Improving Generalization in Coreference Resolution via Adversarial Training
This addresses a generalization issue in coreference resolution for NLP applications, but it is incremental as it builds on an existing state-of-the-art method.
The paper tackled the problem of coreference resolution systems failing to generalize when encountering new named entities, and showed that adversarial gradient-based training improved performance on both the original and modified CoNLL datasets and the GAP dataset.
In order for coreference resolution systems to be useful in practice, they must be able to generalize to new text. In this work, we demonstrate that the performance of the state-of-the-art system decreases when the names of PER and GPE named entities in the CoNLL dataset are changed to names that do not occur in the training set. We use the technique of adversarial gradient-based training to retrain the state-of-the-art system and demonstrate that the retrained system achieves higher performance on the CoNLL dataset (both with and without the change of named entities) and the GAP dataset.