Segmentation Approach for Coreference Resolution Task
This addresses coreference resolution for NLP applications, but it is incremental as it builds on existing methods with a new embedding technique.
The paper tackles coreference resolution by proposing a segmentation approach that resolves all mentions to a given mention in one pass, using BERT encoding and span position embeddings; preliminary results on CoNLL 2012 show promising capture of long-distance relations but do not meet state-of-the-art performance.
In coreference resolution, it is important to consider all members of a coreference cluster and decide about all of them at once. This technique can help to avoid losing precision and also in finding long-distance relations. The presented paper is a report of an ongoing study on an idea which proposes a new approach for coreference resolution which can resolve all coreference mentions to a given mention in the document in one pass. This has been accomplished by defining an embedding method for the position of all members of a coreference cluster in a document and resolving all of them for a given mention. In the proposed method, the BERT model has been used for encoding the documents and a head network designed to capture the relations between the embedded tokens. These are then converted to the proposed span position embedding matrix which embeds the position of all coreference mentions in the document. We tested this idea on CoNLL 2012 dataset and although the preliminary results from this method do not quite meet the state-of-the-art results, they are promising and they can capture features like long-distance relations better than the other approaches.