We analyze narrative text spans (also named as arguments) in this paper, and merely concentrate on the recognition of semantic relations between them. Because larger-grain linguistic units (such as phrase, chunk) are ...
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ISBN:
(纸本)9783030322335;9783030322328
We analyze narrative text spans (also named as arguments) in this paper, and merely concentrate on the recognition of semantic relations between them. Because larger-grain linguistic units (such as phrase, chunk) are inherently cohesive in semantics, they generally contribute more than words in the representation of sentence-level text spans. On the basis of it, we propose the multi-grain representation learning method, which uses different convolution filters to form larger-grain linguistic units. Methodologically, Bi-LSTM based attention mechanism is used to strengthen suitable-grain representation, which is concatenated with word-level representation to form multi-grain representation. In addition, we employ bidirectional interactive attention mechanism to focus on the key information in the arguments. Experimental results on the Penn discourse TreeBank show that the proposed method is effective.
Different words in discourse arguments usually have varying contributions on the recognition of implicitdiscourserelations. Following this intuition, we propose two attention-based neural networks, namely inner atte...
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Different words in discourse arguments usually have varying contributions on the recognition of implicitdiscourserelations. Following this intuition, we propose two attention-based neural networks, namely inner attention model and outer attention model, to learn better discourse representation by automatically estimating the degrees of relevance of words to discourserelations. The former model only utilizes the information inside discourse arguments, while the latter model builds upon an outside semantic memory to exploit general world knowledge. Both models are capable of assigning more weights to relation-relevant words, and operate in an end-to-end manner. Upon these two models, we further propose a full attention model that combines their strengths into a unified framework. Extensive experiments on the PDTB data set show that our model significantly benefits from highlighting relation-relevant words and yields competitive and even better results against several state-of-the-art systems. (C) 2017 Elsevier B.V. All rights reserved.
A lack of labeled corpora obstructs the research progress on implicit discourse relation recognition （DRR） for Chinese, while there are some available discourse corpora in other languages, such as English. In this p...
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A lack of labeled corpora obstructs the research progress on implicit discourse relation recognition （DRR） for Chinese, while there are some available discourse corpora in other languages, such as English. In this paper, we propose a cross-lingual implicit DRR framework that exploits an available English corpus for the Chinese DRR task. We use machine translation to generate Chinese instances from a labeled English discourse corpus. In this way, each instance has two independent views： Chinese and English views. Then we train two classifiers in Chinese and English in a co-training way, which exploits unlabeled Chinese data to implement better implicit DRR for Chinese. Experimental results demonstrate the effectiveness of our method.
Recognizing implicitdiscourserelations is an important but challenging task in discourse understanding. To alleviate the shortage of labeled data, previous work automatically generates synthetic implicit data (SynDa...
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Recognizing implicitdiscourserelations is an important but challenging task in discourse understanding. To alleviate the shortage of labeled data, previous work automatically generates synthetic implicit data (SynData) as additional training data, by removing connectives from explicit discourse instances. Although SynData has been proven useful for implicit discourse relation recognition, it also has the meaning shift problem and the domain problem. In this paper, we first propose to use bilingually-constrained synthetic implicit data (BiSynData) to enrich the training data, which can alleviate the drawbacks of SynData. Our BiSynData is constructed from a bilingual sentence-aligned corpus according to the implicit/explicit mismatch between different languages. Then we design a multi-task neural network model to incorporate our BiSynData to benefit implicit discourse relation recognition. Experimental results on both the English PDTB and Chinese CDTB data sets show that our proposed method achieves significant improvements over baselines using SynData. (C) 2017 Published by Elsevier B.V.
implicit discourse relation recognition aims to discover the semantic relation between two sentences where the discourse connective is absent. Due to the lack of labeled data, previous work tries to generate additiona...
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implicit discourse relation recognition aims to discover the semantic relation between two sentences where the discourse connective is absent. Due to the lack of labeled data, previous work tries to generate additional training data automatically by removing discourse connectives from explicit discourserelation instances. However, using these artificial data indiscriminately has been proven to degrade the performance of implicit discourse relation recognition. To address this problem, we propose a co-training approach based on manual features and distributed features, which identifies useful instances from these artificial data to enlarge the labeled data. In addition, the distributed features are learned via recursive autoencoder based approaches, capable of capturing to some extent the semantics of sentences which is valuable for implicit discourse relation recognition. Experiment results on both the PDTB and CDTB data sets indicate that: (1) The learned distributed features are complementary to the manual features, and thus suitable for co-training. (2) Our proposed co-training approach can use these artificial data effectively, and significantly outperforms the baselines.
implicit discourse relation recognition has emerged to be a difficult problem due to lacking connectives in text. Traditional methods mainly adopted supervised methods which require a large amount of labeled training ...
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implicit discourse relation recognition has emerged to be a difficult problem due to lacking connectives in text. Traditional methods mainly adopted supervised methods which require a large amount of labeled training data. In this work, we explore two novel semi-supervised approaches to classify implicitdiscourserelations with a small amount of labeled data, i.e. self-training and co-training. Results on Penn discourse Treebank(PDTB) 2.0 show that our proposed models outperform traditional methods with small training size. The discovery presented in this paper can be helpful for future research on recognizing implicitdiscourserelations.
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