Qingqing Zhao


2024

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SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification
Yuhan Xia | Qingqing Zhao | Yunfei Long | Ge Xu | Jia Wang
Proceedings of the Workshop on Cognitive Aspects of the Lexicon @ LREC-COLING 2024

In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains. Yet, the significant influence of sensory experiences on emotional responses is undeniable. The natural language processing (NLP) community has often missed the opportunity to merge sensory knowledge with emotion classification. To address this gap, we propose SensoryT5, a neurocognitive approach that integrates sensory information into the T5 (Text-to-Text Transfer Transformer) model, designed specifically for fine-grained emotion classification. This methodology incorporates sensory cues into the T5’s attention mechanism, enabling a harmonious balance between contextual understanding and sensory awareness. The resulting model amplifies the richness of emotional representations. In rigorous tests across various detailed emotion classification datasets, SensoryT5 showcases improved performance, surpassing both the foundational T5 model and current state-of-the-art works. Notably, SensoryT5’s success signifies a pivotal change in the NLP domain, highlighting the potential influence of neurocognitive data in refining machine learning models’ emotional sensitivity.

2023

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A Unified Framework for Synaesthesia Analysis
Kun Sheng | Zhongqing Wang | Qingqing Zhao | Xiaotong Jiang | Guodong Zhou
Findings of the Association for Computational Linguistics: EMNLP 2023

Synaesthesia refers to the description of perceptions in one sensory modality through concepts from other modalities. It involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought and action, which makes understanding it challenging. As a means of cognition, synaesthesia is rendered by more than sensory modalities, cue and stimulus can also play an important role in expressing and understanding it. In addition, understanding synaesthesia involves many cognitive efforts, such as identifying the semantic relationship between sensory words and modalities. Therefore, we propose a unified framework focusing on annotating all kinds of synaesthetic elements and fully exploring the relationship among them. In particular, we introduce a new annotation scheme, including sensory modalities as well as their cues and stimuli, which facilitate understanding synaesthetic information collectively. We further design a structure generation model to capture the relations among synaesthetic elements and generate them jointly. Through extensive experiments, the importance of proposed dataset can be verified by the statistics and progressive performances. In addition, our proposed model yields state-of-the-art results, demonstrating its effectiveness.

2022

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Chinese Synesthesia Detection: New Dataset and Models
Xiaotong Jiang | Qingqing Zhao | Yunfei Long | Zhongqing Wang
Findings of the Association for Computational Linguistics: ACL 2022

In this paper, we introduce a new task called synesthesia detection, which aims to extract the sensory word of a sentence, and to predict the original and synesthetic sensory modalities of the corresponding sensory word. Synesthesia refers to the description of perceptions in one sensory modality through concepts from other modalities. It involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought and action, which makes it become a bridge between figurative linguistic phenomenon and abstract cognition, and thus be helpful to understand the deep semantics. To address this, we construct a large-scale human-annotated Chinese synesthesia dataset, which contains 7,217 annotated sentences accompanied by 187 sensory words. Based on this dataset, we propose a family of strong and representative baseline models. Upon these baselines, we further propose a radical-based neural network model to identify the boundary of the sensory word, and to jointly detect the original and synesthetic sensory modalities for the word. Through extensive experiments, we observe that the importance of the proposed task and dataset can be verified by the statistics and progressive performances. In addition, our proposed model achieves state-of-the-art results on the synesthesia dataset.

2015

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Auditory Synaesthesia and Near Synonyms: A Corpus-Based Analysis of sheng1 and yin1 in Mandarin Chinese
Qingqing Zhao | Chu-Ren Huang | Hongzhi Xu
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

2013

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Qualia Relations in Metaphorical Noun-Noun Compounds
Zuoyan Song | Qingqing Zhao
Proceedings of the 6th International Conference on Generative Approaches to the Lexicon (GL2013)