Papers by Sophie Henning
Generalized chart constraints for efficient PCFG and TAG parsing (P18-2)
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| Challenge: | Existing pruning techniques limit chart constraints to PCFGs and cannot be applied to more expressive grammars. |
| Approach: | They propose to apply chart constraints to more expressive grammars and a neural tagger which predicts chart constraints at very high precision. |
| Outcome: | The proposed technique accelerates both PCFG and TAG parsing by two orders of magnitude while improving accuracy. |
MiST: a Large-Scale Annotated Resource and Neural Models for Functions of Modal Verbs in English Scientific Text (2022.findings-emnlp)
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| Challenge: | modal verbs are used for hedges, but they may also denote abilities and restrictions in scientific texts . modals are often used for hedging, but prior work on this topic has been limited . |
| Approach: | They propose a dataset that contains 3737 modal instances in five scientific domains . they evaluate a set of competitive neural architectures to model the distinctions in MIST . |
| Outcome: | The proposed dataset contains 3737 modal instances in five scientific domains . leveraging non-scientific data is of limited benefit for modeling the distinctions in MIST . |
Is the Answer in the Text? Challenging ChatGPT with Evidence Retrieval from Instructive Text (2023.findings-emnlp)
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| Challenge: | Generative language models have recently shown remarkable success in generating answers to questions in a given textual context, but they suffer from hallucination, wrongly cite evidence, and spread misleading information. |
| Approach: | They propose a benchmark to evaluate an annotated WikiHow article and use it to retrieve answers to questions from trustworthy texts. |
| Outcome: | The proposed model can retrieve answers to lexically varied and open-ended questions from trustworthy instructive texts. |
A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing (2023.eacl-main)
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| Challenge: | Developing methods to improve model performance in imbalanced data settings has been an active area for decades . |
| Approach: | They propose to use sampling, data augmentation, choice of loss function, staged learning, or model design to address class imbalance in NLP. |
| Outcome: | The proposed approaches are evaluated on a variety of NLP tasks or in the computer vision community. |