Large language models (LLMs) can learn to perform a wide range of natural language tasks
from just a handful of in-context examples. However, for generating strings from highly …

Dependency trees have been intensively used with graph neural networks for aspect-based
sentiment classification. Though being effective, such methods rely on external dependency …

Abstract Language modeling on large-scale datasets improves performance of various
downstream tasks. The validation pre-training loss is often used as the evaluation metric for …

Sequence-to-sequence learning with neural networks has become the de facto standard for
sequence modeling. This approach typically models the local distribution over the next …

Nested named entity recognition (NER) has been receiving increasing attention.
Recently,(Fu et al, 2021) adapt a span-based constituency parser to tackle nested NER …

Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community.
Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite …

We introduce a new task, unsupervised vision-language (VL) grammar induction. Given an
image-caption pair, the goal is to extract a shared hierarchical structure for both image and …

We describe a neural transducer that maintains the flexibility of standard sequence-to-
sequence (seq2seq) models while incorporating hierarchical phrases as a source of …

Past work on unsupervised parsing is constrained to written form. In this paper, we present
the first study on unsupervised spoken constituency parsing given unlabeled spoken …

We present ReCAT, a recursive composition augmented Transformer that is able to explicitly
model hierarchical syntactic structures of raw texts without relying on gold trees during both …