“我从没说过那话”：关于回答清晰度分类的数据集、分类体系与基线
"I Never Said That": A dataset, taxonomy and baselines on response clarity classification
AI总结：
本文针对政治访谈问答对中的回答清晰度问题，提出两级分类体系与标注数据集，并结合ChatGPT与人工标注，通过多架构实验建立新基线。
摘要：
公共演讲中的含糊其辞与歧义是研究充分的语篇现象，尤其在政治学和政治访谈分析中。受关于含糊其辞的扎实理论的启发，我们旨在利用大型语言模型（LLMs）的能力和人类专业知识，解决从政治访谈中提取的问题的回答清晰度这一密切相关问题。为此，我们引入了一个新颖的分类体系，用于构建检测和分类回答清晰度的任务，并提出了相应的清晰度分类数据集，该数据集由取自政治访谈的问答（QA）对组成并进行了相应标注。我们提出的两级分类体系从给定问题所提供信息的角度处理回答的清晰度（高层级），并且提供了与不清晰、含糊回答相关的规避技巧的细粒度分类体系（低层级）。我们结合ChatGPT和人类标注员，从政治访谈中收集、验证并标注离散的QA对，以用于我们新引入的回答清晰度任务。我们提供了详细的分析，并使用不同的模型架构、规模和适应方法进行了多项实验，以获得洞察并在提出的数据集和任务上建立新的基线。
英文摘要：
Equivocation and ambiguity in public speech are well-studied discourse phenomena, especially in political science and analysis of political interviews. Inspired by the well-grounded theory on equivocation, we aim to resolve the closely related problem of response clarity in questions extracted from political interviews, leveraging the capabilities of Large Language Models (LLMs) and human expertise. To this end, we introduce a novel taxonomy that frames the task of detecting and classifying response clarity and a corresponding clarity classification dataset which consists of question-answer (QA) pairs drawn from political interviews and annotated accordingly. Our proposed two-level taxonomy addresses the clarity of a response in terms of the information provided for a given question (high-level) and also provides a fine-grained taxonomy of evasion techniques that relate to unclear, ambiguous responses (lower-level). We combine ChatGPT and human annotators to collect, validate and annotate discrete QA pairs from political interviews, to be used for our newly introduced response clarity task. We provide a detailed analysis and conduct several experiments with different model architectures, sizes and adaptation methods to gain insights and establish new baselines over the proposed dataset and task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
