研究意大利方言语音识别性能差异，发现越接近标准语的方言表现越好。
Dialetto, ma Quanto Dialetto? Transcribing and Evaluating Dialects on a Continuum
- 用地理与语言相似性分析方言内部差异
- 发现方言识别准确率与标准语相似度相关系数达-0.5
- 可利用地理位置预测未见地点的零样本性能
自然语言处理中对方言的兴趣日益增长，但多数研究仍将方言视为离散类别。例如，英语变体研究常将印度英语或非裔美国黑人口语英语当作同质整体（Faisal et al., 2024；Ziems et al., 2023），然而同一变体内部仍存在显著差异。本文研究意大利方言内部差异，测量其语音识别性能，并实证观察到明显的地理性能差异。该差异与最高性能方言的语言相似度呈显著负相关（相关系数-0.5）。通过对比方言计量方法，发现性能差异源于模型对更接近标准语的方言存在偏倚。此外，借助地统计学方法预测未见地点的零样本性能，结果表明引入地理信息可显著提升预测效果，说明性能分布具有地理结构。
原文摘要 · Abstract (English)
There is increasing interest in looking at dialects in NLP. However, most work to date still treats dialects as discrete categories. For instance, evaluative work in variation-oriented NLP for English often works with Indian English or African-American Venacular English as homogeneous categories (Faisal et al., 2024; Ziems et al., 2023), yet even within one variety there is substantial variation. We examine within-dialect variation and show that performance critically varies within categories. We measure speech-to-text performance on Italian dialects, and empirically observe a geographical performance disparity. This disparity correlates substantially (-0.5) with linguistic similarity to the highest performing dialect variety. We cross-examine our results against dialectometry methods, and interpret the performance disparity to be due to a bias towards dialects that are more similar to the standard variety in the speech-to-text model examined. We additionally leverage geostatistical methods to predict zero-shot performance at unseen sites, and find the incorporation of geographical information to substantially improve prediction performance, indicating there to be geographical structure in the performance distribution.
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