Coreferential Reasoning Learningfor Language Representation
Table 2: Results on QUOREF measured by exact match (EM) and F1. Results with ∗, + are from Dasigi et al. (2019) and official leaderboard respectively.
['Model', 'Dev EM', 'Dev F1', 'Test EM', 'Test F1']
Our BERTBase outperforms original BERT by about 2 points in EM and F1 score, which indicates the effectiveness of the added reasoning layer and multi-span prediction module. CorefBERTBase and CorefBERTLarge exceeds our adapted BERTBase and BERTLarge by 4.4% and 2.9% F1 points respectively. CorefRoBERTaLarge also gains 0.7% F1 improvement and achieves a new state-of-the-art. We show four case studies in Supplemental Materials, which indicate that through reasoning over mentions, CorefBERT could aggregate information to answer the question requiring coreferential reasoning