  Document-level relation extraction (RE) aims to extract the relations between
entities from the input document that usually containing many
difficultly-predicted entity pairs whose relations can only be predicted
through relational inference. Existing methods usually directly predict the
relations of all entity pairs of input document in a one-pass manner, ignoring
the fact that predictions of some entity pairs heavily depend on the predicted
results of other pairs. To deal with this issue, in this paper, we propose a
novel document-level RE model with iterative inference. Our model is mainly
composed of two modules: 1) a base module expected to provide preliminary
relation predictions on entity pairs; 2) an inference module introduced to
refine these preliminary predictions by iteratively dealing with
difficultly-predicted entity pairs depending on other pairs in an easy-to-hard
manner. Unlike previous methods which only consider feature information of
entity pairs, our inference module is equipped with two Extended Cross
Attention units, allowing it to exploit both feature information and previous
predictions of entity pairs during relational inference. Furthermore, we adopt
a two-stage strategy to train our model. At the first stage, we only train our
base module. During the second stage, we train the whole model, where
contrastive learning is introduced to enhance the training of inference module.
Experimental results on three commonly-used datasets show that our model
consistently outperforms other competitive baselines.
