Attention mechanisms have improved the performance of NLP tasks while allowing models
to remain explainable. Self-attention is currently widely used, however interpretability is …

Constituency parsing is a fundamental and important task for natural language
understanding, where a good representation of contextual information can help this task. N …

Parsing sentences into syntax trees can benefit downstream applications in NLP. Transition-
based parsers build trees by executing actions in a state transition system. They are …

This paper proposes a state-of-the-art recurrent neural network (RNN) language model that
combines probability distributions computed not only from a final RNN layer but also from …

To promote and further develop RST-style discourse parsing models, we need a strong
baseline that can be regarded as a reference for reporting reliable experimental results. This …

The idea of subword-based word embeddings has been proposed in the literature, mainly
for solving the out-of-vocabulary (OOV) word problem observed in standard word-based …

The development of large language models (LLMs) brings significant changes to the field of
natural language processing (NLP), enabling remarkable performance in various high-level …

Sequence-to-sequence constituent parsing requires a linearization to represent trees as
sequences. Top-down tree linearizations, which can be based on brackets or shift-reduce …

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

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 …