从少量标记过的样本中学习是一个困难的问题,也是将当前的 ML 模型与更普遍适用的系统分离开来的核心功能之一。据我所知,零次学习只在有未知单词嵌入的上下文学习中研究过。无数据分类(Song and Roth,2014;Song et al., 2016)[25, 26] 是一个有趣的研究方向,在一个联合空间中融合了标签和文档,但需要有良好表述的可解释性标签。
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105). ↩
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017). Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. arXiv Preprint arXiv:1703.06907. Retrieved from http://arxiv.org/abs/1703.06907 ↩
Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017). mixup: Beyond Empirical Risk Minimization, 1–11. Retrieved from http://arxiv.org/abs/1710.09412 ↩
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. (2016). Matching Networks for One Shot Learning. NIPS 2016. Retrieved from http://arxiv.org/abs/1606.04080 ↩
Li, Y., Cohn, T., & Baldwin, T. (2017). Robust Training under Linguistic Adversity. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (Vol. 2, pp. 21–27). ↩
Wang, D., & Eisner, J. (2016). The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages. Tacl, 4, 491–505. Retrieved from https://www.transacl.org/ojs/index.php/tacl/article/viewFile/917/212%0Ahttps://transacl.org/ojs/index.php/tacl/article/view/917 ↩
Liu, T., Cui, Y., Yin, Q., Zhang, W., Wang, S., & Hu, G. (2017). Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (pp. 102–111). ↩
Samanta, S., & Mehta, S. (2017). Towards Crafting Text Adversarial Samples. arXiv preprint arXiv:1707.02812. ↩
Ebrahimi, J., Rao, A., Lowd, D., & Dou, D. (2017). HotFlip: White-Box Adversarial Examples for NLP. Retrieved from http://arxiv.org/abs/1712.06751 ↩
Yasunaga, M., Kasai, J., & Radev, D. (2017). Robust Multilingual Part-of-Speech Tagging via Adversarial Training. In Proceedings of NAACL 2018. Retrieved from http://arxiv.org/abs/1711.04903 ↩
Jia, R., & Liang, P. (2017). Adversarial Examples for Evaluating Reading Comprehension Systems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. ↩
Sennrich, R., Haddow, B., & Birch, A. (2015). Improving neural machine translation models with monolingual data. arXiv preprint arXiv:1511.06709. ↩
Sennrich, R., Haddow, B., & Birch, A. (2016). Edinburgh neural machine translation systems for wmt 16. arXiv preprint arXiv:1606.02891. ↩
Mallinson, J., Sennrich, R., & Lapata, M. (2017). Paraphrasing revisited with neural machine translation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers (Vol. 1, pp. 881-893). ↩
Dong, L., Mallinson, J., Reddy, S., & Lapata, M. (2017). Learning to Paraphrase for Question Answering. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. ↩
Li, J., Monroe, W., Shi, T., Ritter, A., & Jurafsky, D. (2017). Adversarial Learning for Neural Dialogue Generation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Retrieved from http://arxiv.org/abs/1701.06547 ↩
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., & Bengio, S. (2016). Generating Sentences from a Continuous Space. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL). Retrieved from http://arxiv.org/abs/1511.06349 ↩
Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., & Xing, E. P. (2017). Toward Controlled Generation of Text. In Proceedings of the 34th International Conference on Machine Learning. ↩
Guu, K., Hashimoto, T. B., Oren, Y., & Liang, P. (2017). Generating Sentences by Editing Prototypes. ↩
Shen, T., Lei, T., Barzilay, R., & Jaakkola, T. (2017). Style Transfer from Non-Parallel Text by Cross-Alignment. In Advances in Neural Information Processing Systems. Retrieved from http://arxiv.org/abs/1705.09655 ↩
Mrkšić, N., Vulić, I., Séaghdha, D. Ó., Leviant, I., Reichart, R., Gašić, M., … Young, S. (2017). Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints. TACL. Retrieved from http://arxiv.org/abs/1706.00374 ↩
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). Why should i trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144). ACM. ↩
Ravi, S., & Larochelle, H. (2017). Optimization as a Model for Few-Shot Learning. In ICLR 2017. ↩
Snell, J., Swersky, K., & Zemel, R. S. (2017). Prototypical Networks for Few-shot Learning. In Advances in Neural Information Processing Systems. ↩
Song, Y., & Roth, D. (2014). On dataless hierarchical text classification. Proceedings of AAAI, 1579–1585. Retrieved from http://cogcomp.cs.illinois.edu/papers/SongSoRo14.pdf ↩
Song, Y., Upadhyay, S., Peng, H., & Roth, D. (2016). Cross-Lingual Dataless Classification for Many Languages. Ijcai, 2901–2907. ↩
Augenstein, I., Ruder, S., & Søgaard, A. (2018). Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces. In Proceedings of NAACL 2018. ↩
Alonso, H. M., & Plank, B. (2017). When is multitask learning effective? Multitask learning for semantic sequence prediction under varying data conditions. In EACL. Retrieved from http://arxiv.org/abs/1612.02251 ↩
Misra, I., Shrivastava, A., Gupta, A., & Hebert, M. (2016). Cross-stitch Networks for Multi-task Learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. http://doi.org/10.1109/CVPR.2016.433 ↩
Ruder, S., Bingel, J., Augenstein, I., & Søgaard, A. (2017). Sluice networks: Learning what to share between loosely related tasks. arXiv preprint arXiv:1705.08142. ↩
Peters, M. E., Ammar, W., Bhagavatula, C., & Power, R. (2017). Semi-supervised sequence tagging with bidirectional language models. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017). ↩
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. Proceedings of NAACL. ↩
Howard, J., & Ruder, S. (2018). Fine-tuned Language Models for Text Classification. arXiv preprint arXiv:1801.06146. ↩
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017). Supervised Learning of Universal Sentence Representations from Natural Language Inference Data. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. ↩
Subramanian, S., Trischler, A., Bengio, Y., & Pal, C. J. (2018). Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning. In Proceedings of ICLR 2018. ↩
Ruder, S., Vulić, I., & Søgaard, A. (2017). A Survey of Cross-lingual Word Embedding Models. arXiv Preprint arXiv:1706.04902. Retrieved from http://arxiv.org/abs/1706.04902 ↩
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. ↩
Mou, L., Meng, Z., Yan, R., Li, G., Xu, Y., Zhang, L., & Jin, Z. (2016). How Transferable are Neural Networks in NLP Applications? Proceedings of 2016 Conference on Empirical Methods in Natural Language Processing. ↩
Xie, Z., Wang, S. I., Li, J., Levy, D., Nie, A., Jurafsky, D., & Ng, A. Y. (2017). Data Noising as Smoothing in Neural Network Language Models. In Proceedings of ICLR 2017. ↩
Nie, A., Bennett, E. D., & Goodman, N. D. (2017). DisSent: Sentence Representation Learning from Explicit Discourse Relations. arXiv Preprint arXiv:1710.04334. Retrieved from http://arxiv.org/abs/1710.04334 ↩