Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and is a fundamental task in discourse analysis. For Chinese, previous researches identify EDUs just through discriminating the functions of punctuations. In this paper, we argue that Chinese EDUs may not end at the punctuation positions and should follow the definition of EDU in RST-DT. With this definition, we conduct Chinese discourse segmentation with the help of English labeled data.Using discourse commonality between English and Chinese, we design an adversarial neural network framework to extract common language-independent features and language-specific features which are useful for discourse segmentation, when there is no or only a small scale of Chinese labeled data available. Experiments on discourse segmentation demonstrate that our models can leverage common features from bilingual data, and learn efficient Chinese-specific features from a small amount of Chinese labeled data, outperforming the baseline models.
Recently, much work have been conducted on English EDU segmentation. Most of those segmenters are based on statistical classifiers and leverage many manually extracted features in which lexical syntactic subtree features play an important role @cite_27 . Some modifications to the statistic model and features have made great improvements @cite_46 @cite_9 and @cite_44 achieved the best results ever. Besides, @cite_38 first used neural networks on this task and some neural network models have achieved comparable results using less artificial features. Since @cite_26 see the EDU segmentation task as a sequence labeling problem, we leverage the LSTM-CRF model @cite_47 @cite_29 which is proved effective in this problem.
@cite_38 @cite_47 @cite_26 @cite_9 @cite_29 @cite_44 @cite_27 @cite_46
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In example (1), a sentence from a Wall Street Journal article taken from the Penn TreeBank corpus is further segmented into four EDUs, (1a), (1b), (1c) and (1d) (RST, 2002). Discourse segmentation, clearly, is not as easy as sentence boundary detection. The lack of consensus with regards to what constitutes an elementary discourse unit adds to the difficulty. Building a rule based discourse segmenter can be a tedious task since these rules would have to be based on the underlying grammar of the particular parser that is to be used. Therefore, we adopted a neural network model for automatically building a discourse segmenter from an underlying corpus of segmented text. We chose to use part-of-speech tags, syntactic information, discourse cues and punctuation. Our ultimate goal is to build a discourse parser that uses this discourse segmenter. The data that we used to train and test our discourse segmenter is the RST-DT (RST, 2002) corpus. The corpus contains 385 Wall Street Journal articles from the Penn Treebank. The training set consists of 347 articles for a total of 6132 sentences, whilst the test set contains 38 articles for a total of 991 sentences. The RST-DT corpus provides us with pairs of sentences and EDUs. For the syntactic structure of the sentences, we have used both the gold standard Penn Treebank data and syntactic parse trees generated by (Charniak, 2000). As regards the discourse cues, we used a list of 168 possible discourse markers. In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is the first to apply a bidirectional LSTM CRF (denoted as BI-LSTM-CRF) model to NLP benchmark sequence tagging data sets. We show that the BI-LSTM-CRF model can efficiently use both past and future input features thanks to a bidirectional LSTM component. It can also use sentence level tag information thanks to a CRF layer. The BI-LSTM-CRF model can produce state of the art (or close to) accuracy on POS, chunking and NER data sets. In addition, it is robust and has less dependence on word embedding as compared to previous observations. In this paper we consider the problem of analysing sentence-level discourse structure. We introduce discourse chunking (i.e., the identification of intra-sentential nucleus and satellite spans) as an alternative to full-scale discourse parsing. Our experiments show that the proposed modelling approach yields results comparable to state-of-the-art while exploiting knowledge-lean features and small amounts of discourse annotations. We also demonstrate how discourse chunking can be successfully applied to a sentence compression task. Clauses and sentences rarely stand on their own in an actual discourse; rather, the relationship between them carries important information that allows the discourse to express a meaning as a whole beyond the sum of its individual parts. Rhetorical analysis seeks to uncover this coherence structure. In this article, we present CODRA-a COmplete probabilistic Discriminative framework for performing Rhetorical Analysis in accordance with Rhetorical Structure Theory, which posits a tree representation of a discourse. CODRA comprises a discourse segmenter and a discourse parser. First, the discourse segmenter, which is based on a binary classifier, identifies the elementary discourse units in a given text. Then the discourse parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intra-sentential parsing and the other for multi-sentential parsing. We present two approaches to combine these two stages of parsing effectively. By conducting a series of empirical evaluations over two different data sets, we demonstrate that CODRA significantly outperforms the state-of-the-art, often by a wide margin. We also show that a reranking of the k-best parse hypotheses generated by CODRA can potentially improve the accuracy even further. State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF. Our system is truly end-to-end, requiring no feature engineering or data pre-processing, thus making it applicable to a wide range of sequence labeling tasks. We evaluate our system on two data sets for two sequence labeling tasks --- Penn Treebank WSJ corpus for part-of-speech (POS) tagging and CoNLL 2003 corpus for named entity recognition (NER). We obtain state-of-the-art performance on both the two data --- 97.55 accuracy for POS tagging and 91.21 F1 for NER. This paper presents a discriminative reranking model for the discourse segmentation task, the first step in a discourse parsing system. Our model exploits subtree features to rerank N-best outputs of a base segmenter, which uses syntactic and lexical features in a CRF framework. Experimental results on the RST Discourse Treebank corpus show that our model outperforms existing discourse segmenters in both settings that use gold standard Penn Treebank parse trees and Stanford parse trees. We introduce two probabilistic models that can be used to identify elementary discourse units and build sentence-level discourse parse trees. The models use syntactic and lexical features. A discourse parsing algorithm that implements these models derives discourse parse trees with an error reduction of 18.8 over a state-of-the-art decision-based discourse parser. A set of empirical evaluations shows that our discourse parsing model is sophisticated enough to yield discourse trees at an accuracy level that matches near-human levels of performance. We investigate different feature sets for performing automatic sentence-level discourse segmentation within a general machine learning approach, including features derived from either finite-state or contextfree annotations. We achieve the best reported performance on this task, and demonstrate that our SPADE-inspired context-free features are critical to achieving this level of accuracy. This counters recent results suggesting that purely finite-state approaches can perform competitively.