Boosting implicit discourse relation recognition with connective-based word embeddings

Abstract(#br)Implicit discourse relation recognition is the performance bottleneck of discourse structure analysis. To alleviate the shortage of training data, previous methods usually use explicit discourse data, which are naturally labeled by connectives, as additional training data. However, it is often difficult for them to integrate large amounts of explicit discourse data because of the noise problem. In this paper, we propose a simple and effective method to leverage massive explicit discourse data. Specifically, we learn connective-based word embeddings ( CBWE ) by performing connective classification on explicit discourse data. The learned CBWE is capable of capturing discourse relationships between words, and can be used as pre-trained word embeddings for implicit discourse relation recognition. On both the English PDTB and Chinese CDTB data sets, using CBWE achieves significant improvements over baselines with general word embeddings, and better performance than baselines integrating explicit discourse data. By combining CBWE with a strong baseline, we achieve the state-of-the-art performance

A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations

We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model's ability to selectively focus on the relevant parts of an input sequence.Comment: To appear at ACL2017, code available at https://github.com/sronnqvist/discourse-ablst

Historians and Consciousness: The Modern Politics of the Taiping Heavenly Kingdom

This is a publisher's version of an article published in the journal Social Research in 1987. The offprint is posted here in accordance with existing publisher policy, or by special permission via correspondence.tru

Cognitive Case Studies of Chinese in Discourse Analysis and Classroom Teaching

In the first case study, a piece of recent BBC news reported on Chinese netizens leaving random but funny comments on a Western website attracted people’s attention. A closer look at those comments reveals that understanding the Chinese netizens’ comments requires metaphorical and cultural knowledge. This study starts with theoretical explanations on metaphor from different perspectives and then presents cultural variations in Western and Eastern metaphors. With theories and cultures grounded, a detailed analysis was done to show people without Chinese cultural background how to understand the Chinese Internet metaphors that drew people’s attention. The second case study takes a critical discourse analysis approach to investigate metaphors in political discourses in Chinese. Five pieces of Chinese government reports were studied. Metaphor, revealing how we think about the world, encompasses cultural and social factors. It functions differently for different communication purpose. The current study proves the persuasive role of metaphor in political discourse which can evoke people’s emotional response, for the governing group to have an ideological influence on how people conceptualize things. The third case study applies word recognition as part of the classroom instruction in the form of meaning, character and pronunciation, to investigate whether training on either two of the three constituents can improve students’ vocabulary acquisition. The results showed that, for new learners, the bond between characters and either pronunciation or meaning is weak. Training in either character with meaning or character with pronunciation has positive effects and training to enhance the relation between character and pronunciation also retrieve meaning, which brings a three-way benefit

GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking (DISRPT2019