Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

Document-level relation extraction requires integrating information within and across
multiple sentences of a document and capturing complex interactions between inter …

Prior work on pretrained sentence embeddings and benchmarks focus on the capabilities of
stand-alone sentences. We propose DiscoEval, a test suite of tasks to evaluate whether …

Discourse processing suffers from data sparsity, especially for dialogues. As a result, we
explore approaches to build discourse structures for dialogues, based on attention matrices …

Neural network based deep learning methods aim to learn representations of data and have
produced state-of-the-art results in many natural language processing (NLP) tasks …

The lack of large and diverse discourse treebanks hinders the application of data-driven
approaches, such as deep-learning, to RST-style discourse parsing. In this work, we present …

In this paper, we propose an entity-based neural local coherence model which is
linguistically more sound than previously proposed neural coherence models. Recent …

Due to the success of pre-trained language models, versions of languages other than
English have been released in recent years. This fact implies the need for resources to …

Discourse parsing could not yet take full advantage of the neural NLP revolution, mostly due
to the lack of annotated datasets. We propose a novel approach that uses distant …

Previous neural coherence models have focused on identifying semantic relations between
adjacent sentences. However, they do not have the means to exploit structural information …