Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition
We tackle implicit discourse relation recognition. Both self-attention and interactive-attention mechanisms have been applied for attention-aware representation learning, which improves the current discourse analysis models. To take advantages of the two attention mechanisms simultaneously, we develop a propagative attention learning model using a cross-coupled two-channel network. We experiment on Penn Discourse Treebank. The test results demonstrate that our model yields substantial improvements over the baselines (BiLSTM and BERT).
Code (0)
등록된 구현이 없습니다.
Tasks
RelationRepresentation LearningSimilar Papers 제목 키워드 기반
Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning
Implicit discourse relation recognition aims to understand and annotate the latent relations between two discourse arguments, such as temporal, comparison, etc. Most previous methods encode two discourse arguments separa…
RelationSparse LearningText SummarizationA 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 p…
TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature
Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show …
Graph Neural NetworkRecommendation SystemsRecognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention
Recognizing implicit discourse relations is a challenging but important task in the field of Natural Language Processing. For such a complex text processing task, different from previous studies, we argue that it is nece…
A Side-by-side Comparison of Transformers for English Implicit Discourse Relation Classification
Though discourse parsing can help multiple NLP fields, there has been no wide language model search done on implicit discourse relation classification. This hinders researchers from fully utilizing public-available model…
Discourse ParsingImplicit Discourse Relation ClassificationLanguage ModelingLanguage Modelling+3