Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …

Large language models (LLMs) are capable of successfully performing many language
processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify …

In the last decade, the problem of computational metaphor processing has garnered
immense attention from the domains of computational linguistics and cognition. A wide …

Metaphor is not only a linguistic phenomenon but also reflects the concept projection
between source and target domains in human cognition. Previous sequence tagging-based …

Metaphor is a special linguistic phenomenon, challenging diverse natural language
processing tasks. Previous works focused on either metaphor identification or domain …

Multi-task learning (MTL) has been widely applied in Natural Language Processing. A major
task and its associated auxiliary tasks share the same encoder; hence, an MTL encoder can …

Sentiment analysis has been a popular field in natural language processing. Sentiments can
be expressed explicitly or implicitly. Most current studies on sentiment analysis focus on the …

Automated metaphor detection is a challenging task to identify metaphorical expressions of
words in a sentence. To tackle this problem, we adopt pre-trained contextualized models …

Compared with explicit sentiment analysis that attracts considerable attention, implicit
sentiment analysis is a more difficult task due to the lack of sentimental words. The abundant …

In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn
and incorporate FrameNet Embeddings for concept-level metaphor detection. FrameBERT …