Aaron Steven White
Director
Aaron is an Associate Professor in the Department of Linguistics at the University of Rochester, with a secondary appointment in the Department of Computer Science and an affiliation with the Goergen Institute for Data Science. He directs the Center for Language Sciences and the FACTS.lab.
Website
Projects
Papers
- Walden, William, Pavlo Kuchmiichuk, Alexander Martin, Chihsheng Jin, Angela Cao, Claire Sun, Curisia Allen & Aaron White. 2025. Cross-Document Event-Keyed Summarization. In Hao Fei, Kewei Tu, Yuhui Zhang, Xiang Hu, Wenjuan Han, Zixia Jia, Zilong Zheng, et al. (eds.), Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025), 218–241. Vienna, Austria: Association for Computational Linguistics.
@inproceedings{walden_cross-document_2025,
title = "Cross-Document Event-Keyed Summarization",
author = "Walden, William and
Kuchmiichuk, Pavlo and
Martin, Alexander and
Jin, Chihsheng and
Cao, Angela and
Sun, Claire and
Allen, Curisia and
White, Aaron",
editor = "Fei, Hao and
Tu, Kewei and
Zhang, Yuhui and
Hu, Xiang and
Han, Wenjuan and
Jia, Zixia and
Zheng, Zilong and
Cao, Yixin and
Zhang, Meishan and
Lu, Wei and
Siddharth, N. and
{\O}vrelid, Lilja and
Xue, Nianwen and
Zhang, Yue",
booktitle = "Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.xllm-1.19/",
pages = "218--241",
ISBN = "979-8-89176-286-2",
abstract = "Event-keyed summarization (EKS) requires summarizing a specific event described in a document given the document text and an event representation extracted from it. In this work, we extend EKS to the cross-document setting (CDEKS), in which summaries must synthesize information from accounts of the same event as given by multiple sources. We introduce **SEAMuS** (**S**ummaries of **E**vents **A**cross **Mu**ltiple **S**ources), a high-quality dataset for CDEKS based on an expert reannotation of the FAMuS dataset for cross-document argument extraction. We present a suite of baselines on SEAMuS{---}covering both smaller, fine-tuned models, as well as zero- and few-shot prompted LLMs{---}along with detailed ablations and a human evaluation study, showing SEAMuS to be a valuable benchmark for this new task."
}- Cao, Angela, Aaron White & Daniel Lassiter. 2025. Cause, Make, and Force as Graded Causatives. Experiments in Linguistic Meaning 3. 88–103.
- Cao, Angela, Faye Holt, Jonas Chan, Stephanie Richter, Lelia Glass & Aaron Steven White. 2024. Generating event descriptions under syntactic and semantic constraints.
- Grove, Julian & Aaron Steven White. 2025. Factivity, presupposition projection, and the role of discrete knowledge in gradient inference judgments. Natural Language Semantics.
@article{grove_factivity_2025,
title={Factivity, presupposition projection, and the role of discrete knowledge in gradient inference judgments},
author={Grove, Julian and White, Aaron Steven},
journal={Natural Language Semantics},
year={2025},
doi={10.1007/s11050-025-09244-9},
url={https://link.springer.com/article/10.1007/s11050-025-09244-9},
}- Grove, Julian & Aaron Steven White. 2025. Modeling the prompt in inference judgment tasks. Experiments in Linguistic Meaning 3. 176–187.
- Gantt, William, Alexander Martin, Pavlo Kuchmiichuk & Aaron White. 2024. Event-Keyed Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2024, 7333–7345. Miami, Florida, USA: Association for Computational Linguistics.
@inproceedings{gantt_event_2024,
title = "Event-Keyed Summarization",
author = "Gantt, William and
Martin, Alexander and
Kuchmiichuk, Pavlo and
White, Aaron",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.431",
doi = "10.18653/v1/2024.findings-emnlp.431",
pages = "7333--7345",
}- Gantt, William & Aaron Steven White. 2024. Small Models Are (Still) Effective Cross-Domain Argument Extractors.
- Vashishtha, Siddharth, Alexander Martin, William Gantt, Benjamin Van Durme & Aaron White. 2024. FAMuS: Frames Across Multiple Sources. In Kevin Duh, Helena Gomez & Steven Bethard (eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 8250–8273. Mexico City, Mexico: Association for Computational Linguistics.
