利用问答蓝图实现多语言表格到文本生成中可归因性的挑战
The Challenge of Achieving Attributability in Multilingual Table-to-Text Generation with Question-Answer Blueprints
AI总结：
本文扩展含QA蓝图的多语言TaTA数据集并微调序列到序列模型，发现蓝图仅改善英文设置，未能提升多语言表格到文本生成的可归因性。
摘要：
多语言自然语言生成（NLG）因低资源语言缺乏训练数据而颇具挑战。然而，部分低资源语言在全球拥有多达数千万使用者，因此改进面向这些语言的NLG工具十分重要。表格到文本NLG是衡量模型推理能力的出色方式，但在多语言场景中极具挑战性。系统输出往往无法归因于或忠实于源表格中的数据。诸如问答（QA）蓝图之类的中间规划技术已被证明能够提升摘要任务的可归因性。本工作探究QA蓝图能否使多语言表格到文本输出更能归因于输入表格。本文为具有挑战性的、包含非洲语言的多语言表格到文本数据集TaTA扩展了QA蓝图。随后，研究人员在该数据集上分别以使用蓝图和不使用蓝图的方式微调序列到序列语言模型。结果表明，QA蓝图提升了仅在英文样本上微调并评估的模型性能，但在多语言场景中并未展现出收益。这是由于在生成训练数据时，将蓝图从英文机器翻译为目标语言存在不准确之处，且模型未能紧密依赖其生成的蓝图。本文还对这一挑战背后的原因进行了深入分析。
英文摘要：
Multilingual Natural Language Generation (NLG) is challenging due to the lack of training data for low-resource languages. However, some low-resource languages have up to tens of millions of speakers globally, making it important to improve NLG tools for them. Table-to-Text NLG is an excellent measure of models' reasoning abilities but is very challenging in the multilingual setting. System outputs are often not attributable, or faithful, to the data in the source table. Intermediate planning techniques like Question-Answer (QA) blueprints have been shown to improve attributability on summarisation tasks. This work explores whether QA blueprints make multilingual Table-to-Text outputs more attributable to the input tables. This paper extends the challenging multilingual Table-to-Text dataset, TaTA, which includes African languages, with QA blueprints. Sequence-to-sequence language models are then finetuned on this dataset, with and without blueprints. Results show that QA blueprints improve performance for models finetuned and evaluated only on English examples, but do not demonstrate gains in the multilingual setting. This is due to inaccuracies in machine translating the blueprints from English into target languages when generating the training data, and models failing to rely closely on the blueprints they generate. An in-depth analysis is conducted on why this is challenging.
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。
