Large, pre-trained language models (PLMs) such as BERT and GPT have drastically
changed the Natural Language Processing (NLP) field. For numerous NLP tasks …

We design a predictive layer for structured-output prediction (SOP) that can be plugged into
any neural network guaranteeing its predictions are consistent with a set of predefined …

Despite their impressive performance, large language models (LMs) still struggle with
reliably generating complex output structures when not finetuned to follow the required …

Despite their impressive performance in NLP, self-attention networks were recently proved
to be limited for processing formal languages with hierarchical structure, such as $\mathsf …

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims
to align aspects and corresponding sentiments for aspect-specific sentiment polarity …

We report two essential improvements in readability assessment: 1. three novel features in
advanced semantics and 2. the timely evidence that traditional ML models (eg Random …

One strategy to recognize nested entities is to enumerate overlapped entity spans for
classification. However, current models independently verify every entity span, which …

The increasing size of large language models (LLMs) has introduced challenges in their
training and inference. Removing model components is perceived as a solution to tackle the …

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 …

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 …