Paraphrase Based Data Augmentation For Chinese-English Medical Machine Translation
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摘要: 医疗机器翻译对于跨境医疗、医疗文献翻译等应用具有重要价值。汉英神经机器翻译依靠深度学习强大的建模能力和大规模双语平行数据取得了长足的进步。神经机器翻译通常依赖于大规模的平行句对训练翻译模型。目前，汉英翻译数据主要以新闻、政策等领域数据为主，缺少医疗领域的数据，导致医疗领域的汉英机器翻译效果不佳。针对医疗垂直领域机器翻译训练数据不足的问题，该文提出利用复述生成技术对汉英医疗机器翻译数据进行增广，扩大汉英机器翻译的规模。通过多种主流的神经机器翻译模型的实验结果表明，通过复述生成对数据进行增广可以有效地提升机器翻译的性能，在RNNSearch, Transformer等多个主流模型上均取得了6个点以上的BLEU值提升，验证了复述增广方法对领域机器翻译的有效性。同时，基于MT5等大规模预训练语言模型可以进一步地提升机器翻译的性能。
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关键词:
- 神经机器翻译 /
- 汉英翻译 /
- 复述生成 /
- 数据增广 /
- 大规模预训练语言模型
Abstract: Medical machine translation is of great value for cross-border medical translation. Chinese to English neural machine translation has made great progress based on deep learning, powerful modeling ability and large-scale bilingual parallel data. Neural machine translation relies usually on large-scale parallel sentence pairs to train translation models. At present, Chinese-English translation data are mainly in the fields of news, policy and so on. Due to the lack of parallel data in the medical field, the performance of Chinese to English machine translation in the medical field is not compromising. To reduce the size of parallel data for training medical machine translation models, this paper proposes a paraphrase based data augmentation mechanism. The experimental results on a variety of neural machine translation models show that data augmentation through paraphrase augmentation can effectively improve the performance of medical machine translation, and has achieved consistency improvements on mainstream models such as RNNSearch and Transformers, which verifies the effectiveness of paraphrase augmentation method for domain machine translation. Meanwhile, the medical machine translation performances could be further improved based on large-scale pre-training language model, such as MT5. -
表 2 模型参数设置
机器翻译模型 参数 参数值 Seq2Seq Embedding size 300 Beam size 50 Batch size 64 Sentence length 256 Learning rate 0.01 Optimizer Adam RNN cell LSTM Drouput 0.2 RNNSearch Embedding size 300 Beam size 50 Batch size 64 Sentence length 256 Learning rate 0.01 Optimizer Adam RNN cell LSTM Drouput 0.2 Transformer Embedding size 300 Beam size 50 Batch size 64 Sentence length 256 Learning rate 0.1 Optimizer Adam RNN cell LSTM Drouput 0.2 Num head 8 
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表 3 汉英医疗机器翻译结果
机器翻译模型 数据增广模型 BLEU 提升(%) Seq2Seq – 31.99 – WordRep 32.12 0.41 BiLSTM-para 33.45 4.56 Transformer-para 35.23 10.13 Bert-para 35.28 10.28 MT5-para 35.74 11.72 RNNSearch – 41.28 – WordRep 40.98 -0.73 BiLSTM-para 43.25 4.77 Transformer-para 44.12 6.88 Bert-para 44.67 8.21 MT5-para 44.97 8.94 Transformer – 48.21 – WordRep 48.29 0.17 BiLSTM-para 49.86 3.42 Transformer-para 51.32 6.45 Bert-para 51.36 6.53 MT5-para 51.97 7.80
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表 4 汉英医疗机器翻译例子
汉语句子 患者男，31岁，因中重度反复头痛18天入院，表现为枕部至双额部逐渐发作，呈搏动性，发作持续超过4h，并持续加重。 百度 The patient, a 31 year old male, was hospitalized for 18 days due to moderate and severe recurrent headache. He showed a gradual attack from the occipital part to the double frontal part, which was pulsatile. The attack lasted for more than 4 hours and continued to worsen. 谷歌 A 31-year-old male patient was admitted to the hospital for 18 days with moderate to severe recurrent headaches. The manifestations were pulsatile attacks from the occiput to the forehead. The attacks lasted more than 4 hours and gradually worsened. 本文 A 31-year-old man was admitted with an 18-day history of a moderate to severe recurrent headache, presenting gradual onset from occipital to bifrontal regions, pulsatile, in episodes lasting beyond four hours, and progressive worsening.
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