no code implementations • Findings (EMNLP) 2021 • Jia Chen, Yike Wu, Shiwan Zhao, Qin Jin
Our analysis of caption models with SC loss shows that the performance degradation is caused by the increasingly noisy estimation of reward and baseline with fewer language resources.
no code implementations • 26 Jan 2024 • Nan Hu, Jiaoyan Chen, Yike Wu, Guilin Qi, Sheng Bi, Tongtong Wu, Jeff Z. Pan
The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention.
1 code implementation • 20 Sep 2023 • Yike Wu, Nan Hu, Sheng Bi, Guilin Qi, Jie Ren, Anhuan Xie, Wei Song
To this end, we propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements most informative for KGQA.
1 code implementation • 1 Jun 2023 • Mengting Hu, Yinhao Bai, Yike Wu, Zhen Zhang, Liqi Zhang, Hang Gao, Shiwan Zhao, Minlie Huang
We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens.
1 code implementation • 19 May 2023 • Yu Zhao, Yike Wu, Xiangrui Cai, Ying Zhang, Haiwei Zhang, Xiaojie Yuan
Our approach captures the unified correlation pattern of two kinds of information between entities, and explicitly models the fine-grained interaction between original entity information.
1 code implementation • 18 Mar 2023 • Nan Hu, Yike Wu, Guilin Qi, Dehai Min, Jiaoyan Chen, Jeff Z. Pan, Zafar Ali
Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP).
1 code implementation • 19 Oct 2022 • Mengting Hu, Yike Wu, Hang Gao, Yinhao Bai, Shiwan Zhao
By fine-tuning the pre-trained language model with these template orders, our approach improves the performance of quad prediction, and outperforms state-of-the-art methods significantly in low-resource settings.
Ranked #3 on Aspect-Based Sentiment Analysis (ABSA) on ACOS
1 code implementation • 17 Oct 2022 • Yu Zhao, Xiangrui Cai, Yike Wu, Haiwei Zhang, Ying Zhang, Guoqing Zhao, Ning Jiang
Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically.
1 code implementation • COLING 2022 • Yike Wu, Yu Zhao, Shiwan Zhao, Ying Zhang, Xiaojie Yuan, Guoqing Zhao, Ning Jiang
In this work, we define the training instances with the same question type but different answers as \textit{superficially similar instances}, and attribute the language priors to the confusion of VQA model on such instances.
no code implementations • 30 Aug 2021 • Yike Wu, Bo Zhang, Gang Yu, Weixi Zhang, Bin Wang, Tao Chen, Jiayuan Fan
The goal of few-shot fine-grained image classification is to recognize rarely seen fine-grained objects in the query set, given only a few samples of this class in the support set.
no code implementations • IJCNLP 2019 • Mengting Hu, Yike Wu, Shiwan Zhao, Honglei Guo, Renhong Cheng, Zhong Su
Cross-domain sentiment classification has drawn much attention in recent years.
no code implementations • 21 Aug 2019 • Yike Wu, Shiwan Zhao, Jia Chen, Ying Zhang, Xiaojie Yuan, Zhong Su
Improving the captioning performance on low-resource languages by leveraging English caption datasets has received increasing research interest in recent years.