Papers by Weiwei Gu
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. |
An Empirical Study on Finding Spans (2022.emnlp-main)
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| Challenge: | Various information extraction tasks require a span finding component, which either directly yields the output or serves as an essential component of downstream linking. |
| Approach: | They propose methods for span finding, the selection of consecutive tokens in text for some downstream tasks. |
| Outcome: | The proposed methods perform better on masked language models and pre-trained encoders than on encoder-decoder models. |