文档智能: 数据集、模型和应用

崔磊,徐毅恒,吕腾超,韦福如

PDF(5178 KB)
PDF(5178 KB)
中文信息学报 ›› 2022, Vol. 36 ›› Issue (6) : 1-19.
综述

文档智能: 数据集、模型和应用

  • 崔磊,徐毅恒,吕腾超,韦福如
作者信息 +

Document AI: Benchmarks, Models and Applications

  • CUI Lei, XU Yiheng, LYU Tengchao, WEI Furu
Author information +
History +

摘要

文档智能是指通过计算机进行自动阅读、理解以及分析商业文档的过程,是自然语言处理和计算机视觉交叉领域的一个重要研究方向。近年来,深度学习技术的普及极大地推动了文档智能领域的发展,以文档版面分析、文档信息抽取、文档视觉问答以及文档图像分类等为代表的文档智能任务均有显著的性能提升。该文对于早期基于启发式规则的文档分析技术、基于统计机器学习的算法以及近年来基于深度学习和预训练的方法进行简要介绍,并展望了文档智能技术的未来发展方向。

Abstract

Document AI, or Document Intelligence, is a relatively new research topic that refers to the techniques to automatically read, understand and analyze business documents. It is an important interdisciplinary study involving natural language processing and computer vision. In recent years, the popularity of deep learning technology has greatly advanced the development of Document AI tasks, such as document layout analysis, document information extraction, document visual question answering, and document image classification etc. This paper briefly introduces the early-stage heuristic rule-based document analysis, statistical machine learning based algorithms, as well as the deep learning-based approaches especially the pre-training approaches. Finally, we also look into the future direction of Document AI.

关键词

文档智能 / 深度学习 / 多模态自然语言处理

Key words

Document AI / deep learning / multimodal NLP

引用本文

导出引用
崔磊,徐毅恒,吕腾超,韦福如. 文档智能: 数据集、模型和应用. 中文信息学报. 2022, 36(6): 1-19
CUI Lei, XU Yiheng, LYU Tengchao, WEI Furu. Document AI: Benchmarks, Models and Applications. Journal of Chinese Information Processing. 2022, 36(6): 1-19

