Aaron Steven White


2020

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Universal Decompositional Semantic Parsing
Elias Stengel-Eskin | Aaron Steven White | Sheng Zhang | Benjamin Van Durme
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores. We also introduce a strong pipeline model for parsing into the UDS graph structure, and show that our transductive parser performs comparably while additionally performing attribute prediction. By analyzing the attribute prediction errors, we find the model captures natural relationships between attribute groups.

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Reading the Manual: Event Extraction as Definition Comprehension
Yunmo Chen | Tongfei Chen | Seth Ebner | Aaron Steven White | Benjamin Van Durme
Proceedings of the Fourth Workshop on Structured Prediction for NLP

We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals. Such a capability would allow for the trivial construction and extension of an extraction framework by intended end-users through declarations such as, “Some person was born in some location at some time.” We introduce an example of a model that employs such statements, with experiments illustrating we can extract events under closed ontologies and generalize to unseen event types simply by reading new definitions.

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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.

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Temporal Reasoning in Natural Language Inference
Siddharth Vashishtha | Adam Poliak | Yash Kumar Lal | Benjamin Van Durme | Aaron Steven White
Findings of the Association for Computational Linguistics: EMNLP 2020

We introduce five new natural language inference (NLI) datasets focused on temporal reasoning. We recast four existing datasets annotated for event duration—how long an event lasts—and event ordering—how events are temporally arranged—into more than one million NLI examples. We use these datasets to investigate how well neural models trained on a popular NLI corpus capture these forms of temporal reasoning.

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The lexical and grammatical sources of neg-raising inferences
Hannah Youngeun An | Aaron Steven White
Proceedings of the Society for Computation in Linguistics 2020

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Natural Language Inference with Mixed Effects
William Gantt | Benjamin Kane | Aaron Steven White
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

There is growing evidence that the prevalence of disagreement in the raw annotations used to construct natural language inference datasets makes the common practice of aggregating those annotations to a single label problematic. We propose a generic method that allows one to skip the aggregation step and train on the raw annotations directly without subjecting the model to unwanted noise that can arise from annotator response biases. We demonstrate that this method, which generalizes the notion of a mixed effects model by incorporating annotator random effects into any existing neural model, improves performance over models that do not incorporate such effects.

2019

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Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements
Venkata Govindarajan | Benjamin Van Durme | Aaron Steven White
Transactions of the Association for Computational Linguistics, Volume 7

We present a novel semantic framework for modeling linguistic expressions of generalization— generic, habitual, and episodic statements—as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token-level information—including hand-engineered features and static (GloVe) and contextual (ELMo) word embeddings—for predicting expressions of generalization.

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Fine-Grained Temporal Relation Extraction
Siddharth Vashishtha | Benjamin Van Durme | Aaron Steven White
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations.

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A Framework for Decoding Event-Related Potentials from Text
Shaorong Yan | Aaron Steven White
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

We propose a novel framework for modeling event-related potentials (ERPs) collected during reading that couples pre-trained convolutional decoders with a language model. Using this framework, we compare the abilities of a variety of existing and novel sentence processing models to reconstruct ERPs. We find that modern contextual word embeddings underperform surprisal-based models but that, combined, the two outperform either on its own.

2018

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Neural Models of Factuality
Rachel Rudinger | Aaron Steven White | Benjamin Van Durme
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well.

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Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
Adam Poliak | Aparajita Haldar | Rachel Rudinger | J. Edward Hu | Ellie Pavlick | Aaron Steven White | Benjamin Van Durme
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.

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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.

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Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
Adam Poliak | Aparajita Haldar | Rachel Rudinger | J. Edward Hu | Ellie Pavlick | Aaron Steven White | Benjamin Van Durme
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. Our collection of diverse datasets is available at http://www.decomp.net/, and will grow over time as additional resources are recast and added from novel sources.

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.

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Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework
Aaron Steven White | Pushpendre Rastogi | Kevin Duh | Benjamin Van Durme
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations. The resulting suite of datasets enables us to probe a statistical RTE model’s performance on different aspects of semantics. We demonstrate the value of this approach by investigating the behavior of a popular neural network RTE model.

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