@inproceedings{NER,
    author = "Takahiro Omi",
    title = "Construction of a Japanese dataset for named entity recognition using Wikipedia (in Japanese)",
    booktitle = "Proceedings of the Twenty-seven
    Annual Meeting of the Association for Natural Language Processing",
    year = 2021,
}

@inproceedings{UD,
    author = "Takaaki Tanaka and Yusuke Miyao and Masayuki Asahara and Sumire Uematsu and Hiroshi Kanayama and Shinsuke Mori and Yuji Matsumoto",
    title = "Universal Dependencies for Japanese",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)",
    month = may,
    year = 2016,
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L16-1261.pdf",
    pages = "1651--1658"
}

@inproceedings{unidic,
    author = "Yasuharu Den and Toshinobu Ogiso and Hideki Ogura and Atsushi Yamada and Nobuaki Minematsu and Kiyotaka Uchimoto and Hanae Koiso",
    title = "Language Resources for Corpus Japanese Studies : Development and Application of an Electronic Dictionary for Morphological Analysis (in Japanese)",
    booktitle = "Japanese Linguistics",
    volume = "22",
    pages = "101-123",
    year = "2017"
}

@inproceedings{NEURIPS2018_e555ebe0,
 author = {Gong, Chengyue and He, Di and Tan, Xu and Qin, Tao and Wang, Liwei and Liu, Tie-Yan},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {FRAGE: Frequency-Agnostic Word Representation},
 url = {https://proceedings.neurips.cc/paper/2018/file/e555ebe0ce426f7f9b2bef0706315e0c-Paper.pdf},
 volume = {31},
 year = {2018}
}

@inproceedings{sato2017mecabipadicneologdnlp2017,
    author    = {Toshinori Sato and Taiichi Hashimoto and Manabu Okumura},
    title     = {Implementation of a word segmentation dictionary called mecab-ipadic-NEologd and study on how to use it effectively for information retrieval (in Japanese)},
    booktitle = "Proceedings of the Twenty-three
    Annual Meeting of the Association for Natural Language Processing",
    year      = "2017",
    pages     = "NLP2017-B6-1",
    publisher = "The Association for Natural Language Processing",
}

@article{kim2020comparison,
  title={Comparison of Korean Preprocessing Performance according to Tokenizer in NMT Transformer Model},
  author={Kim, Geumcheol and Lee, Sang-Hong},
  journal={Journal of Advances in Information Technology Vol},
  volume={11},
  number={4},
  year={2020}
}


% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% pytorch
@incollection{NEURIPS2019_9015,
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {8024--8035},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}

% AdamW
@inproceedings{
loshchilov2018decoupled,
title={Decoupled Weight Decay Regularization},
author={Ilya Loshchilov and Frank Hutter},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=Bkg6RiCqY7},
}

% MS COCO
@misc{https://doi.org/10.48550/arxiv.1504.00325,
  doi = {10.48550/ARXIV.1504.00325},
  
  url = {https://arxiv.org/abs/1504.00325},
  
  author = {Chen, Xinlei and Fang, Hao and Lin, Tsung-Yi and Vedantam, Ramakrishna and Gupta, Saurabh and Dollar, Piotr and Zitnick, C. Lawrence},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Microsoft COCO Captions: Data Collection and Evaluation Server},
  
  publisher = {arXiv},
  
  year = {2015},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

% ConceptNet
@article{Speer_Chin_Havasi_2017, title={ConceptNet 5.5: An Open Multilingual Graph of General Knowledge}, volume={31}, url={https://ojs.aaai.org/index.php/AAAI/article/view/11164}, DOI={10.1609/aaai.v31i1.11164}, abstractNote={ &lt;p&gt; Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. When ConceptNet is combined with word embeddings acquired from distributional semantics (such as word2vec), it provides applications with understanding that they would not acquire from distributional semantics alone, nor from narrower resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art results on intrinsic evaluations of word relatedness that translate into improvements on applications of word vectors, including solving SAT-style analogies. &lt;/p&gt; }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Speer, Robyn and Chin, Joshua and Havasi, Catherine}, year={2017}, month={Feb.} }

% bap
@inproceedings{
dozat2017deep,
title={Deep Biaffine Attention for Neural Dependency Parsing},
author={Timothy Dozat and Christopher D. Manning},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=Hk95PK9le}
}

%wordpiece
@INPROCEEDINGS{6289079,
  author={Schuster, Mike and Nakajima, Kaisuke},
  booktitle={2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Japanese and Korean voice search}, 
  year={2012},
  volume={},
  number={},
  pages={5149-5152},
  doi={10.1109/ICASSP.2012.6289079}}

