Papers by Hui-Syuan Yeh
On Training Instance Selection for Few-Shot Neural Text Generation (2021.acl-short)
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| Challenge: | Pretraining large neural networks with a language modeling objective has led to dramatic improvements in text generation. |
| Approach: | They propose a selection strategy to select few-shot training instances based on unlabeled data to identify the most worthwhile data points that should be annotated under some budget of labeling cost. |
| Outcome: | The proposed strategy outperforms random sampling on three text generation tasks. |
Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction (2022.lrec-1)
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| Challenge: | Recent research shows that prompt-based learning improves performance on relation extraction tasks. |
| Approach: | They propose a prompt-based learning method that generates comprehensive prompts for biomedical relation extraction using a ChemProt dataset. |
| Outcome: | The proposed method improves fine-tuning on a biomedical relation extraction task with a cloze-test task and fewer training examples to make reasonable predictions. |
Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning (2021.eacl-main)
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| Challenge: | Recent advances in data-to-text generation have been focused on curriculum learning, which is a process of presenting training data in a specific order, starting from easy examples and moving on to more difficult ones, as the learner becomes more competent. |
| Approach: | They propose to use a curriculum learning process to change the order of training samples in a model based on the model's competence to improve model performance and convergence speed. |
| Outcome: | The proposed model shows faster convergence speed and reduced training time by 38.7% and performance by 4.84 BLEU. |
Logic-Guided Message Generation from Raw Real-Time Sensor Data (2022.lrec-1)
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| Challenge: | Developing a natural language generation model to enable human pilots to communicate with drones is challenging because of its redundant nature and diversity. |
| Approach: | They propose a corpus for a specific domain that instantiates these properties by combining sensor data with text. |
| Outcome: | The proposed model can alert the human pilot of the system state and environment in preparation of handover of control. |
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages (2024.lrec-main)
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Lisa Raithel, Hui-Syuan Yeh, Shuntaro Yada, Cyril Grouin, Thomas Lavergne, Aurélie Névéol, Patrick Paroubek, Philippe Thomas, Tomohiro Nishiyama, Sebastian Möller, Eiji Aramaki, Yuji Matsumoto, Roland Roller, Pierre Zweigenbaum
| Challenge: | Existing clinical corpora mostly revolves around scientific articles in English . existing literature is limited to only a few scientific articles . |
| Approach: | They propose to use user-generated data sources to uncover adverse drug reactions . existing clinical corpora mostly revolves around scientific articles in english . authors provide statistics to highlight certain challenges associated with the corpus . |
| Outcome: | The proposed corpus includes 12 entity types, four attribute types, and 13 relation types . it provides strong baselines for extracting entities and relations between entities . |