论文标题
自然语言生成中的忠诚：分析，评估和优化方法的系统调查
Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods
论文作者
论文摘要
自然语言产生（NLG）近年来由于开发了深度学习技术（例如预训练的语言模型），取得了长足的进步。这种进步导致了更流利，连贯甚至属性可控制的（例如，风格，情感，长度等），自然会导致下游任务的发展，例如抽象性摘要，对话生成，机器翻译和数据对文本生成。但是，生成的文本通常包含不忠或非事实的信息的忠诚问题已成为最大的挑战，这使得文本生成的性能在许多真实世界的情况下对实际应用不满意。已经提出了许多有关忠实问题的分析，评估和优化方法的研究，但尚未以合并方式进行组织，比较和讨论。在这项调查中，我们提供了有关NLG忠实问题的研究进度的系统概述，包括问题分析，评估指标和优化方法。我们将不同任务的评估和优化方法组织成统一的分类法，以促进跨任务的比较和学习。进一步讨论了几种研究趋势。
Natural Language Generation (NLG) has made great progress in recent years due to the development of deep learning techniques such as pre-trained language models. This advancement has resulted in more fluent, coherent and even properties controllable (e.g. stylistic, sentiment, length etc.) generation, naturally leading to development in downstream tasks such as abstractive summarization, dialogue generation, machine translation, and data-to-text generation. However, the faithfulness problem that the generated text usually contains unfaithful or non-factual information has become the biggest challenge, which makes the performance of text generation unsatisfactory for practical applications in many real-world scenarios. Many studies on analysis, evaluation, and optimization methods for faithfulness problems have been proposed for various tasks, but have not been organized, compared and discussed in a combined manner. In this survey, we provide a systematic overview of the research progress on the faithfulness problem of NLG, including problem analysis, evaluation metrics and optimization methods. We organize the evaluation and optimization methods for different tasks into a unified taxonomy to facilitate comparison and learning across tasks. Several research trends are discussed further.