@inproceedings{vashishtha_famus_2024,
title = "{FAM}u{S}: Frames Across Multiple Sources",
author = "Vashishtha, Siddharth and
Martin, Alexander and
Gantt, William and
Van Durme, Benjamin and
White, Aaron",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.457",
doi = "10.18653/v1/2024.naacl-long.457",
pages = "8250--8273",
abstract = "Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event across documents can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that report on some event, paired with underlying, genre-diverse (non-Wikipedia) source articles for the same event. Events and (cross-sentence) arguments in both report and source are annotated against FrameNet, providing broad coverage of different event types. We present results on two key event understanding tasks enabled by FAMuS: source validation{---}determining whether a document is a valid source for a target report event{---}and cross-document argument extraction{---}full-document argument extraction for a target event from both its report and the correct source article.",
}- Gantt, William, Shabnam Behzad, Hannah An, Yunmo Chen, Aaron White, Benjamin Van Durme & Mahsa Yarmohammadi. 2024. MultiMUC: Multilingual Template Filling on MUC-4. In Yvette Graham & Matthew Purver (eds.), Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), 349–368. St. Julian’s, Malta: Association for Computational Linguistics.
@inproceedings{gantt_multimuc_2024,
title = "{M}ulti{MUC}: Multilingual Template Filling on {MUC}-4",
author = "Gantt, William and
Behzad, Shabnam and
An, Hannah and
Chen, Yunmo and
White, Aaron and
Van Durme, Benjamin and
Yarmohammadi, Mahsa",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.21",
pages = "349--368",
abstract = "We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian. We obtain automatic translations from a strong multilingual machine translation system and manually project the original English annotations into each target language. For all languages, we also provide human translations for key portions of the dev and test splits. Finally, we present baselines on MultiMUC both with state-of-the-art template filling models for MUC-4 and with ChatGPT. We release MUC-4 and the supervised baselines to facilitate further work on document-level information extraction in multilingual settings.",
}- Gantt, William, Reno Kriz, Yunmo Chen, Siddharth Vashishtha & Aaron White. 2023. On Event Individuation for Document-Level Information Extraction. In Houda Bouamor, Juan Pino & Kalika Bali (eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, 12938–12958. Singapore: Association for Computational Linguistics.
@inproceedings{gantt_event_2023,
title = "On Event Individuation for Document-Level Information Extraction",
author = "Gantt, William and
Kriz, Reno and
Chen, Yunmo and
Vashishtha, Siddharth and
White, Aaron",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.862.bib",
doi = "10.18653/v1/2023.findings-emnlp.862",
pages = "12938--12958",
}- Chen, Yunmo, William Gantt, Tongfei Chen, Aaron White & Benjamin Van Durme. 2023. A Unified View of Evaluation Metrics for Structured Prediction. In Houda Bouamor, Juan Pino & Kalika Bali (eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 12868–12882. Singapore: Association for Computational Linguistics.
@inproceedings{chen_unified_2023,
title = "A Unified View of Evaluation Metrics for Structured Prediction",
author = "Chen, Yunmo and
Gantt, William and
Chen, Tongfei and
White, Aaron and
Van Durme, Benjamin",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.795",
doi = "10.18653/v1/2023.emnlp-main.795",
pages = "12868--12882",
}- Barham, Samuel, Orion Weller, Michelle Yuan, Kenton Murray, Mahsa Yarmohammadi, Zhengping Jiang, Siddharth Vashishtha, et al. 2023. MegaWika: Millions of reports and their sources across 50 diverse languages.
@misc{barham_megawika_2023,
title={MegaWika: Millions of reports and their sources across 50 diverse languages},
author={Samuel Barham and Orion Weller and Michelle Yuan and Kenton Murray and Mahsa Yarmohammadi and Zhengping Jiang and Siddharth Vashishtha and Alexander Martin and Anqi Liu and Aaron Steven White and Jordan Boyd-Graber and Benjamin Van Durme},
year={2023},
eprint={2307.07049},
archivePrefix={arXiv},
primaryClass={cs.CL}
}- Chen, Yunmo, William Gantt, Weiwei Gu, Tongfei Chen, Aaron White & Benjamin Van Durme. 2023. Iterative Document-level Information Extraction via Imitation Learning. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 1858–1874. Dubrovnik, Croatia: Association for Computational Linguistics.
