论文标题
多任务学习用于跨语言抽象摘要
Multi-Task Learning for Cross-Lingual Abstractive Summarization
论文作者
论文摘要
我们提出了一个多任务学习框架，用于跨语性的抽象摘要来增强培训数据。最近的研究构建了伪跨语性的抽象摘要数据，以训练其神经编码器描述器。同时，我们将现有的真实数据（例如翻译对和单语抽象摘要数据）引入培训中。我们提出的方法Transum将特殊令牌附加到输入句子的开头，以指示目标任务。特殊令牌使我们能够轻松地将真实数据纳入培训数据中。实验结果表明，与仅使用伪跨语言摘要数据训练的模型相比，Transum的性能更好。此外，我们在中文英语和阿拉伯语 - 英语抽象摘要上取得了最高的胭脂评分。此外，Transum对机器翻译也有积极影响。实验结果表明，Transum在中文英语，阿拉伯语 - 英语和英文 - 日本翻译数据集中提高了强大的基线，变压器的性能。
We present a multi-task learning framework for cross-lingual abstractive summarization to augment training data. Recent studies constructed pseudo cross-lingual abstractive summarization data to train their neural encoder-decoders. Meanwhile, we introduce existing genuine data such as translation pairs and monolingual abstractive summarization data into training. Our proposed method, Transum, attaches a special token to the beginning of the input sentence to indicate the target task. The special token enables us to incorporate the genuine data into the training data easily. The experimental results show that Transum achieves better performance than the model trained with only pseudo cross-lingual summarization data. In addition, we achieve the top ROUGE score on Chinese-English and Arabic-English abstractive summarization. Moreover, Transum also has a positive effect on machine translation. Experimental results indicate that Transum improves the performance from the strong baseline, Transformer, in Chinese-English, Arabic-English, and English-Japanese translation datasets.
