阿拉伯语问句的深度语境化成对语义相似度
Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions
摘要：
问题语义相似性是一个具有挑战性和活跃的研究问题，在许多NLP应用中非常有用，例如在Quora等社区问答平台中检测重复问题。阿拉伯语被认为是一种资源不足的语言，有许多方言，并且有丰富的形态。这些挑战加在一起，使得在阿拉伯语中识别语义相似的问题变得更加困难。在本文中，我们介绍了一种新的方法来解决这个问题，并在两个基准上进行测试;一个是现代标准阿拉伯语（MSA），另一个是24种主要的阿拉伯语方言。我们能够证明，我们的新系统在MSA基准测试中获得了93%的F1分数，在辩证测试中获得了82%的F1分数，优于最先进的方法。这是通过利用在包含MSA和辩证句的文本语料库上训练的上下文化单词表示（ELMo嵌入）来实现的。这与成对细粒度相似性层相结合，有助于我们的问题到问题相似性模型在仅基于问题到问题MSA数据进行训练的同时，对不同方言进行概括预测。
英文摘要：
Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under-resourced language, has many dialects, and rich in morphology. Combined together, these challenges make identifying semantically similar questions in Arabic even more difficult. In this paper, we introduce a novel approach to tackle this problem, and test it on two benchmarks; one for Modern Standard Arabic (MSA), and another for the 24 major Arabic dialects. We are able to show that our new system outperforms state-of-the-art approaches by achieving 93% F1-score on the MSA benchmark and 82% on the dialectical one. This is achieved by utilizing contextualized word representations (ELMo embeddings) trained on a text corpus containing MSA and dialectic sentences. This in combination with a pairwise fine-grained similarity layer, helps our question-to-question similarity model to generalize predictions on different dialects while being trained only on question-to-question MSA data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
