A Refined End-to-End Discourse Parser

Abstract

The CoNLL-2015 shared task focuses on shallow discourse parsing, which takes a piece of newswire text as input and returns the discourse relations in a PDTB style. In this paper, we describe our discourse parser that participated in the shared task. We use 9 components to construct the whole parser to identify discourse connectives, label arguments and classify the sense of Explicit or Non-Explicit relations in free texts. Compared to previous discourse parser, new components and features are added in our system, which further improves the overall performance of the discourse parser. Our parser ranks the first on two test datasets, i.e., PDTB Section 23 and a blind test dataset.

OPT: Oslo–Potsdam–Teesside. Pipelining Rules, Rankers, and Classifier Ensembles for Shallow Discourse Parsing

“…With an end-toend performance of 27.77 F 1 on the English 'blind' test data, our system advances the previous state of the art (Wang & Lan, 2015) by close to four F 1 points, with particularly good results for the argument identification sub-tasks. …”

“…However, the Arg1s of explicit relations frequently occur in the same sentence (SS), so, following Wang & Lan (2015), we attempt to learn a classification function to predict whether these are in SS or PS. Considering all features proposed by Wang & Lan, but under cross-validation on the training set, we found that the significantly informative features were limited to: the connective form, the syntactic path from connective to root, the connective position in sentence (tertiles), and a bigram of the connective and following token part-of-speech.…”

SoNLP-DP System for ConLL-2016 English Shallow Discourse Parsing

“…In the blind test, our parser achieve a better result than the best system of last year (Wang and Lan, 2015 Table 2: the supplementary F1 score of our system.…”

“…Then, the supplementary results provided by the shared task organizes are reported. (Wang and Lan, 2015) 46.37 91.86 24.00 Table 1: the official F1 score of our system.…”

Modelling the Interpretation of Discourse Connectives by Bayesian Pragmatics

“…We employ the winning parser of the CONLL shared task (Wang and Lan, 2015). The parser is also trained on Sections 2-22 of PDTB, and thus does not overlap with our test set.…”