@inproceedings{chen_iterative_2023,
title = "Iterative Document-level Information Extraction via Imitation Learning",
author = "Chen, Yunmo and
Gantt, William and
Gu, Weiwei and
Chen, Tongfei and
White, Aaron and
Van Durme, Benjamin",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.136.bib",
pages = "1858--1874",
}- Gantt, William, Lelia Glass & Aaron Steven White. 2022. Decomposing and Recomposing Event Structure. Transactions of the Association for Computational Linguistics 10. 17–34.
@article{gantt_decomposing_2022,
title = {Decomposing and {Recomposing} {Event} {Structure}},
volume = {10},
issn = {2307-387X},
url = {https://doi.org/10.1162/tacl_a_00445},
doi = {10.1162/tacl_a_00445},
urldate = {2022-01-30},
journal = {Transactions of the Association for Computational Linguistics},
author = {Gantt, William and Glass, Lelia and White, Aaron Steven},
month = jan,
year = {2022},
pages = {17--34},
}- Kane, Benjamin, Will Gantt & Aaron Steven White. 2022. Intensional Gaps: Relating veridicality, factivity, doxasticity, bouleticity, and neg-raising. Semantics and Linguistic Theory 31. 570–605.
@article{kane_intensional_2022,
title = {Intensional {Gaps}: {Relating} veridicality, factivity, doxasticity, bouleticity, and neg-raising},
volume = {31},
copyright = {Copyright (c) 2022 Benjamin Kane, Will Gantt, Aaron Steven White},
issn = {2163-5951},
shorttitle = {Intensional {Gaps}},
url = {https://journals.linguisticsociety.org/proceedings/index.php/SALT/article/view/31.029},
doi = {10.3765/salt.v31i0.5137},
abstract = {We investigate which patterns of lexically triggered doxastic, bouletic, neg(ation)-raising, and veridicality inferences are (un)attested across clause-embedding verbs in English. To carry out this investigation, we use a multiview mixed effects mixture model to discover the inference patterns captured in three lexicon-scale inference judgment datasets: two existing datasets, MegaVeridicality and MegaNegRaising, which capture veridicality and neg-raising inferences across a wide swath of the English clause-embedding lexicon, and a new dataset, MegaIntensionality, which similarly captures doxastic and bouletic inferences. We focus in particular on inference patterns that are correlated with morphosyntactic distribution, as determined by how well those patterns predict the acceptability judgments in the MegaAcceptability dataset. We find that there are 15 such patterns attested. Similarities among these patterns suggest the possibility of underlying lexical semantic components that give rise to them. We use principal component analysis to discover these components and suggest generalizations that can be derived from them.},
language = {en},
urldate = {2022-01-13},
journal = {Semantics and Linguistic Theory},
author = {Kane, Benjamin and Gantt, Will and White, Aaron Steven},
month = jan,
year = {2022},
pages = {570--605},
}- White, Aaron Steven. 2021. On believing and hoping whether. Semantics and Pragmatics 14(6). 1–18.
@article{white_believing_2021,
title = {On believing and hoping whether},
volume = {14},
issn = {1937-8912},
url = {https://semprag.org/index.php/sp/article/view/sp.14.6},
doi = {10.3765/sp.14.6},
abstract = {Theories of clause selection that aim to explain the distribution of interrogative and declarative complement clauses often take as a starting point that predicates like think, believe, hope, and fear are incompatible with interrogative complements. After discussing experimental evidence against the generalizations on which these theories rest, I give corpus evidence that even the core data are faulty: think, believe, hope, and fear are in fact compatible with interrogative complements, suggesting that any theory predicting that they should not be must be jettisoned.},
language = {en},
number = {6},
journal = {Semantics and Pragmatics},
author = {White, Aaron Steven},
month = jun,
year = {2021},
keywords = {clause embedding, interrogative, neg-raising, preferential, selection, veridical},
pages = {1--18},
}- Stengel-Eskin, Elias, Kenton Murray, Sheng Zhang, Aaron Steven White & Benjamin Van Durme. 2021. Joint Universal Syntactic and Semantic Parsing. Transactions of the Association for Computational Linguistics 9. 756–773.
