Co-Teaching Student-Model through Submission Results of Shared Task-猫眼课题宝

DOI：

10.18653/v1/2021.findings-emnlp.383

发表时间：

2021

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Kouta Nakayama;Shuhei Kurita;Akio Kobayashi;Yukino Baba;S. Sekine

共享任务由来已久，已成为自然语言处理研究的主流。大多数共享任务只要求参与者提交系统产出和说明。由于许可证问题和实现差异，共享任务请求提交系统本身的情况并不常见。因此，许多系统没有在实际应用中使用，也没有设计出更好的系统，就被抛弃了。在本研究中，我们提出了一种利用所有参与共享任务的系统的方案。在该方案中，我们使用所有参与的系统输出作为任务教师，并以学生的身份开发了一个新的模型，旨在了解每个系统的特点。我们把这项计划称为“合作教学”。该方案创建了一个比任务的单一最佳系统执行得更好的单一ﬁ系统。它只需要系统输出，参与者和组织者需要稍微额外的工作。我们将该方案应用于“SHINRA2019-JP”共享任务，该任务有9个参与者，具有不同的输出精度，ﬁ证实了单一ﬁ系统的性能优于最好的系统。此外，我们实验中使用的代码已经发布。1

Shared tasks have a long history and have become the mainstream of NLP research. Most of the shared tasks require participants to submit only system outputs and descriptions. It is uncommon for the shared task to request submission of the system itself because of the license issues and implementation differences. Therefore, many systems are abandoned without being used in real applications or contribut-ing to better systems. In this research, we propose a scheme to utilize all those systems which participated in the shared tasks. We use all participated system outputs as task teachers in this scheme and develop a new model as a student aiming to learn the characteris-tics of each system. We call this scheme “Co-Teaching.” This scheme creates a uniﬁed system that performs better than the task’s single best system. It only requires the system outputs, and slightly extra effort is needed for the participants and organizers. We apply this scheme to the “SHINRA2019-JP” shared task, which has nine participants with various output accuracies, conﬁrming that the uniﬁed system outperforms the best system. Moreover, the code used in our experiments has been released. 1

Co-Teaching Student-Model through Submission Results of Shared Task

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