Papers by Elizabeth Boschee
DEGREE: A Data-Efficient Generation-Based Event Extraction Model (2022.naacl-main)
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I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng
| Challenge: | Existing models for event extraction require expensive human annotations. |
| Approach: | They propose a data-efficient event extraction model that formulates event extraction as a conditional generation problem. |
| Outcome: | The proposed model can be trained with only a few labeled examples. |
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation (2020.acl-demos)
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Dong-Ho Lee, Rahul Khanna, Bill Yuchen Lin, Seyeon Lee, Qinyuan Ye, Elizabeth Boschee, Leonardo Neves, Xiang Ren
| Challenge: | Existing frameworks for sequence labeling and classification require massive human effort and labeling data is limited. |
| Approach: | They propose a web-based, Label-Efficient AnnotatioN framework that allows an annotator to provide the needed labels for a task and can capture explanations for each labeling decision. |
| Outcome: | The proposed framework surpasses baseline F1 scores by 5-10 percentage points while using 2X times fewer labeled instances. |
Teaching Machine Comprehension with Compositional Explanations (2020.findings-emnlp)
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| Challenge: | Recent advances in machine reading comprehension rely heavily on large-scale annotated corpora, which are timeconsuming and costly to collect. |
| Approach: | They propose to use semi-structured explanations to “teach” machines reading comprehension using a small number of semi-structural explanations that explicitly inform machines why answer spans are correct. |
| Outcome: | The proposed method achieves 70.14% F1 score with supervision from 26 explanations on the SQuAD dataset, comparable to plain supervised learning using 1,100 labeled instances yielding a 12x speed up. |
CAVE: Controllable Authorship Verification Explanations (2025.naacl-long)
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| Challenge: | Authorship Verification (AV) is used for tasks such as plagiarism detection, forensic analysis, analysis of the spread of misinformation. |
| Approach: | They propose to train an offline authorship verification model that is accessible and easy to use. |
| Outcome: | The proposed model generates high quality explanations and competitive task accuracy on three difficult AV datasets. |
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction (2023.eacl-main)
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Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren
| Challenge: | Named entity recognition models have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. |
| Approach: | They propose a framework that automatically generates and leverages “entity triggers” which are human-readable cues in the text that help guide the model to make better decisions. |
| Outcome: | The proposed framework outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on three well-studied datasets. |
SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage (P19-3)
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Elizabeth Boschee, Joel Barry, Jayadev Billa, Marjorie Freedman, Thamme Gowda, Constantine Lignos, Chester Palen-Michel, Michael Pust, Banriskhem Kayang Khonglah, Srikanth Madikeri, Jonathan May, Scott Miller
| Challenge: | a new cross-lingual information retrieval system for low-resource languages is available in less-frequently-taught languages . a multilingual system can search for relevant information in a haystack of documents in swahili or Somali . human-driven approaches to this problem are complicated in 'low-resourced' languages aaron sagar: "the key role played by humans in triaging results is complicated" |
| Approach: | They propose an end-to-end cross-lingual information retrieval system for low-resource languages . the system enables English speakers to search foreign language repositories using English queries . it summarizes the retrieved documents in English with respect to a particular information need . |
| Outcome: | The proposed system achieves top performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. |
Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search (2023.acl-demo)
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| Challenge: | Using only English training data, ISI-Clear makes global events available on-demand in 100 languages . Using a fixed task, events may still shift from day to day . |
| Approach: | They propose a cross-lingual zero-shot event extraction system that makes global events available on-demand in 100 languages. |
| Outcome: | The proposed system can extract events from non-English documents in 100 languages. |