【记录】研究生已阅文献清单

2020.9 - 2010.20

• 篇章关系分析研究综述

• The Penn Discourse TreeBank 2.0

• PDTB3-Annotation-Manual

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

2020.10 - 2020.11

ps: 2016之前论文每篇花1h，大概浏览

• 2008年

  • Easily Identifiable Discourse Relations

• 2009年

  • Automatic sense prediction for implicit discourse relations in text

• 2010年

  • A PDTB-Styled End-to-End Discourse Parser

  • PDTB XML: the XMLization of the Penn Discourse TreeBank 2.0

• 2012年

  • PDTB-style Discourse Annotation of Chinese Text

• 2013年

• 2014年

  • Towards Cross-Domain PDTB-Style Discourse Parsing

• 2015年

  • Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition

• 2016年

  • Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network

  • A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification

  • Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention

  • Implicit Discourse Relation Classification via Multi-Task Neural Networks

  • Bilingually-constrained Synthetic Data for Implicit Discourse Relation Recognition

• 2017年

  • Attention is all you need

• 2019年

  • RoBERTa: A Robustly Optimized BERT Pretraining Approach

2020.11 - 2020.12

• 2017

  • Adversarial Connective-exploiting Networks forImplicit Discourse Relation Classification

  • Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings

  • Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification

• 2018

  • Deep Enhanced Representation for Implicit Discourse Relation Recognition

  • Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning

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

2020.12 - 2021.01

• 2019

  • Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese

  • Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings