Implicit Discourse Relation Recognition

Implicit discourse relation recognition is a challenging task in the natural language processing field, but important to many applications such as question answering, summarization and so on. Previous research used …...

To overcome the shortage of labeled data for implicit discourse relation recogni- tion, previous works attempted to auto- matically generate training data by remov- ing explicit discourse connectives from sentences …...

We propose a method for implicit discourse relation recognition using a recursive neural network (RNN). Many previous studies have used the word-pair feature to compare the meaning of two sentences …...

Implicit discourse relation recognition re- mains a serious challenge due to the ab- sence of discourse connectives. In this pa- per, we propose a Shallow Convolutional Neural Network (SCNN) for …...

AbstractFor the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem. Neural models provide a solution with distributed representations, which could encode …...

Implicit discourse relation recognition remains a challenging task as state-of-the-art approaches reach F1 scores ranging from 9.95% to 37.67% on the 2016 CoNLL shared task. In our work, we explore …...

AbstractImplicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface …...

AbstractImplicit 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 separately, the …...

AbstractWe 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 …...

AbstractImplicit discourse relation recognition is a challenging task due to the lack of connectives as strong linguistic clues. Previous methods primarily encode two arguments separately or extract the specific interaction …...

AbstractImplicit discourse relation recognition (IDRR) is a critical task in discourse analysis. Previous studies only regard it as a classification task and lack an in-depth understanding of the semantics of …...

AbstractImplicit Discourse Relation Recognition (IDRR) is to detect and classify relation sense between two text segments without an explicit connective. Vanilla pre-train and fine-tuning paradigm builds upon a Pre-trained Language …...

AbstractImplicit discourse relation recognition is a challenging task that involves identifying the sense or senses that hold between two adjacent spans of text, in the absense of an explicit connective …...

AbstractImplicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis due to the absence of connectives. Most existing methods utilize one-hot labels as the sole optimization target, ignoring …...

AbstractDiscourse analysis is an important task because it models intrinsic semantic structures between sentences in a document. Discourse markers are natural representations of discourse in our daily language. One challenge …...