论文标题
文本跨度表示形式的交叉任务分析
A Cross-Task Analysis of Text Span Representations
论文作者
论文摘要
许多自然语言处理（NLP）任务涉及对文本跨度进行推理，包括问答，实体识别和核心解决方案。尽管广泛的研究集中在代表单词和句子的功能体系结构上，但在表示句子中代表任意跨越文本的工作较少。在本文中，我们使用六个任务中的八个验证语言表示模型对六种跨度表示方法进行了全面的经验评估，其中包括我们介绍的两个任务。我们发现，尽管某些简单的跨度表示在整个任务之间相当可靠，但总的来说，最佳跨度表示会因任务而变化，并且在单个任务的不同方面也可能会有所不同。我们还发现，与固定的编码器相比，与固定的编码器相比，跨度表示的选择具有更大的影响。
Many natural language processing (NLP) tasks involve reasoning with textual spans, including question answering, entity recognition, and coreference resolution. While extensive research has focused on functional architectures for representing words and sentences, there is less work on representing arbitrary spans of text within sentences. In this paper, we conduct a comprehensive empirical evaluation of six span representation methods using eight pretrained language representation models across six tasks, including two tasks that we introduce. We find that, although some simple span representations are fairly reliable across tasks, in general the optimal span representation varies by task, and can also vary within different facets of individual tasks. We also find that the choice of span representation has a bigger impact with a fixed pretrained encoder than with a fine-tuned encoder.
