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Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve theExpand
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A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification
Discourse parsing is considered as one of the most challenging natural language processing (NLP) tasks. Implicit discourse relation classification is the bottleneck for discourse parsing. Without theExpand
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Automatic Article Commenting: the Task and Dataset
Comments of online articles provide extended views and improve user engagement. Automatically making comments thus become a valuable functionality for online forums, intelligent chatbots, etc. ThisExpand
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Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous.Expand
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Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings
For the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem. Neural models provide a solution with distributedExpand
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Probabilistic Graph-based Dependency Parsing with Convolutional Neural Network
This paper presents neural probabilistic parsing models which explore up to thirdorder graph-based parsing with maximum likelihood training criteria. Two neural network extensions are exploited forExpand
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Shallow Discourse Parsing Using Convolutional Neural Network
This paper describes a discourse parsing system for our participation in the CoNLL 2016 Shared Task. We focus on the supplementary task: Sense Classification, especially the Non-Explicit one which isExpand
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Deep Generative Models with Learnable Knowledge Constraints
The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end DGMs. PosteriorExpand
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Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization,Expand
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Social Bias Frames: Reasoning about Social and Power Implications of Language
Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but all the implied meanings thatExpand
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