no code implementations • SemEval (NAACL) 2022 • Zhou Mengyuan, Dou Hu, Mengfei Yuan, Jin Zhi, Xiyang Du, Lianxin Jiang, Yang Mo, Xiaofeng Shi
This paper describes our system used in the SemEval-2022 Task 7(Roth et al.): Identifying Plausible Clarifications of Implicit and Under-specified Phrases.
no code implementations • SemEval (NAACL) 2022 • Zhihao Ruan, Xiaolong Hou, Lianxin Jiang
This paper describes our system used in the SemEval-2022 Task 09: R2VQ - Competence-based Multimodal Question Answering.
no code implementations • SemEval (NAACL) 2022 • Jin Zhi, Zhou Mengyuan, Mengfei Yuan, Dou Hu, Xiyang Du, Lianxin Jiang, Yang Mo, Xiaofeng Shi
This paper describes our system used in the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI).
no code implementations • SemEval (NAACL) 2022 • Mengfei Yuan, Zhou Mengyuan, Lianxin Jiang, Yang Mo, Xiaofeng Shi
This paper describes the systematic approach applied in “SemEval-2022 Task 6 (iSarcasmEval) : Intended Sarcasm Detection in English and Arabic”.
no code implementations • SemEval (NAACL) 2022 • Xiyang Du, Dou Hu, Jin Zhi, Lianxin Jiang, Xiaofeng Shi
This paper describes the method we utilized in the SemEval-2022 Task 6 iSarcasmEval: Intended Sarcasm Detection In English and Arabic.
no code implementations • 1 Nov 2022 • Dou Hu, Xiaolong Hou, Xiyang Du, Mengyuan Zhou, Lianxin Jiang, Yang Mo, Xiaofeng Shi
Pre-trained language models have achieved promising performance on general benchmarks, but underperform when migrated to a specific domain.
no code implementations • SemEval (NAACL) 2022 • Dou Hu, Mengyuan Zhou, Xiyang Du, Mengfei Yuan, Meizhi Jin, Lianxin Jiang, Yang Mo, Xiaofeng Shi
Patronizing and condescending language (PCL) has a large harmful impact and is difficult to detect, both for human judges and existing NLP systems.
Ranked #1 on Binary Condescension Detection on DPM
Binary Condescension Detection Multi-label Condescension Detection +2
1 code implementation • 4 Mar 2022 • Dou Hu, Xiaolong Hou, Lingwei Wei, Lianxin Jiang, Yang Mo
For multimodal ERC, it is vital to understand context and fuse modality information in conversations.
Ranked #23 on Emotion Recognition in Conversation on IEMOCAP
no code implementations • 7 Sep 2021 • Mengyuan Zhou, Jian Ma, Haiqin Yang, Lianxin Jiang, Yang Mo
In this paper, we target at how to further improve the token representations on the language models.
no code implementations • SEMEVAL 2021 • Gang Rao, Maochang Li, Xiaolong Hou, Lianxin Jiang, Yang Mo, Jianping Shen
In this paper we propose a contextual attention based model with two-stage fine-tune training using RoBERTa.
no code implementations • SEMEVAL 2021 • Shuyi Xie, Jian Ma, Haiqin Yang, Lianxin Jiang, Yang Mo, Jianping Shen
Second, we construct a new vector on the fine-tuned embeddings from XLM-RoBERTa and feed it to a fully-connected network to output the probability of whether the target word in the context has the same meaning or not.
no code implementations • SEMEVAL 2021 • Jian Ma, Shuyi Xie, Haiqin Yang, Lianxin Jiang, Mengyuan Zhou, Xiaoyi Ruan, Yang Mo
This paper describes MagicPai's system for SemEval 2021 Task 7, HaHackathon: Detecting and Rating Humor and Offense.
no code implementations • SEMEVAL 2021 • Xiaoyi Ruan, Meizhi Jin, Jian Ma, Haiqin Yang, Lianxin Jiang, Yang Mo, Mengyuan Zhou
Question answering from semi-structured tables can be seen as a semantic parsing task and is significant and practical for pushing the boundary of natural language understanding.
no code implementations • SEMEVAL 2020 • Chenyang Guo, Xiaolong Hou, Junsong Ren, Lianxin Jiang, Yang Mo, Haiqin Yang, Jianping Shen
This paper describes the model we apply in the SemEval-2020 Task 10.