参考文献

[1] Vaswani A. Attention is all you need[C]//Proceedings of the 31st International Conferance on Neural Information Processing Systems, 2017: 5998-6008.
[2] He K. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[3] Ren S. Faster R-CNN: Towards real-time object detection with region proposal networks[C]//Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 2016: 1137-1149.
[4] He K. Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017.
[5] Liu Wei. SSD: Single shot multibox detector[G].Lecture Notes in Computer Science, 2016,9905: 21-37.
[6] Redmon J, Ali F. YOLOv3: An incremental improvement[EB/OL]. http://pjreddie.com/media/files/papers/YQL0v3.pdf[2021-08-29]
[7] Yang X. Learning to extract semantic structure from documents using multimodal fully convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4342-4351.
[8] Schreiber S. Deep DeSRT: Deep learning for detection and structure recognition of tables in document images[C]//Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition, 2017: 1162-1167.
[9] Gbel M C. ICDAR 2013 table competition[C]//Proceedings of the 12th International Conference on Document Analysis and Recognition, 2013: 1449-1453.
[10] Xu Z, Tang J, Antonio J Y. PubLayNet: Largest dataset ever for document layout analysis[C]//Proceedings of the International Conference on Document Analysis and Recognition, 2019. 1015-1022.
[11] Xu Z. Image-based table recognition: data, model, and evaluation[G].Lecture Notes in Computer Science, 2020,12366: 564-80.
[12] Li M. TableBank: Table benchmark for image-based table detection and recognition[C]//Proceedings of the 12th Language Resources and Evaluation Conference. Marseille: European Language Resources Association, 2020: 1918-1925.
[13] Li M. DocBank: A benchmark dataset for document layout analysis[C]//Proceedings of the 28th International Conference on Computational Linguistics. Barcelona, Spain: International Committee on Computational Linguistics, 2020: 949-960.
[14] Liu X J. Graph convolution for multimodal information extraction from visually rich documents.[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis: Association for Computational Linguistics, 2019. 32-39.
[15] Xu Y. Layout L M: pre-training of text and layout for document image understanding[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2020: 1192-1200.
[16] Gao L. ICDAR 2019 competition on table detection and recognition[C]//Proceedings of the International Conference on Document Analysis and Recognition, 2019: 1510-1515.
[17] Yepes A J, Xu Z, Douglas B. ICDAR 2021 competition on scientific literature parsing[C]//Proceedings of the Competition on Scientific Literature Parsing, 2021:605-617.
[18] Shahab A. An open approach towards the benchmarking of table structure recognition systems[C]//Proceedings of the 9th IAPR International Workshop on Document Analysis Systems. New York, NY, USA: Association for Computing Machinery, 2010: 113-120.
[19] Fang J. Dataset, ground-truth and performance metrics for table detection evaluation[C]//Proceedings of the 10th IAPR International Workshop on Document Analysis Systems, 2012: 445-449.
[20] Abdallah A. TNCR: Table net detection and classification dataset[J]. arXiv preprint arXiv:2106.15322, 2021.
[21] Desai H. TabLeX: A benchmark dataset for structure and content information extraction from scientific tables[G]. Lecture Notes in Computer Science, 2021: 554-569.
[22] Smock B, Rohith P, Robin A. PubTables-1M: Towards a universal dataset and metrics for training and evaluating table extraction models[J]. arXiv preprint arXiv: 2110.00061v2, 2021.
[23] Mondal A, Peter L, Jawahar C V. IIIT-AR-13K: A new dataset for graphical object detection in documents[G]. Lecture Notes in Computer Science, 2020,12116: 216-230.
[24] Wang Z. Layout Reader: Pre-training of text and layout for reading order detection[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2021: 4735-4744.
[25] Hao Q. From one tree to a forest: a unified solution for structured web data extraction[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and development in Information Retrieval. Association for Computing Machinery, New York, NY, USA,2011: 775-784.
[26] Jaume G, Hazim K E, Jean Philippe T. FUNSD: A dataset for form understanding in noisy scanned documents[C]//Proceedings of the ICDARW, 2019: 1-6.
[27] Huang Z. ICDAR 2019 competition on scanned receipt OCR and information extraction[C]//Proceedings of the International Conference on Document Analysis and Recognition, 2019:1516-1520.
[28] Park S. CORD: A consolidated receipt dataset for post-OCR parsing[DB/OL].https://github.com/clovaai/cord[2021-08-29]
[29] Guo H. EATEN: Entity-aware attention for single shot visual text extraction[C]//Proceedings of the International Conference on Document Analysis and Recognition, 2019: 254-259.
[30] Wang J. Towards robust visual information extraction in real world: new dataset and novel solution[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 2738-2745.
[31] Stray J, Stacey S. Project DeepForm: extract information from documents[BD/OL].https:wandb.ai/deepform/politicd-ad-extraction[2021-08-29]
[32] Stanisawek T. Kleister: Key information extraction datasets involving long documents with complex layouts[G].Lecture Notes in Computer Science, 2021: 564-79.
[33] Xu Y. XFUND: A benchmark dataset for multilingual visually rich form understanding[G].ACL 2022 Findings,2022: 3214-3224.
