角色之眼：通过高级标签看语言
Character Eyes: Seeing Language through Character-Level Taggers
摘要：
近年来，在自然语言处理任务中，词汇表级模型被广泛地用作封闭词汇表级词表征的补充和替代。在一种流行的体系结构中，字符级LSTM用于将令牌表示馈送到序列标记器中，预测诸如词性（POS）标记的令牌级注释。在这项工作中，我们从字符LSTM中的各个隐藏单元的角度来研究跨语言的POS标记器的行为。我们将这些单元的行为聚合成语言级别的指标，这些指标量化了标记者在具有不同形态属性的语言上面临的挑战，并确定了合成和词缀偏好与隐藏标记者层的紧急行为之间的联系。在一个比较实验中，我们展示了如何修改前向和后向隐藏单元之间的平衡影响模型的安排和性能，在这些类型的语言。
英文摘要：
Character-level models have been used extensively in recent years in NLP tasks as both supplements and replacements for closed-vocabulary token-level word representations. In one popular architecture, character-level LSTMs are used to feed token representations into a sequence tagger predicting token-level annotations such as part-of-speech (POS) tags. In this work, we examine the behavior of POS taggers across languages from the perspective of individual hidden units within the character LSTM. We aggregate the behavior of these units into language-level metrics which quantify the challenges that taggers face on languages with different morphological properties, and identify links between synthesis and affixation preference and emergent behavior of the hidden tagger layer. In a comparative experiment, we show how modifying the balance between forward and backward hidden units affects model arrangement and performance in these types of languages.
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。