@article{stengel-eskin_joint_2021,
author = {Stengel-Eskin, Elias and Murray, Kenton and Zhang, Sheng and White, Aaron Steven and Van Durme, Benjamin},
title = "{Joint Universal Syntactic and Semantic Parsing}",
journal = {Transactions of the Association for Computational Linguistics},
volume = {9},
pages = {756-773},
year = {2021},
month = {08},
issn = {2307-387X},
doi = {10.1162/tacl_a_00396},
url = {https://doi.org/10.1162/tacl\_a\_00396},
eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00396/1955190/tacl\_a\_00396.pdf},
}- Kim, Gene Louis & Aaron Steven White. 2021. Montague Grammar Induction. Semantics and Linguistic Theory 30. 227–251.
- Gantt, William, Benjamin Kane & Aaron Steven White. 2020. Natural Language Inference with Mixed Effects. In Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, 81–87. Barcelona, Spain (Online): Association for Computational Linguistics.
@inproceedings{gantt_natural_2020,
title = {Natural Language Inference with Mixed Effects},
author = {Gantt, William and
Kane, Benjamin and
White, Aaron Steven},
booktitle = {Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics},
month = dec,
year = {2020},
address = {Barcelona, Spain (Online)},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/2020.starsem-1.9},
pages = {81--87}
}- Vashishtha, Siddharth, Adam Poliak, Yash Kumar Lal, Benjamin Van Durme & Aaron Steven White. 2020. Temporal Reasoning in Natural Language Inference. In Findings of the Association for Computational Linguistics: EMNLP 2020, 4070–4078. Online: Association for Computational Linguistics.
@inproceedings{vashishtha_temporal_2020,
title = "Temporal Reasoning in Natural Language Inference",
author = "Vashishtha, Siddharth and
Poliak, Adam and
Lal, Yash Kumar and
Van Durme, Benjamin and
White, Aaron Steven",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.363",
pages = "4070--4078"
}- Chen, Yunmo, Tongfei Chen, Seth Ebner, Aaron Steven White & Benjamin Van Durme. 2020. Reading the Manual: Event Extraction as Definition Comprehension. In Proceedings of the Fourth Workshop on Structured Prediction for NLP, 74–83. Online: Association for Computational Linguistics.
@inproceedings{chen_reading_2020,
title = "Reading the Manual: Event Extraction as Definition Comprehension",
author = "Chen, Yunmo and
Chen, Tongfei and
Ebner, Seth and
White, Aaron Steven and
Van Durme, Benjamin",
booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.spnlp-1.9",
pages = "74--83"
}- An, Hannah & Aaron White. 2020. The lexical and grammatical sources of neg-raising inferences. Proceedings of the Society for Computation in Linguistics 3(1). 220–233.
@article{an_lexical_2020,
title = {The lexical and grammatical sources of neg-raising inferences},
volume = {3},
url = {https://scholarworks.umass.edu/scil/vol3/iss1/23},
doi = {https://doi.org/10.7275/yts0-q989},
number = {1},
journal = {Proceedings of the Society for Computation in Linguistics},
author = {An, Hannah and White, Aaron},
month = jan,
year = {2020},
pages = {220--233}
}- White, Aaron Steven. 2019. Lexically triggered veridicality inferences. In Handbook of Pragmatics, vol. 22, 115–148. John Benjamins Publishing Company.
@incollection{white_lexically_2019,
title = {Lexically triggered veridicality inferences},
volume = {22},
url = {https://doi.org/10.1075/hop.22.lex4},
booktitle = {Handbook of {Pragmatics}},
publisher = {John Benjamins Publishing Company},
author = {White, Aaron Steven},
year = {2019},
pages = {115--148}
}- Vashishtha, Siddharth, Benjamin Van Durme & Aaron Steven White. 2019. Fine-Grained Temporal Relation Extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2906–2919. Florence, Italy: Association for Computational Linguistics.
@inproceedings{vashishtha_fine-grained_2019,
address = {Florence, Italy},
title = {Fine-{Grained} {Temporal} {Relation} {Extraction}},
url = {https://www.aclweb.org/anthology/P19-1280},
abstract = {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.},
booktitle = {Proceedings of the 57th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}},
publisher = {Association for Computational Linguistics},
author = {Vashishtha, Siddharth and Van Durme, Benjamin and White, Aaron Steven},
month = jul,
year = {2019},
pages = {2906--2919}
}- White, Aaron Steven & Kyle Rawlins. 2018. The role of veridicality and factivity in clause selection. In Sherry Hucklebridge & Max Nelson (eds.), Proceedings of the 48th Annual Meeting of the North East Linguistic Society, 221–234. Amherst, MA: GLSA Publications.