[34] Mathew M. DocVQA: A dataset for VQA on document images[C]//Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021: 2200-2209.
[35] Mathew M. Infographics VQA[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022: 2582-2591.
[36] Tanaka R, Kyosuke N, Sen Y. VISUALMRC: Machine reading comprehension on document images[C]//Proceedings of the AAAI Conference Artifical Intelligence, 2021,35(15): 13878-13888.
[37] Chen X. WebSRC: A dataset for web-based structural reading comprehension[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2021:4173-4185.
[38] Kumar J, Peng Y, Doermann D. Structural similarity for document image classification and retrieval[J]. Pattern Recognit. Lett. 2014(43): 119-126.
[39] Harley A W, Alex U, Konstantinos G D. Evaluation of deep convolutional nets for document image classification and retrieval[C]//Proceedings of the 13th International Conference on Document Analysis and Recognition, 2015: 991-995.
[40] Nagy G, Sharad C S. Hierarchical representation of optically scanned documents[C]//Proceedings of the 7th International Conference on Pattern Recognition, 1984: 347-349.
[41] Itay B Y. Line segmentation for degraded handwritten historical documents[C]//Proceedings of the 10th International Conference on Document Analysis and Recognition. 2009: 1161-1165.
[42] O′gorman L. The document spectrum for page layout analysis[J].IEEE Transactions on pattern analysis and machine intelligence,1993(15): 1162-1173.
[43] Sylwester D, Sharad S. A trainable, single-pass algorithm for column segmentation[C]//Proceedings of 3rd International Conference on Document Analysis and Recognition, 1995: 615-618.
[44] Wong K Y, Richard G C, Friedrich M W. Document analysis system[J].IBM journal of research and development, 1982,26(6): 647-656.
[45] Fisher J L, Stuart C H, Donald P, et al. A rule-based system for document image segmentation[C]//Proceedings of 10th International Conference on Pattern Recognition, 1990: 567-572.
[46] Esposito F. An experimental page layout recognition system for office document automatic classification: an integrated approach for inductive generalization[C]//Proceedings of 10th International Conference on Pattern Recognition, 1990: 557-562.
[47] Shi Z, Venu G. Line separation for complex document images using fuzzy runlength[C]//Proceedings of 1st International Workshop on Document Image Analysis for Libraries, 2004: 306-312.
[48] Saitoh T, Michiyoshi T, Toshifumi Y. Document image segmentation and text area ordering[C]//Proceedings of 2nd International Conference on Document Analysis and Recognition, 1993: 323-329.
[49] Kise K, Akinori S, Motoi I. Segmentation of page images using the area Voronoi diagram[J].Computer Vision and Image Understanding, 1998(70): 370-382.
[50] Bukhari S S. Document image segmentation using discriminative learning over connected components[C]//Proceedings of the 9th IAPR International Workshop on Document Analysis Systems,2010: 183-190.
[51] Baird H S, Susan E J, Steven J F. Image segmentation by shape-directed covers[C]//Proceedings of 10th International Conference on Pattern Recognition, 1990: 820-825.
[52] Xiao Y, Hong Y. Text region extraction in a document image based on the Delaunay tessellation[J].Pattern Recognition,2003 (36): 799-809.
[53] Bukhari S S, Faisal S, Thomas M B. Script-independent handwritten textlines segmentation using active contours[C]//Proceedings of the 10th International Conference on Document Analysis and Recognition, 2009: 446-450.
[54] Okamoto M, Makoto T. A hybrid page segmentation method[C]//Proceedings of the 2nd International Conference on Document Analysis and Recognition, 1993: 743-746.
[55] Smith R W. Hybrid page layout analysis via tab-stop detection[C]//Proceedings of the 10th International Conference on Document Analysis and Recognition, 2009: 241-245.
[56] Baechler M, Rolf I. Multi resolution layout analysis of medieval manuscripts using dynamic MLP[C]//Proceedings of the International Conference on Document Analysis and Recognition. 2011: 1185-1189.
[57] Esposito F. Machine learning for digital document processing: from layout analysis to metadata extraction[M].Machine learning in document analysis and recognition. Springer, 2008: 105-138.
[58] Dietterich T G, Richard H L, Toms L P. Solving the multiple instance problem with axis-parallel rectangles[J]. Artificial intelligence,1997(89): 31-71.
[59] Wu C C,Chou C H, Fu C. A machine-learning approach for analyzing document layout structures with two reading orders[J]. Pattern recognition,2008 (41): 3200-3213.
[60] Wei H. Evaluation of SVM, MLP and GMM classifiers for layout analysis of historical documents[C]//Proceedings of the 12th International Conference on Document Analysis and Recognition,2013: 1220-1224.
[61] Bukhari S S. Layout analysis for Arabic historical document images using machine learning[C]//Proceedings of the International Conference on Frontiers in Handwriting Recognition, 2012: 639-644.
[62] Wang Y, Robert H, Ihsin T P. Improvement of zone content classification by using background analysis[C]//Proceedings of the 4th IAPR International Workshop on Document Analysis Systems. 2000.
[63] Wang Y, Ihsin T P, Robert Haralick. Automatic table ground truth generation and a background-analysis-based table structure extraction method[C]//Proceedings of 6th International Conference on Document Analysis and Recognition. 2001: 528-532.
