Comparing Experience- and Description-based Economic Preferences Across 11 Countries

Comparison of Experiences and Descriptions of Basic Economic Preferences in 11 Countries Background and Motivation Recent studies have shown that humans exhibit a high degree of context-dependency in encoding the value of rewards, which in some cases leads to suboptimal decisions. However, it remains unclear whether this computational limitation is...

Negation Mitigates Rather Than Inverts the Neural Representations of Adjectives

Background Introduction A notable characteristic of human language processing is our ability to combine stored lexical elements, i.e., words, as needed to flexibly generate or alter current meanings. At the core of this process is how we construct semantic representations in real time. Although research on the generation of syntactic structures has...

Prototype-Based Sample-Weighted Distillation Unified Framework Adapted to Missing Modality Sentiment Analysis

Prototype-Based Sample-Weighted Distillation Unified Framework Adapted to Missing Modality Sentiment Analysis

Application of a Prototype-Based Sample Weighted Distillation Unified Framework in Missing Modality Sentiment Analysis Research Background Sentiment analysis is a significant field in Natural Language Processing (NLP). With the development of social media platforms, people increasingly tend to express their emotions through short video clips. This ...

Inhibition Adaption on Pre-Trained Language Models

InA: Inhibition Adaptation Method on Pre-trained Language Models Pre-trained Language Models (LMs) have achieved significant results in Natural Language Processing (NLP) tasks. However, traditional fine-tuning methods suffer from the problem of redundant parameters, which affects efficiency and effectiveness. To address this challenge, this paper p...

Multi-Grained Visual Pivot-Guided Multi-Modal Neural Machine Translation with Text-Aware Cross-Modal Contrastive Disentangling

Multi-Grained Visual Pivot-Guided Multi-Modal Neural Machine Translation with Text-Aware Cross-Modal Contrastive Disentangling

Multi-Scale Vision-Centric Multi-Modal Neural Machine Translation: Text-Aware Cross-Modality Contrastive Decoupling Academic Background Multi-Modal Neural Machine Translation (MNMT) aims to incorporate language-independent visual information into text to enhance machine translation performance. However, due to the significant modal differences betw...