@inproceedings{10.1145/3459637.3482155,
author = {Chen, Chung-Chi and Huang, Hen-Hsen and Chen, Hsin-Hsi},
title = {NQuAD: 70,000+ Questions for Machine Comprehension of the Numerals in Text},
year = {2021},
isbn = {9781450384469},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3459637.3482155},
doi = {10.1145/3459637.3482155},
booktitle = {Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
pages = {2925–2929},
numpages = {5},
keywords = {numeracy, machine reading comprehension, cloze test},
location = {Virtual Event, Queensland, Australia},
series = {CIKM '21}
}

@inproceedings{chen-etal-2023-improving,
    title = "Improving Numeracy by Input Reframing and Quantitative Pre-Finetuning Task",
    author = "Chen, Chung-Chi  and
      Takamura, Hiroya  and
      Kobayashi, Ichiro  and
      Miyao, Yusuke",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-eacl.4",
    doi = "10.18653/v1/2023.findings-eacl.4",
    pages = "69--77"
}

@inproceedings{devlin-etal-2019-bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1423",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).",
}

@article{Liu2019Neural,
    title={Neural Machine Reading Comprehension: Methods And Trends},
    author={Shanshan Liu and Xin Zhang and Sheng Zhang and Hui Wang and Weiming Zhang},
    journal={APPLIED SCIENCES-BASEL},
    volume={9},
    number={18},
    year={2019}
}

@article{Zhang2020DCMN+:,
    title={DCMN+: Dual Co-Matching Network for Multi-Choice Reading Comprehension.},
    author={Shuiliang Zhang and Hai Zhao and Yuwei Wu and Zhuosheng Zhang and Xi Zhou and Xiang Zhou},
    journal={THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE},
    volume={34},
    number={05},
    pages={9563-9570},
    year={2020}
}

@inproceedings{ma-du-2023-poe,
    title = "{POE}: Process of Elimination for Multiple Choice Reasoning",
    author = "Ma, Chenkai  and
      Du, Xinya",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.273",
    doi = "10.18653/v1/2023.emnlp-main.273",
    pages = "4487--4496",
    abstract = "Language models (LMs) are capable of conducting in-context learning for multiple choice reasoning tasks, but the options in these tasks are treated equally. As humans often first eliminate wrong options before picking the final correct answer, we argue a similar two-step strategy can make LMs better at these tasks. To this end, we present the Process of Elimination (POE), a two-step scoring method. In the first step, POE scores each option, and eliminates seemingly wrong options. In the second step, POE masks these wrong options, and makes the final prediction from the remaining options. Zero-shot experiments on 8 reasoning tasks illustrate the effectiveness of POE, and a following analysis finds our method to be especially performant on logical reasoning tasks. We further analyze the effect of masks, and show that POE applies to few-shot settings and large language models (LLMs) like ChatGPT.",
}

@inproceedings{sharma-etal-2023-financial,
    title = "Financial Numeric Extreme Labelling: A dataset and benchmarking",
    author = "Sharma, Soumya  and
      Khatuya, Subhendu  and
      Hegde, Manjunath  and
      Shaikh, Afreen  and
      Dasgupta, Koustuv  and
      Goyal, Pawan  and
      Ganguly, Niloy",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.219",
    doi = "10.18653/v1/2023.findings-acl.219",
    pages = "3550--3561",
    abstract = "The U.S. Securities and Exchange Commission (SEC) mandates all public companies to file periodic financial statements that should contain numerals annotated with a particular label from a taxonomy. In this paper, we formulate the task of automating the assignment of a label to a particular numeral span in a sentence from an extremely large label set. Towards this task, we release a dataset, Financial Numeric Extreme Labelling (FNXL), annotated with 2,794 labels. We benchmark the performance of the FNXL dataset by formulating the task as (a) a sequence labelling problem and (b) a pipeline with span extraction followed by Extreme Classification. Although the two approaches perform comparably, the pipeline solution provides a slight edge for the least frequent labels.",
}

@article{Jin2020Mmm:,
title={Mmm: Multi-Stage Multi-Task Learning For Multi-Choice Reading Comprehension},
author={Di Jin and Shuyang Gao and Jiun-Yu Kao and Tagyoung Chung and Dilek Hakkani-tur},
journal={National Conference on Artificial Intelligence},
volume={34},
number={05},
pages={8010-8017},
year={2020}
}