[64] Wang Y, Ihsin T P, Robert M H. Table detection via probability optimization[C]//Proceedings of the International Workshop on Document Analysis Systems, 2002: 272-282.
[65] Pinto D. Table extraction using conditional random fields[C]//Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, 2003: 235-242.
[66] Silva E, Ana C. Learning rich hidden Markov models in document analysis: table location [C]//Proceedings of the 10th International Conference on Document Analysis and Recognition, 2009: 843-847.
[67] Chen J, Daniel L. Table detection in noisy off-line handwritten documents[C]//Proceedings of the International Conference on Document Analysis and Recognition, 2011: 399-403.
[68] Kasar T, et al. Learning to detect tables in scanned document images using line information[C]//Proceedings of the 12th International Conference on Document Analysis and Recognition, 2013: 1185-1189.
[69] Barlas P. A typed and handwritten text block segmentation system for heterogeneous and complex documents[C]//Proceedings of the 11th IAPR International Workshop on Document Analysis Systems, 2014: 46-50.
[70] Bansal A, Gaurav H, Sumantra D R. Table extraction from document images using fixed point model[C]//Proceedings of the Indian Conference on Computer Vision Graphics and Image Processing, 2014: 1-8.
[71] Bloomberg D S. Multiresolution morphological approach to document image analysis[C]//Proceedings of the International Conference on Document Analysis and Recognition, Saint-Malo, France, 1991.
[72] Li Q. Fixed-point model for structured labeling[C]//Proceedings of the International Conference on Machine Learning, 2013: 214-221.
[73] Rashid S F. Table recognition in heterogeneous documents using machine learning[C]//Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition, 2017: 777-782.
[74] Binmakhashen G M, Sabri A M. Document layout analysis: a comprehensive survey[J]. ACM computing surveys, 2019(52): 1-36.
[75] Viana M P, Drio A B O. Fast CNN-based document layout analysis[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017: 1173-1180.
[76] Chen K. Convolutional neural networks for page segmentation of historical document images[C]//Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition, 2017: 965-970.
[77] Oliveira S A, Benoit S, Frederic K. dhSegment: A generic deep-learning approach for document segmentation[C]//Proceedings of the 16th International Conference on Frontiers in Handwriting Recognition, 2018: 7-12.
[78] Wick C, Frank P. Fully convolutional neural networks for page segmentation of historical document images[C]//Proceedings of the 13th IAPR International Workshop on Document Analysis Systems, 2018: 287-292.
[79] Grüning, T. A two-stage method for text line detection in historical documents[J].International Journal on Document Analysis and Recognition, 2019(22): 285-302.
[80] Soto C, Shinjae Y. Visual detection with context for document layout analysis[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong: Association for Computational Linguistics, 2019: 3462-3468.
[81] Siddiqui S A. Decnt: Deep deformable CNN for table detection[J]. IEEE Access, 2018(6): 74151-74161.
[82] Prasad D. Cascade TabNet: An approach for end to end table detection and structure recognition from image-based documents[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020: 572-573.
[83] Cai Z, Nuno V. Cascade R-CNN: Delving into high quality object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 6154-6162.
[84] Dong H. Tablesense: Spreadsheet table detection with convolutional neural networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019: 69-76.
[85] Herzig J. TaPas: Weakly supervised table parsing via pre-training[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020.
[86] Wang Z. TUTA: Tree-based transformers for generally structured table pre-training[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021.
[87] Yu W. PICK: Processing key information extraction from documents using improved graph learning-convolutional networks[C]//Proceedings of the 25th International Conference on Pattern Recognition, 2021: 4363-4370.
[88] Katti A R. Chargrid: Towards understanding 2D documents[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics, 2018. 4459-4469.
[89] Kerroumi M. VisualWordGrid: Information extraction from scanned documents using a multimodal approach[G].Lecture Notes in Computer Science, 2021: 389-402.
[90] Denk T I, Christian R. BERTgrid: Contextualized embedding for 2D document representation and understanding[C]//Proceedings of Document Intelligence Workshop of 33rd Conference on Neural Information Processing Systems,2019.
[91] Lin W. ViBERTgrid: A jointly trained multi-modal 2D document representation for key information extraction from documents[C]//Proceedings of Lecture Notes in Computer Science, 2021: 548-63.
[92] Majumder B P. Representation learning for information extraction from form-like documents[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 6495-6504.
[93] Zhang P. Trie: End-to-end text reading and information extraction for document understanding[C]//Proceedings of the 28th ACM International Conference on Multimedia, 2020: 1413-1422.
[94] Wang Z. DocStruct: A multimodal method to extract hierarchy structure in document for general form understanding[C]//Proceedings of the Association for Computational Linguistics,2020.
