用机器翻译循环一致性提升低资源语言共指消解效果
Multilingual Coreference Resolution via Cycle-Consistent Machine Translation
- 通过英译目标语言再回译，验证翻译质量
- 基于双语相似度加权训练样本，提升模型性能
- 在4种低资源语言上实现有效共指消解
共指消解是自然语言处理的核心任务，广泛应用于机器翻译、问答系统、文档摘要等场景。尽管英语上的研究已较成熟，但其他语言尤其是低资源语言的研究仍较少。为此，本文提出一种新型共指消解流程，利用英文到目标低资源语言的机器翻译生成或扩充训练数据。为自动评估翻译样本质量，采用回译方法，并通过BERT模型隐空间中的余弦相似度比较回译文本与原始英文文本。相似度得分被融入损失函数，以根据机器翻译的循环一致性对训练样本进行加权。在四种低资源语言上的大量实验表明，该方法显著提升了共指消解性能，且使此前无标注语料的语言实现了准确的共指消解。
原文摘要 · Abstract (English)
Coreference resolution is a core NLP task, having a broad range of downstream applications, e.g.~machine translation, question answering, document summarization, etc. While the task is well-studied in English, comparatively less attention is dedicated to coreference resolution in other languages, especially low-resource ones. To mitigate this gap, we propose a novel coreference resolution pipeline that harnesses machine translation (MT) from English to a target low-resource language, to generate or expand training data. To automatically validate the quality of the translated samples, we back-translate the samples and assess the similarity with the original English samples via cosine similarity in the latent space of a BERT model. The resulting similarity scores are integrated into the loss function to weight training samples according to their MT cycle consistency. Extensive experiments on four low-resource languages show that our pipeline brings significant performance gains in coreference resolution. Moreover, our pipeline enables accurate coreference resolution in languages where no previous corpora were available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。