Recent work has shown that generation from a prompted or fine-tuned language model can
perform well at semantic parsing when the output is constrained to be a valid semantic …

Constituency parsing and nested named entity recognition (NER) are similar tasks since
they both aim to predict a collection of nested and non-crossing spans. In this work, we cast …

Constituency parsing is a fundamental yet unsolved natural language processing task. In
this paper, we explore the potential of recent large language models (LLMs) that have …

Previous studies have made great advances in RST discourse parsing through specific
neural frameworks or features, but they usually split the parsing process into two subtasks …

Parsing is a core natural language processing technique that can be used to obtain the
structure underlying sentences in human languages. Named entity recognition (NER) is the …

We propose a novel method for calculating PARSEVAL measures to evaluate constituent
parsing results. Previous constituent parsing evaluation techniques were constrained by the …

Abstract Sequence-to-Sequence (S2S) models have achieved remarkable success on
various text generation tasks. However, learning complex structures with S2S models …

In order to achieve deep natural language understanding, syntactic constituent parsing is a
vital step, highly demanded by many artificial intelligence systems to process both text and …

We introduce a generic seq2seq parsing framework that casts constituency parsing
problems (syntactic and discourse parsing) into a series of conditional splitting decisions …

We investigate the ability of transformer models to approximate the CKY algorithm, using
them to directly predict a sentence's parse and thus avoid the CKY algorithm's cubic …