Human communication often involves information gaps between the
interlocutors. For example, in an educational dialogue, a student often
provides an answer that is incomplete, and there is a gap between this answer
and the perfect one expected by the teacher. Successful dialogue then hinges on
the teacher asking about this gap in an effective manner, thus creating a rich
and interactive educational experience. We focus on the problem of generating
such gap-focused questions (GFQs) automatically. We define the task, highlight
key desired aspects of a good GFQ, and propose a model that satisfies these.
Finally, we provide an evaluation by human annotators of our generated
questions compared against human generated ones, demonstrating competitive
performance.

人际交流通常涉及到交流双方之间的信息缺口。

突破常规：针对答案评估的差异关注型问题生成

Covering Uncommon Ground: Gap-Focused Question Generation for Answer  Assessment

This paper describes the results of the first shared task on the generation
of teacher responses in educational dialogues. The goal of the task was to
benchmark the ability of generative language models to act as AI teachers,
replying to a student in a teacher-student dialogue. Eight teams participated
in the competition hosted on CodaLab. They experimented with a wide variety of
state-of-the-art models, including Alpaca, Bloom, DialoGPT, DistilGPT-2,
Flan-T5, GPT-2, GPT-3, GPT- 4, LLaMA, OPT-2.7B, and T5-base. Their submissions
were automatically scored using BERTScore and DialogRPT metrics, and the top
three among them were further manually evaluated in terms of pedagogical
ability based on Tack and Piech (2022). The NAISTeacher system, which ranked
first in both automated and human evaluation, generated responses with GPT-3.5
using an ensemble of prompts and a DialogRPT-based ranking of responses for
given dialogue contexts. Despite the promising achievements of the
participating teams, the results also highlight the need for evaluation metrics
better suited to educational contexts.

本文选举为教育对话中教师回应产生共性任务的首个任务，以基于生成语言模型的人工智能教师为校准目标；在八个参赛者中，NAISTeacher 系统以 GPT-3.5 生成器为基础靠类似于提示流的集合和对话环境上的排名生成，达到自动和人工评估的双重第一名；然而，结果也凸显出现有的关键问题，包括需适合于教育场景的评估指标等。