Finding Arguments as Sequence Labeling in Discourse Parsing

This paper describes our system for the CoNLL-2016 Shared Task on Shallow Discourse Parsing on English. We adopt a cascaded framework consisting of nine components, among which six are casted as sequence labeling tasks and the remaining three are treated as classification problems. All our sequence labeling and classification models are implemented based on linear models with averaged perceptron training. Our feature sets are mostly borrowed from previous works. The main focus of our effort is to recall cases when Arg1 locates at sentences far before the connective phrase, with some yet limited success. 1 General Description This paper descirbes our participating system for CoNLL-2016 discourse parsing shared task (Xue et al., 2016). We participate in the closed track, and due to the time limitation, we focus on English. Given an document, which contains several paragraphs and each paragraph is composed of a few sentences, discourse parsing aims to identify explicit and non-explict discourse relations, including explicit connnective phrases (CP), explicit/non-explicit arguments and senses. Figure 1 presents a graphical illustration of the task. Following the official requirement, we use Section 2-21 of the PDTB 2.0 (Prasad et al., 2008; Prasad et al., 2014) as the training data, Section 22 as the development data, and Section 23 as the test data. A blind test is also used for evaluation. Table 1 presents the data statistics. Due to the complexity of the task, our system follows previous practice and employs a cas∗Correspondence author. Figure 1: Illustration of discourse parsing.