% Viterbi
@ARTICLE{1054010,
  author={Viterbi, Andrew},
  journal={IEEE Transactions on Information Theory}, 
  title={Error bounds for convolutional codes and an asymptotically optimum decoding algorithm}, 
  year={1967},
  volume={13},
  number={2},
  pages={260-269},
  doi={10.1109/TIT.1967.1054010}}

% Vaporetto
@inproceedings{vaporetto,
    author = {Koichi Akabe and Shunsuke Kanda and Yusuke Oda and Shinsuke Mori},
    title = "Vaporetto: Fast Japanese tokenizer based on pointwise prediction (in {J}apanese)",
    booktitle = "Proceedings of the 28th
    Annual Meeting of the Association for Natural Language Processing",
    url={https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/D2-5.pdf},
    year = 2022
}

% Kruskal & Wallis Test
@article{doi:10.1080/01621459.1952.10483441,
author = { William H.   Kruskal  and  W.   Allen   Wallis },
title = {Use of Ranks in One-Criterion Variance Analysis},
journal = {Journal of the American Statistical Association},
volume = {47},
number = {260},
pages = {583-621},
year  = {1952},
publisher = {Taylor & Francis},
doi = {10.1080/01621459.1952.10483441},

URL = { 
    
    
        https://www.tandfonline.com/doi/abs/10.1080/01621459.1952.10483441
    

},
eprint = { 
    
    
        https://www.tandfonline.com/doi/pdf/10.1080/01621459.1952.10483441
    

}

}

% Steel-Dwass test
@article{doi:10.1080/03610929108830487,
author = { Critchlow E.   Douglas  and  Fligner A.   Michael },
title = {On distribution-free multiple comparisons in the one-way analysis of variance},
journal = {Communications in Statistics - Theory and Methods},
volume = {20},
number = {1},
pages = {127-139},
year  = {1991},
publisher = {Taylor & Francis},
doi = {10.1080/03610929108830487},

URL = { 
    
        https://doi.org/10.1080/03610929108830487
    
    

},
eprint = { 
    
        https://doi.org/10.1080/03610929108830487
    
    

}

}

% Turkish
@misc{https://doi.org/10.48550/arxiv.2204.08832,
  doi = {10.48550/ARXIV.2204.08832},
  
  url = {https://arxiv.org/abs/2204.08832},
  
  author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}

% GPT-3
@misc{https://doi.org/10.48550/arxiv.2005.14165,
  doi = {10.48550/ARXIV.2005.14165},
  
  url = {https://arxiv.org/abs/2005.14165},
  
  author = {Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel M. and Wu, Jeffrey and Winter, Clemens and Hesse, Christopher and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Language Models are Few-Shot Learners},
  
  publisher = {arXiv},
  
  year = {2020},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

% GPT Japanese Tokenizer
@inproceedings{Inoue2022GPT,
	author =	"Seiichi Inoue and Nguyen Tung and Akifumi Nakamachi and Shengzhe Li and  Toshinori Sato",
	title =		"Investigation of the impact of tokenizers using Japanese GPT (in {J}apanese)", 
	booktitle =	"Proceedings of the 28th
    Annual Meeting of the Association for Natural Language Processing",
    url={https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/A1-2.pdf},
	year =		2022,
}

% T5
@article{10.5555/3455716.3455856,
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.},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
year = {2020},
issue_date = {January 2020},
publisher = {JMLR.org},
volume = {21},
number = {1},
issn = {1532-4435},
abstract = {Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pretraining objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.},
journal = {J. Mach. Learn. Res.},
month = {jan},
articleno = {140},
numpages = {67},
keywords = {natural language processing, multi-task learning, attention based models, transfer learning, deep learning}
}

% Thai case
@article{DBLP:journals/corr/abs-2101-09635,
  author       = {Lalita Lowphansirikul and
                  Charin Polpanumas and
                  Nawat Jantrakulchai and
                  Sarana Nutanong},
  title        = {WangchanBERTa: Pretraining transformer-based Thai Language Models},
  journal      = {CoRR},
  volume       = {abs/2101.09635},
  year         = {2021},
  url          = {https://arxiv.org/abs/2101.09635},
  eprinttype    = {arXiv},
  eprint       = {2101.09635},
  timestamp    = {Sat, 30 Jan 2021 18:02:51 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2101-09635.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}