Journal of East China Normal University(Educational Sciences) ›› 2022, Vol. 40 ›› Issue (9): 19-31.doi: 10.16382/j.cnki.1000-5560.2022.09.003
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Bo Zhang1, Ruihai Dong2
Accepted:
2022-05-30
Online:
2022-09-01
Published:
2022-08-24
Bo Zhang, Ruihai Dong. How Natural Language Processing Technology Empowers the AIED: The Perspective of AI Scientist[J]. Journal of East China Normal University(Educational Sciences), 2022, 40(9): 19-31.
1 | 阿里云. (2020). 智能文本分类: https://ai.aliyun.com/nlp/tc. |
2 |
戴静, 顾小清. (2020). 人工智能将把教育带往何方——WIPO《2019 技术趋势: 人工智能》 报告解读. 中国电化教育, (10), 24- 31.
doi: 10.3969/j.issn.1006-9860.2020.10.004. |
3 | 德勤. (2020). 全球教育智能化发展报告. 取自德勤网站: https://www2.deloitte.com/cn/zh/pages/technology-media-and-telecommunications/articles/development-of-ai-based-education-in-china.html. |
4 | 何晓东. (2019). 自然语言处理帮助人类更好地连接世界. 中国人工智能大会, http://2019.ccai.cn/. |
5 | 彭绍东.(2021). 人工智能教育的含义界定与原理挖掘. 中国电化教育, (06), 49−59. |
6 | 前瞻产业研究院. (2020). 2021—2026中国智能教育行业发展前景预测与投资战略规划分析报告. |
7 | 清华大学人工智能研究院. (2020). 人工智能发展报告(2011—2020). |
8 | 沈向洋.(2018).人工智能将会颠覆所有的商业应用.人工智能大会. 取自微软网站(2018年9月19日): https://www.microsoft.com/zh-cn/ard/news/news_2018_58. |
9 | 伍红林. (2020). 人工智能进步可能为当代教育学发展带来什么?. 大学教育科学, (05), 103- 111. |
10 | 微软亚洲研究院. (2019). 周明: 自然语言处理的技术体系和未来之路. 取自微软亚洲研究院网站(2019年7月15日): https://www.msra.cn/zh-cn/news/features/ccf-gair-2019-ming-zhou. |
11 | 吴永和, 刘博文, 马晓玲. (2017). 构筑“人工智能+ 教育”的生态系统. 远程教育杂志, 35 (5), 27- 39. |
12 | 郑南宁. (2019). 面对人工智能挑战 人才培养的下一步该如何走. 中国大学教学, (02), 9- 13+8. |
13 | 知乎. (2019). 华为语音语义首席科学家刘群谈“自然语言处理”. 取自知乎(2019年2月12日): https://zhuanlan.zhihu.com/p/56526597. |
14 | 宗成庆.(2020). 特约专栏: 人类语言技术展望. CAAI中国人工智能学会通讯, 1(10). |
15 | 庄福振, 罗平, 何清, 史忠植. (2015). 迁移学习研究进展. 软件学报, 26 (1), 26- 39. |
16 | Alhawiti, K. M. (2014). Natural language processing and its use in education. Tabuk, Saudi Arabia. |
17 |
Ardoin, S. P., Williams, J. C., Christ, T. J., Klubnik, C., & Wellborn, C. (2010). Examining readability estimates’ predictions of students’ oral reading rate: Spache, Lexile, and Forcast. School Psychology Review, 39 (2), 277- 285.
doi: 10.1080/02796015.2010.12087778 |
18 | Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv : 1409.0473. |
19 | Bengio, Y., Ducharme, R., Vincent, P., & Janvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3 (Feb), 1137- 1155. |
20 | Bin Dahmash, N. (2020). ‘I can’t live without Google Translate’: A close look at the use of Google Translate App by second language learners in Saudi Arabia. Arab World English Journal (AWEJ)., 11 (3), 226- 240. |
21 | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of machine Learning research, 3, 993- 1022. |
22 | Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P. , . . . & Amodei, D. (2020). Language models are few-shot learners. arXiv: 2005.14165. |
23 | Dan, J. (2017). Dan Jurafsky on natural language processing. Retrieved from https://www.youtube.com/watch?v=QIdB6M5WdkI. |
24 |
Cancino, M., & Panes, J. (2021). The impact of Google Translate on L2 writing quality measures: Evidence from Chilean EFL high school learners. System, 98, 102464.
