Papers by Bruno Taillé
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction (2021.emnlp-main)
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| Challenge: | State-of-the-art NLP models adopt shallow heuristics that limit their generalization capability. |
| Approach: | They propose to use heuristics that limit their generalization capability to model lexical overlap with the training set in Named-Entity Recognition and Event or Type heuristic in Relation Extraction to test their models. |
| Outcome: | The proposed model can perform better on the two key tasks, while the retention of training relation triples. |
Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction! (2020.emnlp-main)
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| Challenge: | Existing literature on Relation Extraction (RE) uses multiple evaluation setups to compare performance. |
| Approach: | They propose to quantify the most common comparison mistake and evaluate it leads to overestimating the final RE performance by around 5% on ACE05. |
| Outcome: | The proposed meta-analysis overestimates the final RE performance by around 5% on ACE05. |