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

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 information from constituents on upper levels, can further improve the accuracy.