@inproceedings{white_role_2018,
address = {Amherst, MA},
title = {The role of veridicality and factivity in clause selection},
booktitle = {Proceedings of the 48th {Annual} {Meeting} of the {North} {East} {Linguistic} {Society}},
publisher = {GLSA Publications},
author = {White, Aaron Steven and Rawlins, Kyle},
editor = {Hucklebridge, Sherry and Nelson, Max},
year = {2018},
pages = {221--234}
}- White, Aaron Steven & Kyle Rawlins. 2020. Frequency, acceptability, and selection: A case study of clause-embedding. Glossa: a journal of general linguistics 5(1). 105.
@article{white_frequency_2020,
title = {Frequency, acceptability, and selection: {A} case study of clause-embedding},
volume = {5},
copyright = {Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access ). All third-party images reproduced on this journal are shared under Educational Fair Use. For more information on Educational Fair Use , please see this useful checklist prepared by Columbia University Libraries . All copyright of third-party content posted here for research purposes belongs to its original owners. Unless otherwise stated all references to characters and comic art presented on this journal are ©, ® or ™ of their respective owners. No challenge to any owner’s rights is intended or should be inferred.},
issn = {2397-1835},
shorttitle = {Frequency, acceptability, and selection},
url = {http://www.glossa-journal.org//article/10.5334/gjgl.1001/},
doi = {10.5334/gjgl.1001},
abstract = {Article: Frequency, acceptability, and selection: A case study of clause-embedding},
language = {en},
number = {1},
urldate = {2020-11-04},
journal = {Glossa: a journal of general linguistics},
author = {White, Aaron Steven and Rawlins, Kyle},
month = nov,
year = {2020},
note = {Number: 1
Publisher: Ubiquity Press},
pages = {105},
file = {Full Text PDF:/home/aaronsteven/snap/zotero-snap/common/Zotero/storage/MNKR3NNA/White and Rawlins - 2020 - Frequency, acceptability, and selection A case st.pdf:application/pdf;Snapshot:/home/aaronsteven/snap/zotero-snap/common/Zotero/storage/Q4S2JYBW/gjgl.1001.html:text/html}
}- White, Aaron Steven, Drew Reisinger, Keisuke Sakaguchi, Tim Vieira, Sheng Zhang, Rachel Rudinger, Kyle Rawlins & Benjamin Van Durme. 2016. Universal Decompositional Semantics on Universal Dependencies. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 1713–1723. Austin, Texas: Association for Computational Linguistics.
@inproceedings{white_universal_2016,
address = {Austin, Texas},
title = {Universal {Decompositional} {Semantics} on {Universal} {Dependencies}},
url = {https://www.aclweb.org/anthology/D16-1177},
doi = {10.18653/v1/D16-1177},
booktitle = {Proceedings of the 2016 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
publisher = {Association for Computational Linguistics},
author = {White, Aaron Steven and Reisinger, Drew and Sakaguchi, Keisuke and Vieira, Tim and Zhang, Sheng and Rudinger, Rachel and Rawlins, Kyle and Van Durme, Benjamin},
month = nov,
year = {2016},
pages = {1713--1723}
}- White, Aaron Steven, Kyle Rawlins & Benjamin Van Durme. 2017. The Semantic Proto-Role Linking Model. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, 92–98. Valencia, Spain: Association for Computational Linguistics.
@inproceedings{white_semantic_2017,
address = {Valencia, Spain},
title = {The {Semantic} {Proto}-{Role} {Linking} {Model}},
url = {https://www.aclweb.org/anthology/E17-2015},
abstract = {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.},
booktitle = {Proceedings of the 15th {Conference} of the {European} {Chapter} of the {Association} for {Computational} {Linguistics}: {Volume} 2, {Short} {Papers}},
publisher = {Association for Computational Linguistics},
author = {White, Aaron Steven and Rawlins, Kyle and Van Durme, Benjamin},
month = apr,
year = {2017},
pages = {92--98}
}- White, Aaron Steven, Pushpendre Rastogi, Kevin Duh & Benjamin Van Durme. 2017. Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 996–1005. Taipei, Taiwan: Asian Federation of Natural Language Processing.