doi: 10.1016/j.system.2021.102464 |
25 | Chen, X., Cui, Z., Zhang, J., Wei, C., Cui, J., Wang, B. , . . . & Yan, R. (2020). Reasoning in dialog: Improving response generation by context reading comprehension. arXiv: 2012.07410. |
26 | Chen, P., Lu, Y., Liu, J., & Xu, Q. (2021). An intelligent assistant for problem behavior management. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 18, pp. 16007−16010). |
27 | Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv: 1406.1078. |
28 | Conneau, A., Lample, G., Ranzato, M. A., Denoyer, L., & Jégou, H. (2017). Word translation without parallel data. arXiv : 1710.04087. |
29 | Cynthia, B. (2019). Developing social and empathetic AI. Retrieved from https://www.youtube.com/watch?v=T52g7dCxJ4A. |
30 | Dragomir, R. (2017). Deep learning for NLP. Retrieved from https://vimeo.com/230044807 . |
31 | Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv: 1810.04805. |
32 | Fedus, W., Zoph, B., & Shazeer, N. (2021). Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. arXiv: 2101.03961. |
33 | Guo, Q., Qiu, X., Liu, P., Xue, X., & Zhang, Z. (2020). Multi-scale self-attention for text classification. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 05, pp. 7847−7854). |
34 | Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 50−57). |
35 | Kalchbrenner, N., & Blunsom, P. (2013). Recurrent continuous translation models. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 1700−1709). |
36 | Koelstra, S., Muhl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., . . & Patras, I. (2011). Deap: A database for emotion analysis; using physiological signals. IEEE transactions on affective computing, 3 (1), 18- 31. |
37 | Lex, F. (2021). MIT deep learning and artificial intelligence lectures. Retrieved from https://deeplearning.mit.edu/. |
38 | Liu, Y., Zhang, J., Xiong, H., Zhou, L., He, Z., Wu, H., . . & Zong, C. (2020). Synchronous speech recognition and speech-to-text translation with interactive decoding. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 05, pp. 8417−8424). |
39 | Lu, Y., Pian, Y., Chen, P., Meng, Q., & Cao, Y. (2021). RadarMath: An intelligent tutoring system for math education. Interaction, 1, U2. |
40 | Matthew, M. (2020). AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021. Retrieved from https://www.kdnuggets.com/2020/12/predictions-ai-machine-learning-data-science-research.html. |
41 | Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv: 1301.3781. |
42 | Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep learning-based text classification: A comprehensive review. ACM Computing Surveys (CSUR), 54 (3), 1- 40. |
43 | Newman, H., & Joyner, D. (2018). Sentiment analysis of student evaluations of teaching. In International conference on artificial intelligence in education (pp. 246−250). Springer, Cham. |
44 | OpenAI. (2021). DALL·E: Creating Images from Text. Retrieved from https://openai.com/blog/dall-e/. |
45 | Russell, S. J. (2020) . The Future of Artificial Intelligence. Retrieved from https://www.carnegiecouncil.org/studio/multimedia/20200219-future-artificial-intelligence-stuart-russell. |
46 | Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002). Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics (pp. 311−318). |
47 | Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532−1543). |
48 | Peng, Y., Chen, P., Lu, Y., Meng, Q., Xu, Q., & Yu, S. (2019). A task-oriented dialogue system for moral education. In International Conference on Artificial Intelligence in Education (pp. 392−397). Springer, Cham. |
49 | Qi, F., Chang, L., Sun, M., Ouyang, S., & Liu, Z. (2020). Towards building a multilingual sememe knowledge base: Predicting sememes for BabelNet synsets. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 05, pp. 8624−8631). |
50 | Rajput, Q., Haider, S., & Ghani, S. (2016). Lexicon-based sentiment analysis of teachers’ evaluation. Applied computational intelligence and soft computing, 2016. |
51 | Rajpurkar, P., Jia, R., & Liang, P. (2018). Know what you don't know: Unanswerable questions for SQuAD. arXiv: 1806.03822. |
52 |
Ruder, S., Vulic, I., & Søgaard, A. (2019). A survey of cross-lingual word embedding models. Journal of Artificial Intelligence Research, 65, 569- 631.
