@inproceedings{semeval2024task8,
title = {{SemEval}-2024 Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection},
author = {Wang, Yuxia and Mansurov, Jonibek and Ivanov, Petar and Su, Jinyan and Shelmanov, Artem and Tsvigun, Akim and Whitehouse, Chenxi and Afzal, Osama Mohammed and Mahmoud, Tarek and Puccetti, Giovanni and Arnold, Thomas and Aji, Alham Fikri and Habash, Nizar and Gurevych, Iryna and Nakov, Preslav},
booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation},
series = {SemEval 2024},
year = {2024},
address = {Mexico City, Mexico},
month = {June},
pages = {}, 
doi= {}
}

@article{wang2023m4,
      title={{M4}: Multi-generator, Multi-domain, and Multi-lingual
                   Black-Box Machine-Generated Text Detection}, 
      author={Yuxia Wang and
              Jonibek Mansurov and
              Petar Ivanov and
              Jinyan Su and
              Artem Shelmanov and
              Akim Tsvigun and
              Chenxi Whitehouse and
              Osama Mohammed Afzal and
              Tarek Mahmoud and
              Alham Fikri Aji and
              Preslav Nakov},
      year={2023},
      journal={arXiv:2305.14902},
      primaryClass={cs.CL}
}


@article{schuster1997bidirectional,
  title={Bidirectional recurrent neural networks},
  author={Schuster, Mike and Paliwal, Kuldip K},
  journal={IEEE transactions on Signal Processing},
  volume={45},
  number={11},
  pages={2673--2681},
  year={1997},
  publisher={Ieee}
}

@article{hochreiterschmidhuber1997,
    author = {Hochreiter, Sepp and Schmidhuber, Jürgen},
    title = "{Long Short-Term Memory}",
    journal = {Neural Computation},
    volume = {9},
    number = {8},
    pages = {1735-1780},
    year = {1997},
    month = {11},
    abstract = "{Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.}",
    issn = {0899-7667},
    doi = {10.1162/neco.1997.9.8.1735},
    url = {https://doi.org/10.1162/neco.1997.9.8.1735},
    eprint = {https://direct.mit.edu/neco/article-pdf/9/8/1735/813796/neco.1997.9.8.1735.pdf},
}

@misc{koike2023outfox,
      title={OUTFOX: {LLM}-generated Essay Detection through In-context Learning with Adversarially Generated Examples}, 
      author={Ryuto Koike and Masahiro Kaneko and Naoaki Okazaki},
      year={2023},
      eprint={2307.11729},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{kang2018dataset,
    title = "A Dataset of Peer Reviews ({P}eer{R}ead): Collection, Insights and {NLP} Applications",
    author = "Kang, Dongyeop  and
      Ammar, Waleed  and
      Dalvi, Bhavana  and
      van Zuylen, Madeleine  and
      Kohlmeier, Sebastian  and
      Hovy, Eduard  and
      Schwartz, Roy",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N18-1149",
    doi = "10.18653/v1/N18-1149",
    pages = "1647--1661",
    abstract = "Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research purposes (PeerRead v1),1 providing an opportunity to study this important artifact. The dataset consists of 14.7K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR. The dataset also includes 10.7K textual peer reviews written by experts for a subset of the papers. We describe the data collection process and report interesting observed phenomena in the peer reviews. We also propose two novel NLP tasks based on this dataset and provide simple baseline models. In the first task, we show that simple models can predict whether a paper is accepted with up to 21{\%} error reduction compared to the majority baseline. In the second task, we predict the numerical scores of review aspects and show that simple models can outperform the mean baseline for aspects with high variance such as {`}originality{'} and {`}impact{'}.",
}

@article{Hansen_2023,
   title={TextDescriptives: A {P}ython package for calculating a
large variety of metrics from text},
   volume={8},
   ISSN={2475-9066},
   url={http://dx.doi.org/10.21105/joss.05153},
   DOI={10.21105/joss.05153},
   number={84},
   journal={Journal of Open Source Software},
   publisher={The Open Journal},
   author={Hansen, Lasse and Olsen, Ludvig Renbo and Enevoldsen, Kenneth},
   year={2023},
   month=apr, pages={5153} }

@unpublished{spacy2,
    AUTHOR = {Honnibal, Matthew and Montani, Ines},
    TITLE  = {{spaCy 2}: Natural language understanding with {B}loom embeddings, convolutional neural networks and incremental parsing},
    YEAR   = {2017},
    Note   = {To appear}
}

@inproceedings{lee-etal-2021-pushing,
    title = "Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features",
    author = "Lee, Bruce W.  and
      Jang, Yoo Sung  and
      Lee, Jason",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.834",
    doi = "10.18653/v1/2021.emnlp-main.834",
    pages = "10669--10686",
    abstract = "We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random Forest, using handcrafted features) can combine with transformers (e.g. RoBERTa) to augment model performance. First, we explore suitable transformers and traditional ML models. Then, we extract 255 handcrafted linguistic features using self-developed extraction software. Finally, we assemble those to create several hybrid models, achieving state-of-the-art (SOTA) accuracy on popular datasets in readability assessment. The use of handcrafted features help model performance on smaller datasets. Notably, our RoBERTA-RF-T1 hybrid achieves the near-perfect classification accuracy of 99{\%}, a 20.3{\%} increase from the previous SOTA.",
}

@misc{adam,
author="P. K. Diederik",
title="Adam: a method for stochastic optimization",
year="2014",
URL="https://cir.nii.ac.jp/crid/1370580230635113728"
}