@inproceedings{white_inference_2017,
address = {Taipei, Taiwan},
title = {Inference is {Everything}: {Recasting} {Semantic} {Resources} into a {Unified} {Evaluation} {Framework}},
url = {https://www.aclweb.org/anthology/I17-1100},
abstract = {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.},
booktitle = {Proceedings of the {Eighth} {International} {Joint} {Conference} on {Natural} {Language} {Processing} ({Volume} 1: {Long} {Papers})},
publisher = {Asian Federation of Natural Language Processing},
author = {White, Aaron Steven and Rastogi, Pushpendre and Duh, Kevin and Van Durme, Benjamin},
month = nov,
year = {2017},
pages = {996--1005}
}- Rudinger, Rachel, Aaron Steven White & Benjamin Van Durme. 2018. Neural Models of Factuality. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 731–744. New Orleans, Louisiana: Association for Computational Linguistics.
@inproceedings{rudinger_neural_2018,
address = {New Orleans, Louisiana},
title = {Neural {Models} of {Factuality}},
url = {https://www.aclweb.org/anthology/N18-1067},
doi = {10.18653/v1/N18-1067},
abstract = {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.},
booktitle = {Proceedings of the 2018 {Conference} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}: {Human} {Language} {Technologies}, {Volume} 1 ({Long} {Papers})},
publisher = {Association for Computational Linguistics},
author = {Rudinger, Rachel and White, Aaron Steven and Van Durme, Benjamin},
month = jun,
year = {2018},
pages = {731--744}
}- White, Aaron Steven, Rachel Rudinger, Kyle Rawlins & Benjamin Van Durme. 2018. Lexicosyntactic Inference in Neural Models. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 4717–4724. Brussels, Belgium: Association for Computational Linguistics.
@inproceedings{white_lexicosyntactic_2018,
address = {Brussels, Belgium},
title = {Lexicosyntactic {Inference} in {Neural} {Models}},
url = {https://www.aclweb.org/anthology/D18-1501},
doi = {10.18653/v1/D18-1501},
abstract = {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.},
booktitle = {Proceedings of the 2018 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
publisher = {Association for Computational Linguistics},
author = {White, Aaron Steven and Rudinger, Rachel and Rawlins, Kyle and Van Durme, Benjamin},
month = oct,
year = {2018},
pages = {4717--4724}
}- Poliak, Adam, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White & Benjamin Van Durme. 2018. Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 67–81. Brussels, Belgium: Association for Computational Linguistics.
@inproceedings{poliak_collecting_2018,
address = {Brussels, Belgium},
title = {Collecting {Diverse} {Natural} {Language} {Inference} {Problems} for {Sentence} {Representation} {Evaluation}},
url = {https://www.aclweb.org/anthology/D18-1007},
doi = {10.18653/v1/D18-1007},
abstract = {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.},
booktitle = {Proceedings of the 2018 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
publisher = {Association for Computational Linguistics},
author = {Poliak, Adam and Haldar, Aparajita and Rudinger, Rachel and Hu, J. Edward and Pavlick, Ellie and White, Aaron Steven and Van Durme, Benjamin},
month = oct,
year = {2018},
pages = {67--81}
}- Govindarajan, Venkata, Benjamin Van Durme & Aaron Steven White. 2019. Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements. Transactions of the Association for Computational Linguistics 7. 501–517.
@article{govindarajan_decomposing_2019,
title = {Decomposing {Generalization}: {Models} of {Generic}, {Habitual}, and {Episodic} {Statements}},
volume = {7},
url = {https://www.aclweb.org/anthology/Q19-1035},
doi = {10.1162/tacl_a_00285},
abstract = {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.},
journal = {Transactions of the Association for Computational Linguistics},
author = {Govindarajan, Venkata and Van Durme, Benjamin and White, Aaron Steven},
month = mar,
year = {2019},
pages = {501--517}
}- White, Aaron Steven, Elias Stengel-Eskin, Siddharth Vashishtha, Venkata Subrahmanyan Govindarajan, Dee Ann Reisinger, Tim Vieira, Keisuke Sakaguchi, et al. 2020. The Universal Decompositional Semantics Dataset and Decomp Toolkit. In Proceedings of the 12th Language Resources and Evaluation Conference, 5698–5707. Marseille, France: European Language Resources Association.
