Papers Implicit Discourse Relation Classification

Implicit Discourse Relation Identification for Open-domain Dialogues

Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dial…

Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification

Implicit discourse relation classification is one of the most difficult steps in discourse parsing. The difficulty stems from the fact that the coherence relation must be inferred based on the content of the discourse re…

Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification

Implicit discourse relation classification is one of the most challenging and important tasks in discourse parsing, due to the lack of connective as strong linguistic cues. A principle bottleneck to further improvement i…

A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification

Identifying discourse relations that are not overtly marked with discourse connectives remains a challenging problem. The absence of explicit clues indicates a need for the combination of world knowledge and weak context…

Coherence Modeling Improves Implicit Discourse Relation Recognition

The research described in this paper examines how to learn linguistic knowledge associated with discourse relations from unlabeled corpora. We introduce an unsupervised learning method on text coherence that could produc…

Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph

We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure…

Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification

Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text. To tackle this task, recent studies have tried several deep learning methods bu…

Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification

Implicit discourse relation recognition is an extremely challenging task due to the lack of indicative connectives. Various neural network architectures have been proposed for this task recently, but most of them suffer …

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 the recognition. We propose …

On the Need of Cross Validation for Discourse Relation Classification

The task of implicit discourse relation classification has received increased attention in recent years, including two CoNNL shared tasks on the topic. Existing machine learning models for the task train on sections 2-21…

A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification

Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface Features

Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Surface features achieve good performance, but they are not readily applicable to other languages withou…

A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models

Implicit Discourse Relation Classification via Multi-Task Neural Networks

Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse frame…

A Latent Variable Recurrent Neural Network for Discourse Relation Language Models

This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences. A recurrent neural network g…

Comparing Word Representations for Implicit Discourse Relation Classification

Discovering Implicit Discourse Relations Through Brown Cluster Pair Representation and Coreference Patterns