One of the main challenges in conversational question answering (CQA) is to
resolve the conversational dependency, such as anaphora and ellipsis. However,
existing approaches do not explicitly train QA models on how to resolve the
dependency, and thus these models are limited in understanding human dialogues.
In this paper, we propose a novel framework, ExCorD (Explicit guidance on how
to resolve Conversational Dependency) to enhance the abilities of QA models in
comprehending conversational context. ExCorD first generates self-contained
questions that can be understood without the conversation history, then trains
a QA model with the pairs of original and self-contained questions using a
consistency-based regularizer. In our experiments, we demonstrate that ExCorD
significantly improves the QA models' performance by up to 1.2 F1 on QuAC, and
5.2 F1 on CANARD, while addressing the limitations of the existing approaches.

本文提出了一种名为 ExCorD 的新型框架，旨在解决会话依赖性的问题，通过通过生成自包含的问题和使用一种基于一致性的正则化器来训练 QA 模型，显著提高了 QA 模型的性能。

学习解决对话依赖：一种面向对话问答的一致性训练框架

Learn to Resolve Conversational Dependency: A Consistency Training  Framework for Conversational Question Answering

Many natural language questions require qualitative, quantitative or logical
comparisons between two entities or events. This paper addresses the problem of
improving the accuracy and consistency of responses to comparison questions by
integrating logic rules and neural models. Our method leverages logical and
linguistic knowledge to augment labeled training data and then uses a
consistency-based regularizer to train the model. Improving the global
consistency of predictions, our approach achieves large improvements over
previous methods in a variety of question answering (QA) tasks including
multiple-choice qualitative reasoning, cause-effect reasoning, and extractive
machine reading comprehension. In particular, our method significantly improves
the performance of RoBERTa-based models by 1-5% across datasets. We advance the
state of the art by around 5-8% on WIQA and QuaRel and reduce consistency
violations by 58% on HotpotQA. We further demonstrate that our approach can
learn effectively from limited data.

本文通过整合逻辑规则和神经模型，使用基于一致性的正则化来提高问答任务中对比问题的响应的准确性和一致性，该方法在各种问题回答（QA）任务中获得大幅提高。特别是，在数据集中，我们的方法使 RoBERTa 模型的性能提高了 1-5％，，并在 WIQA 和 QuaRel 上将现有技术水平提高了约 5-8％，在 HotpotQA 上减少一致性违规 58％。同时，我们进一步证明了我们的方法可以从有限的数据中有效地学习。