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.
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.
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 • 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.