Papers by Meihan Tong
Improving Event Detection via Open-domain Trigger Knowledge (2020.acl-main)
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| Challenge: | Existing methods for event detecting are prone to overfitting densely labeled trigger words due to the small scale of training data. |
| Approach: | They propose a novel Enrichment Knowledge Distillation model to leverage external open-domain trigger knowledge to reduce in-built biases to frequent trigger words in annotations. |
| Outcome: | The proposed model outperforms nine strong baselines and is especially effective for unseen/sparsely labeled trigger words. |
Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition (2021.acl-long)
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| Challenge: | Existing methods to classify named entity mentions with fewshots fail to differentiate rich semantics in other-class words, which will aggravate overfitting under few shot scenario. |
| Approach: | They propose a model that can automatically induce different unde- fined classes from the other class to improve few-shot Named Entity Recognition (NER) . |
| Outcome: | The proposed model outperforms five state-of-the-art models in 1- shot and 5-shots settings on four NER bench marks. |
Minimal Free Resolution Guided Adaptive Tree Reasoning (2026.acl-long)
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| Challenge: | Existing approaches to building dynamic reasoning trees rely on manual decomposition patterns and subproblems. |
| Approach: | They propose a hierarchical reasoning framework based on MFR theory that supports adaptive reasoning trees and reliable error correction within a single LLM. |
| Outcome: | The proposed framework significantly reduces logical errors and improves reasoning accuracy compared to the Chain-of-Thought, Decompose–Analyze–Rethink and Tree-of–Though. |