用课程学习提升多语言数据生成在噪声数据下的表现
Curriculum Learning for Cross-Lingual Data-to-Text Generation With Noisy Data
- 按对齐得分排序样本，结合渐进式训练策略
- BLEU提升4分，生成忠实度和覆盖率平均提高5-15%
- 适合多语言文本生成与噪声数据场景的研究者
课程学习通过按特定顺序排列训练样本，已用于提升各类任务中的文本生成质量。在数据到文本生成（DTG）中，以往研究基于多种难度标准对单语数据进行排序。然而这些标准无法推广至跨语言场景，也未考虑噪声数据。本文探索了适用于带噪声数据的跨语言DTG的多种排序准则，并采用两种课程学习调度策略。结果表明，使用对齐得分作为排序依据并结合退火训练策略，可在11种印度语言和英语的两个数据集上，使BLEU分数最高提升4分，生成内容的忠实度和覆盖度平均提升5-15%。代码与数据已公开。
原文摘要 · Abstract (English)
Curriculum learning has been used to improve the quality of text generation systems by ordering the training samples according to a particular schedule in various tasks. In the context of data-to-text generation (DTG), previous studies used various difficulty criteria to order the training samples for monolingual DTG. These criteria, however, do not generalize to the crosslingual variant of the problem and do not account for noisy data. We explore multiple criteria that can be used for improving the performance of cross-lingual DTG systems with noisy data using two curriculum schedules. Using the alignment score criterion for ordering samples and an annealing schedule to train the model, we show increase in BLEU score by up to 4 points, and improvements in faithfulness and coverage of generations by 5-15% on average across 11 Indian languages and English in 2 separate datasets. We make code and data publicly available
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