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

A discourse containing one or more sentences describes daily issues and events for people
to communicate their thoughts and opinions. As sentences are normally consist of multiple …

Discourse parsing is an important research area in natural language processing (NLP),
which aims to parse the discourse structure of coherent sentences. In this survey, we …

Research into the area of multiparty dialog has grown considerably over recent years. We
present the Molweni dataset, a machine reading comprehension (MRC) dataset with …

Implicit 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 …

BERT, a neural network-based language model pre-trained on large corpora, is a
breakthrough in natural language processing, significantly outperforming previous state-of …

Knowledge retrieval and reasoning are two key stages in multi-hop question answering (QA)
at web scale. Existing approaches suffer from low confidence when retrieving evidence facts …

Implicit relation classification on Penn Discourse TreeBank (PDTB) 2.0 is a common
benchmark task for evaluating the understanding of discourse relations. However, the lack of …

Answer selection (AS) is a critical subtask of the open‐domain question answering (QA)
problem. The present paper proposes a method called RLAS‐BIABC for AS, which is …

The task of dialogue generation aims to automatically provide responses given previous
utterances. Tracking dialogue states is an important ingredient in dialogue generation for …