Coreference resolution is an important task for gaining more complete understanding about texts by artificial intelligence.The state-of-the-art end-to-end neural coreference model considers all spans in a document as potential mentions and learns to link an antecedent with each possible mention. However, for the verbatim same mentions, the model tends to get similar or even identical representations based on the features, and this leads to wrongful predictions. In this paper, we propose to improve the end-to-end system by building an attention model to reweigh features around different contexts. The proposed model substantially outperforms the state-of-the-art on the English dataset of the CoNLL 2012 Shared Task with 73.45% F1 score on development data and 72.84% F1 score on test data.
This paper extends the neural coreference resolution model in Lee et al. (2018) by 1) introducing an additional mention-level feature (grammatical numbers), and 2) letting the mention/pair scoring functions attend over multiple mention-level features. The proposed model achieves marginal improvement (0.2 avg. F1 points) over Lee et al., 2018, on the CoNLL 2012 English test set.
I recommend rejection for this paper due to the following reasons:
- The technical contribution is very incremental (introducing one more features, and adding an attention layer over the feature vectors). 
- The experiment results aren't strong enough. And the experiments are done on only one dataset.
- I am not convinced that adding the grammatical numbers features and the attention mechanism makes the model more context-aware.
Other suggestions:
- The citation format seems weird through out the paper. 
- Table 1 and 3 look like ablation results. It might be less confusing if it's presented as "Full system: xx%; without pairwise FA: yy%; without grammatical numbers zz% ...".
- Equation 8 - 10 are quite confusing. What is f(x)? How large is V? What is u? etc.
- Please define/explain the "grammatical numbers" feature when it's introduced in Section 2.2.