small, development of our neural model is supported through the creation of a new resource, Roles Across Multiple Sentences (RAMS), which contains 9,124 annotated events across 139 types. We demonstrate strong performance of our model on RAMS and other event-related datasets.

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The Universal Decompositional Semantics Dataset and Decomp Toolkit
Aaron Steven White | Elias Stengel-Eskin | Siddharth Vashishtha | Venkata Subrahmanyan Govindarajan | Dee Ann Reisinger | Tim Vieira | Keisuke Sakaguchi | Sheng Zhang | Francis Ferraro | Rachel Rudinger | Kyle Rawlins | Benjamin Van Durme
Proceedings of the 12th Language Resources and Evaluation Conference

We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification—with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using sophisticated normalization procedures. The Decomp toolkit provides a suite of Python 3 tools for querying UDS graphs using SPARQL. Both UDS1.0 and Decomp0.1 are publicly available at http://decomp.io.

2018

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Lexicosyntactic Inference in Neural Models
Aaron Steven White | Rachel Rudinger | Kyle Rawlins | Benjamin Van Durme
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We investigate neural models’ ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.

2017

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The Semantic Proto-Role Linking Model
Aaron Steven White | Kyle Rawlins | Benjamin Van Durme
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We propose the semantic proto-role linking model, which jointly induces both predicate-specific semantic roles and predicate-general semantic proto-roles based on semantic proto-role property likelihood judgments. We use this model to empirically evaluate Dowty’s thematic proto-role linking theory.

2016

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Universal Decompositional Semantics on Universal Dependencies
Aaron Steven White | Drew Reisinger | Keisuke Sakaguchi | Tim Vieira | Sheng Zhang | Rachel Rudinger | Kyle Rawlins | Benjamin Van Durme
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Semantic Proto-Roles
Drew Reisinger | Rachel Rudinger | Francis Ferraro | Craig Harman | Kyle Rawlins | Benjamin Van Durme
Transactions of the Association for Computational Linguistics, Volume 3

We present the first large-scale, corpus based verification of Dowty’s seminal theory of proto-roles. Our results demonstrate both the need for and the feasibility of a property-based annotation scheme of semantic relationships, as opposed to the currently dominant notion of categorical roles.