To explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA) datasets.
The evidence information has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a model.
In relation extraction task, we achieved 80. 46% in terms of F-score as the top system in the relation extraction or recognition task.
Ranked #1 on Named Entity Recognition (NER) on WNUT 2020
Additionally, the proposed model is able to detect coreferent pairs in long distances, even with a distance of more than 200 sentences.