no code implementations • WASSA (ACL) 2022 • Bin Li, Yixuan Weng, Qiya Song, Bin Sun, Shutao Li
This paper describes the contribution of the LingJing team’s method to the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Emotion Classification.
no code implementations • dialdoc (ACL) 2022 • Minjun Zhu, Bin Li, Yixuan Weng, Fei Xia
Question Answering (QA) is a Natural Language Processing (NLP) task that can measure language and semantics understanding ability, it requires a system not only to retrieve relevant documents from a large number of articles but also to answer corresponding questions according to documents.
1 code implementation • BioNLP (ACL) 2022 • Bin Li, Yixuan Weng, Fei Xia, Bin Sun, Shutao Li
Given an input video, the MedVidCL task aims to correctly classify it into one of three following categories: Medical Instructional, Medical Non-instructional, and Non-medical.
2 code implementations • SemEval (NAACL) 2022 • Bin Li, Yixuan Weng, Fei Xia, Shizhu He, Bin Sun, Shutao Li
This paper introduces the approach of Team LingJing’s experiments on SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings (CODWOE).
1 code implementation • SemEval (NAACL) 2022 • Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Bin Sun, Shutao Li, Kang Liu, Jun Zhao
For the classification sub-task, we adopt the DeBERTa-v3 pre-trained model for fine-tuning datasets of different languages.
no code implementations • TU (COLING) 2022 • Minjun Zhu, Yixuan Weng, Bin Li, Shizhu He, Kang Liu, Jun Zhao
In this work, we propose a knowledge transfer method with visual prompt (VPTG) fusing multi-modal data, which is a flexible module that can utilize the text-only seq2seq model to handle visual dialogue tasks.
1 code implementation • 15 Feb 2024 • Yixuan Weng, Shizhu He, Kang Liu, Shengping Liu, Jun Zhao
This heightens the need to control model behaviors.
2 code implementations • 15 Nov 2023 • Yifan Wei, Xiaoyan Yu, Huanhuan Ma, Fangyu Lei, Yixuan Weng, Ran Song, Kang Liu
Knowledge Editing (KE) for modifying factual knowledge in Large Language Models (LLMs) has been receiving increasing attention.
1 code implementation • 20 Aug 2023 • Yixuan Weng, Zhiqi Wang, Huanxuan Liao, Shizhu He, Shengping Liu, Kang Liu, Jun Zhao
With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to increase.
no code implementations • 23 May 2023 • Minjun Zhu, Yixuan Weng, Shizhu He, Kang Liu, Jun Zhao
In Textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps.
1 code implementation • 9 May 2023 • Yixuan Weng, Bin Li, Fei Xia, Minjun Zhu, Bin Sun, Shizhu He, Kang Liu, Jun Zhao
The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care.
3 code implementations • 4 Apr 2023 • Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Kang Liu, Jun Zhao
Our work highlights the potential of seamlessly unifying explicit rule learning via CoNNs and implicit pattern learning in LMs, paving the way for true symbolic comprehension capabilities.
1 code implementation • 19 Dec 2022 • Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, Jun Zhao
By performing a backward verification of the answers that LLM deduced for itself, we can obtain interpretable answer validation scores to select the candidate answer with the highest score.
no code implementations • 10 Dec 2022 • Bin Li, Yixuan Weng, Qiya Song, Hanjun Deng
As the deep learning rapidly promote, the artificial texts created by generative models are commonly used in news and social media.
1 code implementation • 26 Oct 2022 • Yixuan Weng, Bin Li
In this paper, we propose a cross-modal mutual knowledge transfer span localization (MutualSL) method to reduce the knowledge deviation.
no code implementations • 17 Oct 2022 • Minjun Zhu, Yixuan Weng, Shizhu He, Kang Liu, Jun Zhao
Recently, natural language database (NLDB) conducts complex QA in knowledge base with textual evidences rather than structured representations, this task attracts a lot of attention because of the flexibility and richness of textual evidence.
1 code implementation • 11 Oct 2022 • Bin Li, Yixuan Weng, Bin Sun, Shutao Li
We introduce a new task, named video corpus visual answer localization (VCVAL), which aims to locate the visual answer in a large collection of untrimmed instructional videos using a natural language question.
no code implementations • 5 Jul 2022 • Bin Li, Yixuan Weng, Ziyu Ma, Bin Sun, Shutao Li
To fully leverage the visual information for both scene understanding and dialogue generation, we propose the scene-aware prompt for the MDUG task.
1 code implementation • 20 Apr 2022 • Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Kang Liu, Bin Sun, Shutao Li, Jun Zhao
The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic.
1 code implementation • 9 Apr 2022 • Bin Li, Yixuan Weng, Fei Xia, Hanjun Deng
The last decade has witnessed enormous improvements in science and technology, stimulating the growing demand for economic and cultural exchanges in various countries.
no code implementations • WASSA (ACL) 2022 • Bin Li, Yixuan Weng
This paper describes our proposed method for the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Personality Prediction (PER) and Reactivity Index Prediction (IRI).
no code implementations • 13 Mar 2022 • Bin Li, Yixuan Weng, Bin Sun, Shutao Li
However, due to the weak correlations and huge gaps of the semantic features between the textual question and visual answer, existing methods adopting visual span predictor perform poorly in the TAGV task.
1 code implementation • 8 Dec 2021 • Yixuan Weng, Fei Xia, Bin Li, Xiusheng Huang, Shizhu He
To address the above issue, this paper proposes an new method for acronym disambiguation, named as ADBCMM, which can significantly improve the performance of low-resource languages by building counterfactuals and multilingual mixing.
no code implementations • 29 Nov 2021 • Bin Li, Fei Xia, Yixuan Weng, Xiusheng Huang, Bin Sun, Shutao Li
In this paper, we propose a Prompt-based Sequence Generation (PSG) method for the acronym extraction task.
no code implementations • 29 Nov 2021 • Bin Li, Fei Xia, Yixuan Weng, Xiusheng Huang, Bin Sun
In this paper, we propose a Simple framework for Contrastive Learning of Acronym Disambiguation (SimCLAD) method to better understand the acronym meanings.
no code implementations • 3 Aug 2021 • Bin Li, Encheng Chen, Hongru Liu, Yixuan Weng, Bin Sun, Shutao Li, Yongping Bai, Meiling Hu
Medical Dialogue Generation (MDG) is intended to build a medical dialogue system for intelligent consultation, which can communicate with patients in real-time, thereby improving the efficiency of clinical diagnosis with broad application prospects.