| 1 |
Learning to Recognize Dialect Features ...
|
|
|
|
Abstract:
Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.184/ Abstract: Building NLP systems that serve everyone requires accounting for dialect differences. But dialects are not monolithic entities: rather, distinctions between and within dialects are captured by the presence, absence, and frequency of dozens of dialect features in speech and text, such as the deletion of the copula in "He {} running". In this paper, we introduce the task of dialect feature detection, and present two multitask learning approaches, both based on pretrained transformers. For most dialects, large-scale annotated corpora for these features are unavailable, making it difficult to train recognizers. We train our models on a small number of minimal pairs, building on how linguists typically define dialect features. Evaluation on a test set of 22 dialect features of Indian English demonstrates that these models learn to recognize many features with high accuracy, and that a few minimal pairs can be as ...
|
|
Keyword:
Artificial Intelligence; Computer Science and Engineering; Intelligent System; Natural Language Processing
|
|
URL: https://underline.io/lecture/19996-learning-to-recognize-dialect-features
https://dx.doi.org/10.48448/0kek-4w70
|
|
BASE
|
|
Hide details
|
|
|
|
|