@inproceedings{white_universal_2020,
address = {Marseille, France},
title = {The {Universal} {Decompositional} {Semantics} {Dataset} and {Decomp} {Toolkit}},
isbn = {979-10-95546-34-4},
url = {https://www.aclweb.org/anthology/2020.lrec-1.699},
abstract = {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.},
language = {English},
booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},
publisher = {European Language Resources Association},
author = {White, Aaron Steven and Stengel-Eskin, Elias and Vashishtha, Siddharth and Govindarajan, Venkata Subrahmanyan and Reisinger, Dee Ann and Vieira, Tim and Sakaguchi, Keisuke and Zhang, Sheng and Ferraro, Francis and Rudinger, Rachel and Rawlins, Kyle and Van Durme, Benjamin},
month = may,
year = {2020},
pages = {5698--5707}
}- Stengel-Eskin, Elias, Aaron Steven White, Sheng Zhang & Benjamin Van Durme. 2020. Universal Decompositional Semantic Parsing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 8427–8439. Online: Association for Computational Linguistics.
@inproceedings{stengel-eskin_universal_2020,
address = {Online},
title = {Universal {Decompositional} {Semantic} {Parsing}},
url = {https://www.aclweb.org/anthology/2020.acl-main.746},
doi = {10.18653/v1/2020.acl-main.746},
abstract = {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.},
booktitle = {Proceedings of the 58th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}},
publisher = {Association for Computational Linguistics},
author = {Stengel-Eskin, Elias and White, Aaron Steven and Zhang, Sheng and Van Durme, Benjamin},
month = jul,
year = {2020},
pages = {8427--8439}
}- White, Aaron Steven & Kyle Rawlins. 2016. A computational model of S-selection. (Ed. by) Mary Moroney, Carol-Rose Little, Jacob Collard & Dan Burgdorf. Semantics and Linguistic Theory 26. 641–663.
@article{white_computational_2016,
title = {A computational model of {S}-selection},
volume = {26},
issn = {2163-5951},
url = {https://journals.linguisticsociety.org/proceedings/index.php/SALT/article/view/26.641},
doi = {10.3765/salt.v26i0.3819},
abstract = {We develop a probabilistic model of S(emantic)-selection that encodes both the notion of systematic mappings from semantic type signature to syntactic distribution—i.e., projection rules—and the notion of selectional noise—e.g., C(ategory)-selection, L(exical)-selection, and/or other independent syntactic processes. We train this model on data from a large-scale judgment study assessing the acceptability of 1,000 English clause-taking verbs in 50 distinct syntactic frames, finding that this model infers coherent semantic type signatures. We focus in on type signatures relevant to interrogative and declarative selection, arguing that our results suggest a principled split between cognitive verbs, which select distinct proposition and question types, and communicative verbs, which select a single hybrid type.},
language = {en},
urldate = {2020-08-24},
journal = {Semantics and Linguistic Theory},
author = {White, Aaron Steven and Rawlins, Kyle},
editor = {Mary Moroney and Carol-Rose Little and Jacob Collard and Dan Burgdorf},
month = oct,
year = {2016},
note = {Number: 0},
pages = {641--663},
file = {Full Text PDF:/home/aaronsteven/snap/zotero-snap/common/Zotero/storage/HGQIL3Y2/White and Rawlins - 2016 - A computational model of S-selection.pdf:application/pdf;Snapshot:/home/aaronsteven/snap/zotero-snap/common/Zotero/storage/H4KVLSH9/26.html:text/html}
}- Moon, Ellise & Aaron Steven White. 2020. The source of nonfinite temporal interpretation. In Mariam Asatryan, Yixiao Song & Ayana Whitmal (eds.), Proceedings of the 50th Annual Meeting of the North East Linguistic Society, vol. 3, 11–24. Amherst, MA: GLSA Publications.
@inproceedings{moon_source_2020,
address = {Amherst, MA},
title = {The source of nonfinite temporal interpretation},
volume = {3},
booktitle = {Proceedings of the 50th {Annual} {Meeting} of the {North} {East} {Linguistic} {Society}},
publisher = {GLSA Publications},
author = {Moon, Ellise and White, Aaron Steven},
editor = {Mariam Asatryan and Yixiao Song and Ayana Whitmal},
year = {2020},
pages = {11--24}
}