Paper: A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing

ACL ID P14-1048
Title A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Text-level discourse parsing remains a challenge. The current state-of-the-art overall accuracy in relation assignment is 55.73%, achieved by Joty et al. (2013). However, their model has a high order of time complexity, and thus cannot be ap- plied in practice. In this work, we develop a much faster model whose time complex- ity is linear in the number of sentences. Our model adopts a greedy bottom-up ap- proach, with two linear-chain CRFs ap- plied in cascade as local classifiers. To en- hance the accuracy of the pipeline, we add additional constraints in the Viterbi decod- ing of the first CRF. In addition to effi- ciency, our parser also significantly out- performs the state of the art. Moreover, our novel approach of post-editing, which modifies a fully-built tree by considering inform...