Automatic Arabic Dialect Identification (ADI) of text has gained great
popularity since it was introduced in the early 2010s. Multiple datasets were
developed, and yearly shared tasks have been running since 2018. However, ADI
systems are reported to fail in distinguishing between the micro-dialects of
Arabic. We argue that the currently adopted framing of the ADI task as a
single-label classification problem is one of the main reasons for that. We
highlight the limitation of the incompleteness of the Dialect labels and
demonstrate how it impacts the evaluation of ADI systems. A manual error
analysis for the predictions of an ADI, performed by 7 native speakers of
different Arabic dialects, revealed that $\approx$ 66% of the validated errors
are not true errors. Consequently, we propose framing ADI as a multi-label
classification task and give recommendations for designing new ADI datasets.

Automatic Arabic Dialect Identification (ADI) 系统无法准确区分阿拉伯语的微方言，我们建议将 ADI 任务构建为多标签分类任务并提供设计新的 ADI 数据集的建议。

阿拉伯方言识别审视：单标签分类的局限性

Arabic Dialect Identification under Scrutiny: Limitations of  Single-label Classification

Product attribute value extraction plays an important role for many
real-world applications in e-Commerce such as product search and
recommendation. Previous methods treat it as a sequence labeling task that
needs more annotation for position of values in the product text. This limits
their application to real-world scenario in which only attribute values are
weakly-annotated for each product without their position. Moreover, these
methods only use product text (i.e., product title and description) and do not
consider the semantic connection between the multiple attribute values of a
given product and its text, which can help attribute value extraction. In this
paper, we reformulate this task as a multi-label classification task that can
be applied for real-world scenario in which only annotation of attribute values
is available to train models (i.e., annotation of positional information of
attribute values is not available). We propose a classification model with
semantic matching and negative label sampling for attribute value extraction.
Semantic matching aims to capture semantic interactions between attribute
values of a given product and its text. Negative label sampling aims to enhance
the model's ability of distinguishing similar values belonging to the same
attribute. Experimental results on three subsets of a large real-world
e-Commerce dataset demonstrate the effectiveness and superiority of our
proposed model.

本文将产品属性值的提取任务重新定义为一个多标签分类任务，并提出了一种带有语义匹配和负标签采样的分类模型，实验证明该模型在真实世界的电子商务数据集中表现出有效性和优越性。