In the deep learning (DL) era, parsing models are extremely simplified with little hurt on
performance, thanks to the remarkable capability of multi-layer BiLSTMs in context …

This paper aims to enhance the few-shot relation classification especially for sentences that
jointly describe multiple relations. Due to the fact that some relations usually keep high co …

Recent analyses suggest that encoders pretrained for language modeling capture certain
morpho-syntactic structure. However, probing frameworks for word vectors still do not report …

This paper focuses on the multi-choice tasks, which aim to select the correct choice for a
given query by reasoning over texts, such as sentences and passages. Benefiting from the …

We propose a method to automate orthodontic diagnosis with natural language processing.
It is worthwhile to assist dentists with such technology to prevent errors by inexperienced …

The introduction of pre-trained transformer-based contextualized word embeddings has led
to considerable improvements in the accuracy of graph-based parsers for frameworks such …

We propose a new method for projective dependency parsing based on headed spans. In a
projective dependency tree, the largest subtree rooted at each word covers a contiguous …

Pre-trained language models that learn contextualized word representations from a large un-
annotated corpus have become a standard component for many state-of-the-art NLP …

Dependency-based approaches to syntactic analysis assume that syntactic structure can be
analyzed in terms of binary asymmetric dependency relations holding between elementary …

Graph-based methods, which decompose the score of a dependency tree into scores of
dependency arcs, are popular in dependency parsing for decades. Recently,\citet …