% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}
}

@inproceedings{jullien-etal-2024-semeval,
    title = "{S}em{E}val-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials",
    author = {Jullien, Ma{\"e}l  and
      Valentino, Marco  and
      Freitas, Andr{\'e}},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    year = "2024",
    publisher = "Association for Computational Linguistics",
} 



% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").


@InProceedings{N18-1101,
  author = "Williams, Adina
            and Nangia, Nikita
            and Bowman, Samuel",
  title = "A Broad-Coverage Challenge Corpus for 
           Sentence Understanding through Inference",
  booktitle = "Proceedings of the 2018 Conference of 
               the North American Chapter of the 
               Association for Computational Linguistics:
               Human Language Technologies, Volume 1 (Long
               Papers)",
  year = "2018",
  publisher = "Association for Computational Linguistics",
  pages = "1112--1122",
  location = "New Orleans, Louisiana",
  url = "http://aclweb.org/anthology/N18-1101"
}

@article{bowman2015large,
  title={A large annotated corpus for learning natural language inference},
  author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},
  journal={arXiv preprint arXiv:1508.05326},
  year={2015}
}


@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}
}

@inproceedings{Li_2022,
   title={Pair-Level Supervised Contrastive Learning for Natural Language Inference},
   url={http://dx.doi.org/10.1109/ICASSP43922.2022.9746499},
   DOI={10.1109/icassp43922.2022.9746499},
   booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
   publisher={IEEE},
   author={Li, Shuang and Hu, Xuming and Lin, Li and Wen, Lijie},
   year={2022},
   month=may }

@misc{phan2021scifive,
      title={SciFive: a text-to-text transformer model for biomedical literature}, 
      author={Long N. Phan and James T. Anibal and Hieu Tran and Shaurya Chanana and Erol Bahadroglu and Alec Peltekian and Grégoire Altan-Bonnet},
      year={2021},
      eprint={2106.03598},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{romanov2018lessons,
      title={Lessons from Natural Language Inference in the Clinical Domain}, 
      author={Alexey Romanov and Chaitanya Shivade},
      year={2018},
      eprint={1808.06752},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{HERREROZAZO2013914,
title = {The DDI corpus: An annotated corpus with pharmacological substances and drug–drug interactions},
journal = {Journal of Biomedical Informatics},
volume = {46},
number = {5},
pages = {914-920},
year = {2013},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2013.07.011},
url = {https://www.sciencedirect.com/science/article/pii/S1532046413001123},
author = {María Herrero-Zazo and Isabel Segura-Bedmar and Paloma Martínez and Thierry Declerck},
keywords = {Biomedical corpora, Drug interaction, Information extraction},
abstract = {The management of drug–drug interactions (DDIs) is a critical issue resulting from the overwhelming amount of information available on them. Natural Language Processing (NLP) techniques can provide an interesting way to reduce the time spent by healthcare professionals on reviewing biomedical literature. However, NLP techniques rely mostly on the availability of the annotated corpora. While there are several annotated corpora with biological entities and their relationships, there is a lack of corpora annotated with pharmacological substances and DDIs. Moreover, other works in this field have focused in pharmacokinetic (PK) DDIs only, but not in pharmacodynamic (PD) DDIs. To address this problem, we have created a manually annotated corpus consisting of 792 texts selected from the DrugBank database and other 233 Medline abstracts. This fined-grained corpus has been annotated with a total of 18,502 pharmacological substances and 5028 DDIs, including both PK as well as PD interactions. The quality and consistency of the annotation process has been ensured through the creation of annotation guidelines and has been evaluated by the measurement of the inter-annotator agreement between two annotators. The agreement was almost perfect (Kappa up to 0.96 and generally over 0.80), except for the DDIs in the MedLine database (0.55–0.72). The DDI corpus has been used in the SemEval 2013 DDIExtraction challenge as a gold standard for the evaluation of information extraction techniques applied to the recognition of pharmacological substances and the detection of DDIs from biomedical texts. DDIExtraction 2013 has attracted wide attention with a total of 14 teams from 7 different countries. For the task of recognition and classification of pharmacological names, the best system achieved an F1 of 71.5%, while, for the detection and classification of DDIs, the best result was F1 of 65.1%. These results show that the corpus has enough quality to be used for training and testing NLP techniques applied to the field of Pharmacovigilance. The DDI corpus and the annotation guidelines are free for use for academic research and are available at http://labda.inf.uc3m.es/ddicorpus.}
}

@misc{vaswani2023attention,
      title={Attention Is All You Need}, 
      author={Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
      year={2023},
      eprint={1706.03762},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{Ganaie_2022,
   title={Ensemble deep learning: A review},
   volume={115},
   ISSN={0952-1976},
   url={http://dx.doi.org/10.1016/j.engappai.2022.105151},
   DOI={10.1016/j.engappai.2022.105151},
   journal={Engineering Applications of Artificial Intelligence},
   publisher={Elsevier BV},
   author={Ganaie, M.A. and Hu, Minghui and Malik, A.K. and Tanveer, M. and Suganthan, P.N.},
   year={2022},
   month=oct, pages={105151} }

