Papers by Tianwen Jiang
Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text (D19-1)
Copied to clipboard
| Challenge: | Existing methods to extract factual tuples from scientific text do not consider conditions. |
| Approach: | They propose a new sequence labeling framework to jointly extract fact and condition tuples from scientific sentences. |
| Outcome: | The proposed framework improves F1 score relative to existing methods by 4.2% and 6.2% on bioNLP2013. |
Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for SciNER (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-training a language model by self-supervised tasks on huge datasets and fine-tuning with small labelled data are often inadequate for scientific NER tasks. |
| Approach: | They propose to introduce a "pre-fine tuning" step between pre-training and fine-tuning to construct a corpus by selecting sentences from unlabeled documents that are the most relevant with labelled training data. |
| Outcome: | The proposed approach improves on seven benchmarks on the performance of the proposed model on labelled datasets. |