[95] Wang H W. Spatial dependency parsing for semi-structured document information extraction[C]//Proceedings of the Association for Computational Linguistics,2021.
[96] Riba P. Table detection in invoice documents by graph neural networks[C]//Proceedings of the International Conference on Document Analysis and Recognition, 2019: 122-127.
[97] Wei M, He Y, Zhang Q. Robust layout-aware IE for visually rich documents with pre-trained language models[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020: 2367-2376.
[98] Cheng M. One-shot text field labeling using attention and belief propagation for structure information extraction[C]//Proceedings of the 28th ACM International Conference on Multimedia, 2020: 340-348.
[99] Afzal M Z. Deep doc classifier: document classification with deep convolutional neural network[C]//Proceedings of the 13th International Conference on Document Analysis and Recognition, 2015: 1111-1115.
[100] Afzal M Z. Cutting the error by half: Investigation of very deep CNN and advanced training strategies for document image classification[C]//Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition, 2017: 883-888.
[101] Tensmeyer C, Tony M. Analysis of convolutional neural networks for document image classification[C]//Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition, 2017: 388-393.
[102] Das A. Document image classification with intra-domain transfer learning and stacked generalization of deep convolutional neural networks[C]//Proceedings of the 24th International Conference on Pattern Recognition, 2018: 3180-3185.
[103] Sarkhel R, Arnab N. Deterministic routing between layout abstractions for multi-scale classification of visually rich documents[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019.
[104] Dauphinee T, Nikunj P, Mohammad R. Modular multimodal architecture for document classification[J].arXiv preprint arXiv:1912.04376, 2019.
[105] Xu Y. LayoutLMv2: Multi-modal pre-training for visually-rich document understanding[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Online: Association for Computational Linguistics, 2021: 2579-2591.
[106] Hong T. BROS: A pre-trained language model for understanding texts in document[C]//Proceedings of ICLR, 2021:1-17.
[107] Li C. Structural LM: Structural pre-training for form understanding[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021:6309-6318.
[108] Wu T. LAMPRET: Layout-aware multimodal pretraining for document understanding[J].arXiv preprint arXiv:2104.08405, 2021.
[109] Li P. SelfDoc: Self-supervised document representation learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 5652-5660.
[110] Reimers N, Iryna G. Sentence-BERT: Sentence embeddings using siamese BERT-networks[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong: Association for Computational Linguistics, 2019: 3982-3992.
[111] Appalaraju S. DocFormer: End-to-End transformer for document understanding[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021:973-983.
[112] Joshi M. SpanBERT: Improving pre-training by representing and predicting spans[J].Transactions of the Association for Computational Linguistics, 2020(8): 64-77.
[113] Garncarek . LAMBERT: Layout-aware language modeling for information extraction[G].Lecture Notes in Computer Science, 2021: 532-547.
[114] Liu Y. RoBERTa: A robustly optimized BERT pretraining approach[J].arXiv preprint arXiv:1907.11692, 2019.
[115] Powalski R. Going full-TILT boogie on document understanding with text-image-layout transformer[G].Lecture Notes in Computer Science, 2021: 732-747.
[116] Raffel C. Exploring the limits of transfer learning with a unified text-to-text transformer[J]Journal of Machine Learning Research, 2020(21): 1-67.
[117] Lewis M. BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 7871-7880.
[118] Li J. MarkupLM: Pre-training of text and markup language for visually-rich document understanding[C]//Proceedings of the 60th ACL, 2022:6078-6087.
[119] Dosovitskiy A. An image is worth 16x16 words: transformers for image recognition at scale[C]//Proceedings of the ICLR, 2021: 1-22.
[120] Touvron H. Training data-efficient image transformers and distillation through attention[C]//Proceedings of the International Conference on Machine Learning, 2021: 10347-10357.
[121] Liu Z. Swin transformer: Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 10012-10022.
[122] Chen X. An empirical study of training self-supervised vision transformers[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 9640-9649.
[123] Bao H. BEiT: BERT pre-training of image transformers[J]. arXiv preprint arXiv: 2106.08254,2021.
[124] El-nouby A. XCiT: Cross-covariance image transformers[C]//Proceedings of the NeurIPS, 2021: 1-14.
[125] He K. Masked autoencoders are scalable vision learners[G].Masked Autoencoders Are Scalable Vision Learners. CVPR, 2022: 16000-16009.
[126] Zhou J. iBOT: Image BERT pre-training with online tokenizer[J]. arXiv preprint arXiv: 2111.07832,2021.
[127] Li J. DiT: Self-supervised pre-training for document image transformer[J]. arXiv preprint arXiv: 2203.02378, 2022.
[128] Devlin J. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the NAACL, 2019: 4171-4186.
[129] Brown T B. Language models are few-shot learners[C]//Proceedings of the NeurIPS, 2020,2: 1877-1901.
PDF(5178 KB)

Accesses

Citation

Detail

段落导航
相关文章

/