Conversational tutoring systems (CTSs) aim to help students master
educational material with natural language interaction in the form of a dialog.
CTSs have become a key pillar in educational data mining research. A key
challenge in CTSs is to engage the student in the conversation while exposing
them to a diverse set of teaching strategies, akin to a human teacher, thereby,
helping them learn in the process. Different from previous work that generates
responses given the strategies as input, we propose to jointly predict teaching
strategies and generate tutor responses accordingly, which fits a more
realistic application scenario. We benchmark several competitive models on
three dialog tutoring datasets and propose a unified framework that combines
teaching response generation and pedagogical strategy prediction, where a
self-distillation mechanism is adopted to guide the teaching strategy learning
and facilitate tutor response generation. Our experiments and analyses shed
light on how teaching strategies affect dialog tutoring.

本文提出了一种在教学策略预测和生成辅导响应之间进行联合预测的方法，并选择了若干个对话辅导数据集进行基准测试，最终提出了一种统一框架，该框架将教学响应生成和学术策略预测相结合，并采用了自身蒸馏机制来指导教学策略学习和促进辅导响应生成。通过实验和分析，揭示了教学策略对话式辅导的影响。

教学前策略规划：具备自我精华提炼技术的对话式辅导系统

Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation

Pre-trained language models like BERT have proven to be highly performant.
However, they are often computationally expensive in many practical scenarios,
for such heavy models can hardly be readily implemented with limited resources.
To improve their efficiency with an assured model performance, we propose a
novel speed-tunable FastBERT with adaptive inference time. The speed at
inference can be flexibly adjusted under varying demands, while redundant
calculation of samples is avoided. Moreover, this model adopts a unique
self-distillation mechanism at fine-tuning, further enabling a greater
computational efficacy with minimal loss in performance. Our model achieves
promising results in twelve English and Chinese datasets. It is able to speed
up by a wide range from 1 to 12 times than BERT if given different speedup
thresholds to make a speed-performance tradeoff.

本文提出了一种新颖的速度可调的 FastBERT 语言模型，通过采用自适应推理时间和独特的自蒸馏机制，在保证性能的同时提高了计算效率，并在 12 个英文和中文数据集上取得了良好的结果。