SemEval-2021上的HLE-UPC任务5：用于有毒跨度检测的多深度DistilBERT
HLE-UPC at SemEval-2021 Task 5: Multi-Depth DistilBERT for Toxic Spans Detection
摘要：
本文介绍了我们提交的SemEval-2021任务5：有毒跨度检测。此任务的目的是检测使文本有毒的跨度，这是一项复杂的工作，原因有几个。首先，由于毒性的内在主观性，其次，由于毒性并不总是来自侮辱或冒犯等单个词，但有时来自由单个词组成的整体表达，这些词可能不是有毒的。遵循关注单个单词和多个单词表达的想法，我们研究了使用多深度DistilBERT模型的影响，该模型使用来自不同层的嵌入来估计最终的每个令牌毒性。我们的定量结果表明，使用来自多个深度的信息可以提高模型的性能。最后，我们还定性地分析了我们的最佳模型。
英文摘要：
This paper presents our submission to SemEval-2021 Task 5: Toxic Spans Detection. The purpose of this task is to detect the spans that make a text toxic, which is a complex labour for several reasons. Firstly, because of the intrinsic subjectivity of toxicity, and secondly, due to toxicity not always coming from single words like insults or offends, but sometimes from whole expressions formed by words that may not be toxic individually. Following this idea of focusing on both single words and multi-word expressions, we study the impact of using a multi-depth DistilBERT model, which uses embeddings from different layers to estimate the final per-token toxicity. Our quantitative results show that using information from multiple depths boosts the performance of the model. Finally, we also analyze our best model qualitatively.
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。