doi: 10.1613/jair.1.11640 |
53 | Russell, S., & Norvig, P. (2002). Artificial intelligence: a modern approach. |
54 | Sebastian, R. (2021). ML and NLP Research Highlights of 2020. Retrieved from https://ruder.io/research-highlights-2020/ . |
55 | Shao, C., Zhang, J., Feng, Y., Meng, F., & Zhou, J. (2020). Minimizing the bag-of-ngrams difference for non-autoregressive neural machine translation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 198−205). |
56 | Shoham, Y., Perrault, R., Brynjolfsson, E., & Clark,J. (2017) . 2017 Stanford Artificial Intelligence Index Annual Report. |
57 | Shoham, Y., Perrault, R., Brynjolfsson, E., Clark,J. Manyika, J., Niebles, J. C., Lyons,T., Etchemendy, J., Grosz, B., & Bauer, Z. (2018). The AI Index 2018 Annual Report.AI Index Steering Committee, Human-Centered AI Initiative, Stanford University, Stanford, CA. |
58 | SQuAD2.0. (2021). The Stanford Question Answering Dataset. Retrieved from https://rajpurkar.github.io/SQuAD-explorer/. |
59 | Swieczkowski, D., & Kułacz, S. (2021). The use of the Gunning Fog Index to evaluate the readability of Polish and English drug leaflets in the context of Health Literacy challenges in Medical Linguistics: An exploratory study. Cardiology Journal, 28(4), 627−631. |
60 | Tang, Y., & Yu, D. (2020). The method of calculating sentence readability combined with deep learning and language difficulty characteristics. In Proceedings of the 19th Chinese National Conference on Computational Linguistics (pp. 731−742). |
61 | Tongpoon-Patanasorn, A., & Griffith, K. (2020). Google Translate and Translation Quality: A Case of Translating Academic Abstracts from Thai to English. PASAA: Journal of Language Teaching and Learning in Thailand, 60, 134- 163. |
62 | Tzacheva, A., Ranganathan, J., & Jadi, R. (2019). Multi-label emotion mining from student comments. In Proceedings of the 2019 4th International Conference on Information and Education Innovations (pp. 120−124). |
63 | Tsai, S. C. (2020). Chinese students’ perceptions of using Google Translate as a translingual CALL tool in EFL writing. Computer assisted language learning, 1−23. |
64 | Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N. , . . . & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998−6008). |
65 | Wang, Z., Liu, J., & Dong, R. (2018). Intelligent Auto-grading System. In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) (pp. 430−435). IEEE. |
66 | Xu, J., Wang, H., Niu, Z., Wu, H., & Che, W. (2020). Knowledge graph grounded goal planning for open-domain conversation generation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 05, pp. 9338−9345). |
67 | Xu, R., Tao, C., Jiang, D., Zhao, X., Zhao, D., & Yan, R. (2020). Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues. arXiv: 2009.06265. |
68 | Xu, Y., Wu, Z., Huang, H., Yang, T., Yu, P., & Lu, E. (2016). Grammar Automatic Checking System for English Abstract of Master’s Thesis. In International Conference on Bio-Inspired Computing: Theories and Applications (pp. 497−506). Springer, Singapore. |
69 | Yang, L., Li, J., Cunningham, P., Zhang, Y., Smyth, B., & Dong, R. (2021). Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis. arXiv: 2106.15231. |
70 | Zhang, M., Liu, Y., Luan, H., & Sun, M. (2017). Adversarial training for unsupervised bilingual lexicon induction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1959−1970). |
71 | Zhang, Y., Ou, Z., & Yu, Z. (2020). Task-oriented dialog systems that consider multiple appropriate responses under the same context. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 05, pp. 9604−9611). |
72 | Zheng, L., Qi, F., Liu, Z., Wang, Y., Liu, Q., & Sun, M. (2020). Multi-channel reverse dictionary model. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 312−319). |
73 | Zheng, Y., Zhang, R., Mensah, S., & Mao, Y. (2020). Replicate, walk, and stop on syntax: an effective neural network model for aspect-level sentiment classification. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 05, pp. 9685−9692). |
74 | Zheng, Y., Zhang, R., Huang, M., & Mao, X. (2020). A pre-training based personalized dialogue generation model with persona-sparse data. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 05, pp. 9693−9700). |
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