论文标题
学习从未标记的数据中推断：一种半监督的学习方法，用于强大的自然语言推断
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference
论文作者
论文摘要
自然语言推论（NLI）或认可文本元素（RTE）旨在预测一对句子（前提和假设）之间的关系，因为它需要，矛盾或语义独立性。尽管近年来，深度学习模型显示了NLI的有希望的表现，但它们依靠大规模昂贵的人类注销数据集。半监督学习（SSL）是一种流行的技术，可通过利用未标记的数据进行培训来降低人类注释的依赖。 However, despite its substantial success on single sentence classification tasks where the challenge in making use of unlabeled data is to assign "good enough" pseudo-labels, for NLI tasks, the nature of unlabeled data is more complex: one of the sentences in the pair (usually the hypothesis) along with the class label are missing from the data and require human annotations, which makes SSL for NLI more challenging.在本文中，我们提出了一种新颖的方法，将我们使用条件语言模型的NLI中的未标记数据合并到SSL中，BART为未标记的句子（用作前提）生成假设。我们的实验表明，我们的SSL框架成功利用了未标记的数据，并显着提高了低资源设置中四个NLI数据集的性能。我们在：https：//github.com/msadat3/ssl_for_nli上发布我们的代码。
Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) aims at predicting the relation between a pair of sentences (premise and hypothesis) as entailment, contradiction or semantic independence. Although deep learning models have shown promising performance for NLI in recent years, they rely on large scale expensive human-annotated datasets. Semi-supervised learning (SSL) is a popular technique for reducing the reliance on human annotation by leveraging unlabeled data for training. However, despite its substantial success on single sentence classification tasks where the challenge in making use of unlabeled data is to assign "good enough" pseudo-labels, for NLI tasks, the nature of unlabeled data is more complex: one of the sentences in the pair (usually the hypothesis) along with the class label are missing from the data and require human annotations, which makes SSL for NLI more challenging. In this paper, we propose a novel way to incorporate unlabeled data in SSL for NLI where we use a conditional language model, BART to generate the hypotheses for the unlabeled sentences (used as premises). Our experiments show that our SSL framework successfully exploits unlabeled data and substantially improves the performance of four NLI datasets in low-resource settings. We release our code at: https://github.com/msadat3/SSL_for_NLI.
