Papers by William Gantt
Natural Language Inference with Mixed Effects (2020.starsem-1)
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| Challenge: | aggregating raw annotations to a single label is problematic due to disagreement among annotators. |
| Approach: | They 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. |
| Outcome: | The proposed method improves performance over models that do not incorporate such effects. |
A Unified View of Evaluation Metrics for Structured Prediction (2023.emnlp-main)
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| Challenge: | a framework that unifies evaluation metrics for structured prediction tasks is presented . metric design decisions are motivated by specific characteristics of tasks, and we suggest modifications to existing metrics to meet those motivations. |
| Approach: | They propose a framework that unifies a variety of evaluation metrics for different structured prediction tasks. |
| Outcome: | The proposed framework can be used to create new metrics based on the output structure of a number of tasks. |
MultiMUC: Multilingual Template Filling on MUC-4 (2024.eacl-long)
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William Gantt, Shabnam Behzad, Hannah An, Yunmo Chen, Aaron White, Benjamin Van Durme, Mahsa Yarmohammadi
| Challenge: | We present multilingual parallel template filling datasets for MUCs . systems were required to extract one template per incident, containing details about perpetrators, victims, weapons used . |
| Approach: | They introduce MultiMUC, the first multilingual parallel corpus for template filling . they obtain automatic translations from a strong multilingual machine translation system . |
| Outcome: | The proposed dataset includes translations of the classic MUC-4 template filling benchmark into Arabic, Chinese, Farsi, Korean, and Russian. |
FAMuS: Frames Across Multiple Sources (2024.naacl-long)
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| Challenge: | Recent work in document-level event and argument extraction tasks suffer from two key shortcomings. |
| Approach: | They propose to combine Wikipedia passages with underlying, genre-diverse source articles for an event . they propose two key task enabled by FAMuS: source validation and cross-document argument extraction . |
| Outcome: | The proposed system can extract event arguments from document and report documents. |
On Event Individuation for Document-Level Information Extraction (2023.findings-emnlp)
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| Challenge: | a bomb exploded in a restaurant in Lima, and a second device was deactivated by the police . |
| Approach: | They argue that the task demands definitive answers to thorny questions of *event individuation* they argue that even human experts disagree on the task . |
| Outcome: | The proposed task demands definitive answers to thorny questions of *event individuation* . the proposed task also raises concerns about the usefulness of template filling metrics . |
Decomposing and Recomposing Event Structure (2022.tacl-1)
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| Challenge: | Using annotated sentences and document-level UDS graphs, we induce an event structure classification with semantic role, entity, and event-event relation classifications. |
| Approach: | They propose to use Universal Decompositional Semantics (UDS) graphs to induce event structure classification . they augment existing annotations with inferential properties capturing fine-grained aspects of temporal and aspectual structure of events. |
| Outcome: | The proposed model is the largest annotation of event structure and (partial) event coreference to date. |
Iterative Document-level Information Extraction via Imitation Learning (2023.eacl-main)
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| Challenge: | Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template’s slot values. |
| Approach: | They propose to use iterative extraction to extract complex relations, i.e., N-tuples representing a mapping from named slots to spans of text within a document. |
| Outcome: | The proposed model leads to state-of-the-art results on two established benchmarks and a strong baseline on the new BETTER Granular task. |
Event-Keyed Summarization (2024.findings-emnlp)
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| Challenge: | a novel task combines document-level event extraction with event-keyed summarization . a recent study has shown that traditional summarizing produces inferior summaries of target events . |
| Approach: | They propose a task that marries traditional summarization and document-level event extraction with the goal of generating a contextualized summary for a specific event, given a document and an extracted event structure. |
| Outcome: | The proposed task combines document-level event extraction with event-keyed summarization . the authors show that the proposed task is robust and humane . |