@article{MOHAMMED2023757,
title = {A comprehensive review on ensemble deep learning: Opportunities and challenges},
journal = {Journal of King Saud University - Computer and Information Sciences},
volume = {35},
number = {2},
pages = {757-774},
year = {2023},
issn = {1319-1578},
doi = {https://doi.org/10.1016/j.jksuci.2023.01.014},
url = {https://www.sciencedirect.com/science/article/pii/S1319157823000228},
author = {Ammar Mohammed and Rania Kora},
keywords = {Ensemble learning, Ensemble methods, Machine learning, Deep learning, Ensemble deep learning},
abstract = {In machine learning, two approaches outperform traditional algorithms: ensemble learning and deep learning. The former refers to methods that integrate multiple base models in the same framework to obtain a stronger model that outperforms them. The success of an ensemble method depends on several factors, including how the baseline models are trained and how they are combined. In the literature, there are common approaches to building an ensemble model successfully applied in several domains. On the other hand, deep learning-based models have improved the predictive accuracy of machine learning across a wide range of domains. Despite the diversity of deep learning architectures and their ability to deal with complex problems and the ability to extract features automatically, the main challenge in deep learning is that it requires a lot of expertise and experience to tune the optimal hyper-parameters, which makes it a tedious and time-consuming task. Numerous recent research efforts have been made to approach ensemble learning to deep learning to overcome this challenge. Most of these efforts focus on simple ensemble methods that have some limitations. Hence, this review paper provides comprehensive reviews of the various strategies for ensemble learning, especially in the case of deep learning. Also, it explains in detail the various features or factors that influence the success of ensemble methods. In addition, it presents and accurately categorized several research efforts that used ensemble learning in a wide range of domains.}
}

@misc{yasunaga2022linkbert,
      title={LinkBERT: Pretraining Language Models with Document Links}, 
      author={Michihiro Yasunaga and Jure Leskovec and Percy Liang},
      year={2022},
      eprint={2203.15827},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{pahwa2023bphigh,
  title={BpHigh at SemEval-2023 Task 7: Can Fine-tuned Cross-encoders Outperform GPT-3.5 in NLI Tasks on Clinical Trial Data?},
  author={Pahwa, Bhavish and Pahwa, Bhavika},
  booktitle={Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)},
  pages={1936--1944},
  year={2023}
}

@inproceedings{alameldin2023clemson,
  title={Clemson NLP at SemEval-2023 Task 7: Applying GatorTron to Multi-Evidence Clinical NLI},
  author={Alameldin, Ahamed and Williamson, Ashton},
  booktitle={Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)},
  pages={1598--1602},
  year={2023}
}

@article{devlin2018bert,
  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}

@article{raffel2020exploring,
  title={Exploring the limits of transfer learning with a unified text-to-text transformer},
  author={Raffel, Colin and Shazeer, Noam and Roberts, Adam and Lee, Katherine and Narang, Sharan and Matena, Michael and Zhou, Yanqi and Li, Wei and Liu, Peter J},
  journal={The Journal of Machine Learning Research},
  volume={21},
  number={1},
  pages={5485--5551},
  year={2020},
  publisher={JMLRORG}
}

@inproceedings{rajamanickam2023i2r,
  title={I2R at SemEval-2023 Task 7: Explanations-driven Ensemble Approach for Natural Language Inference over Clinical Trial Data},
  author={Rajamanickam, Saravanan and Rajaraman, Kanagasabai},
  booktitle={Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)},
  pages={1630--1635},
  year={2023}
}

@article{he2020deberta,
  title={Deberta: Decoding-enhanced bert with disentangled attention},
  author={He, Pengcheng and Liu, Xiaodong and Gao, Jianfeng and Chen, Weizhu},
  journal={arXiv preprint arXiv:2006.03654},
  year={2020}
}

@article{lee2020biobert,
  title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining},
  author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
  journal={Bioinformatics},
  volume={36},
  number={4},
  pages={1234--1240},
  year={2020},
  publisher={Oxford University Press}
}

@inproceedings{zhao2023hw,
  title={HW-TSC at SemEval-2023 Task 7: Exploring the Natural Language Inference Capabilities of ChatGPT and Pre-trained Language Model for Clinical Trial},
  author={Zhao, Xiaofeng and Zhang, Min and Ma, Miaomiao and Su, Chang and Liu, Yilun and Wang, Minghan and Qiao, Xiaosong and Guo, Jiaxin and Li, Yinglu and Ma, Wenbing},
  booktitle={Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)},
  pages={1603--1608},
  year={2023}
}

@article{jullien2023semeval,
  title={SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data},
  author={Jullien, Ma{\"e}l and Valentino, Marco and Frost, Hannah and O'Regan, Paul and Landers, Donal and Freitas, Andr{\'e}},
  journal={arXiv preprint arXiv:2305.02993},
  year={2023}
}