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MuseumQA: A Fine-Grained Question Answering Dataset for Museums and Artifacts

Published: 28 February 2024 Publication History

Abstract

In this paper, we present a fine-grained museum artifact question-answering (QA) dataset, which serves as the cornerstone for developing museum question-answering systems. Creating these systems is essential for the advancement of museums and can enhance the visitor experience. Nevertheless, research reveals the current absence of domestically available datasets for museum artifacts in China. To ensure data authenticity and validity, we meticulously collected and screened 3,416 raw data entries from the official websites of provincial museums across China. Using these raw data, we annotated annotatable QA pair information to create the final QA dataset. Initially, a small batch of QA pairs was generated with the assistance of ChatGPT. Subsequently, the remaining QA pairs were annotated using an enhanced QA generation model, yielding 23,149 QA pairs. To mitigate overfitting due to dataset-model size disparities, a noise factor was incorporated into the enhanced generation model. Additionally, a Chinese grammar correction module was integrated to enhance the accuracy of the generated statements. Ultimately, the model achieved optimal performance, and the dataset demonstrated the highest semantic relevance.

References

[1]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.
[2]
Aditya Deshpande, Jyoti Aneja, Liwei Wang, Alexander G. Schwing, and David Forsyth. 2019. Fast, Diverse and Accurate Image Captioning Guided by Part-Of-Speech. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[4]
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019. Unified language model pre-training for natural language understanding and generation. Advances in neural information processing systems 32 (2019).
[5]
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and LingPeng Kong. 2022. Diffuseq: Sequence to sequence text generation with diffusion models. arXiv preprint arXiv:2210.08933 (2022).
[6]
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020. Unifiedqa: Crossing format boundaries with a single qa system. arXiv preprint arXiv:2005.00700 (2020).
[7]
Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2017. The NarrativeQA Reading Comprehension Challenge. Transactions of the Association for Computational Linguistics 6, 3 (2017).
[8]
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019).
[9]
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2015. A diversity-promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055 (2015).
[10]
Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out. 74–81.
[11]
Zhuang Liu, Zhiqiu Xu, Joseph Jin, Zhiqiang Shen, and Trevor Darrell. 2023. Dropout Reduces Underfitting. arXiv preprint arXiv:2303.01500 (2023).
[12]
Lidiya Murakhovs’ ka, Chien-Sheng Wu, Philippe Laban, Tong Niu, Wenhao Liu, and Caiming Xiong. 2021. Mixqg: Neural question generation with mixed answer types. arXiv preprint arXiv:2110.08175 (2021).
[13]
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 311–318.
[14]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21, 1 (2020), 5485–5551.
[15]
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don’t know: Unanswerable questions for SQuAD. arXiv preprint arXiv:1806.03822 (2018).
[16]
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100,000+ questions for machine comprehension of text. Association for Computational Linguistics (2016), 2383–2392.
[17]
Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu. 2021. Roformer: Enhanced transformer with rotary position embedding. arXiv preprint arXiv:2104.09864 (2021).
[18]
Tianxiang Sun, Zhengfu He, Qin Zhu, Xipeng Qiu, and Xuanjing Huang. 2023. Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Toronto, Canada, 11156–11172.
[19]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
[20]
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations. 38–45.
[21]
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, and Xing Xie. 2022. Noisytune: A little noise can help you finetune pretrained language models better. arXiv preprint arXiv:2202.12024 (2022).
[22]
Zichen Wu, Xin Jia, Fanyi Qu, and Yunfang Wu. 2022. Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation. arXiv preprint arXiv:2209.04179 (2022).
[23]
Lvxiaowei Xu, Jianwang Wu, Jiawei Peng, Jiayu Fu, and Ming Cai. 2022. FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction. arXiv preprint arXiv:2210.12364 (2022).
[24]
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2020. mT5: A massively multilingual pre-trained text-to-text transformer. arXiv preprint arXiv:2010.11934 (2020).
[25]
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675 (2019).

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    MLNLP '23: Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing
    December 2023
    252 pages
    ISBN:9798400709241
    DOI:10.1145/3639479
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 February 2024

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    Author Tags

    1. museum artifact dataset
    2. question-answering pair generation

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