Coreference Resolution: Toward EndtoEnd and CrossLingual Systems The task of coreference resolution has attracted considerable attention in the literature due to its importance in deep language understanding and its potential as a subtask in a variety of complex natural language processing problems. In this study, we outlined the fieldxe2x80x99s terminology, describe existing metrics, their differences and shortcomings, as well as the available corpora and external resources. We analyzed existing stateoftheart models and approaches, and reviewed recent advances and trends in the field, namely endtoend systems that jointly model different subtasks of coreference resolution, and crosslingual systems that aim to overcome the challenges of lessresourced languages. Finally, we discussed the main challenges and open issues faced by coreference resolution systems.

Dense Semantic Matching Network for Multiturn Conversation Mining the semantic information in the text to model the multiturn conversation has attracted great interests. Previous models ignore the capturing and utilizing of the matching patterns at different levels, which causes the loss of the valuable information for the calculation of the matching degree. To address the problem, we propose a dense semantic matching network (DMN). Given a contextresponse pair, DMN first constructs semantic representations for the response candidate and each utterance in the context. Then, DMN models the interaction between the response candidate and each utterance in the context to generate interactive matrices. By processing the interactive matrices with dense convolutional blocks, the hierarchical matching patterns are generated for each utteranceresponse pair. The matching patterns of all the utteranceresponse pairs are finally accumulated in chronological order with bidirectional long shortterm memory network. The final matching score of the response candidate and the multiturn context is finally calculated. We evaluate the performance of our network on the benchmark dataset. The results show that our network yields a significant performance gain compared with other methods.