面向互联网复杂语境的文本隐式情感分析研究 1月 1, 2022 随着互联网语境的复杂性越来越强，含有隐式情感的文本越来越多，如舆情系统中反讽等隐式情感在互联网频繁出现，给目前的情感分析系统带来了巨大的压力和挑战。隐式情感分析则通过对典型隐式情感，进行分析识别。 NLP Yequan Wang 研究员，团队主管 我的研究兴趣包含大模型，具身智能和自然语言处理等。 出版物 Research without Re-search: Maximal Update Parametrization Yields Accurate Loss Prediction across Scales We find that Maximal Update parametrization (uP) enables accurate fitting of scaling laws for hyperparameters close to common loss basins, without any search. Thus, different models can be directly compared on large scales with loss prediction even before the training starts. We propose a new paradigm as a first step towards reliable academic research for any model scale without heavy computation. Yiqun Yao, Yequan Wang PDF 引用 项目 CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts We approach comparative opinion classification through prompt learning, taking the advantage of embedded knowledge in pre-trained language model. We design a twin framework with dual prompts, named CORT. This extremely simple model delivers state-of-the-art and robust performance on all benchmark datasets for comparative opinion classification. We believe CORT well serves as a new baseline for comparative opinion classification. Yequan Wang, Hengran Zhang, Aixin Sun, Xuying Meng PDF 引用 代码 数据集 项目 CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction We propose the Context and Former-Label Enhanced Net (CofeNet) for quotation extraction. CofeNet is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets and one proprietary dataset, we show that our achieves state-of-the-art performance on complicated quotation extraction. Yequan Wang, Xiang Li, Aixin Sun, Xuying Meng, Huaming Liao, Jiafeng Guo PDF 引用 代码 数据集 项目
