Our work addresses the problem of unsupervised Aspect Category Detection
using a small set of seed words. Recent works have focused on learning
embedding spaces for seed words and sentences to establish similarities between
sentences and aspects. However, aspect representations are limited by the
quality of initial seed words, and model performances are compromised by noise.
To mitigate this limitation, we propose a simple framework that automatically
enhances the quality of initial seed words and selects high-quality sentences
for training instead of using the entire dataset. Our main concepts are to add
a number of seed words to the initial set and to treat the task of noise
resolution as a task of augmenting data for a low-resource task. In addition,
we jointly train Aspect Category Detection with Aspect Term Extraction and
Aspect Term Polarity to further enhance performance. This approach facilitates
shared representation learning, allowing Aspect Category Detection to benefit
from the additional guidance offered by other tasks. Extensive experiments
demonstrate that our framework surpasses strong baselines on standard datasets.

我们的工作致力于使用一小组种子词进行无监督的方面类别检测。我们提出了一个简单的框架，通过自动增强种子词的质量和选择高质量的句子进行训练，来缓解初始种子词质量和噪声对模型性能的影响。同时，我们对方面类别检测、方面术语提取和方面术语极性联合进行训练，以进一步提高性能。大量实验证明我们的框架在标准数据集上超过了强基准结果。

无监督方面类别检测的自我增强多任务框架

A Self-enhancement Multitask Framework for Unsupervised Aspect Category  Detection

Cross-lingual text classification alleviates the need for manually labeled
documents in a target language by leveraging labeled documents from other
languages. Existing approaches for transferring supervision across languages
require expensive cross-lingual resources, such as parallel corpora, while less
expensive cross-lingual representation learning approaches train classifiers
without target labeled documents. In this work, we propose a cross-lingual
teacher-student method, CLTS, that generates "weak" supervision in the target
language using minimal cross-lingual resources, in the form of a small number
of word translations. Given a limited translation budget, CLTS extracts and
transfers only the most important task-specific seed words across languages and
initializes a teacher classifier based on the translated seed words. Then, CLTS
iteratively trains a more powerful student that also exploits the context of
the seed words in unlabeled target documents and outperforms the teacher. CLTS
is simple and surprisingly effective in 18 diverse languages: by transferring
just 20 seed words, even a bag-of-words logistic regression student outperforms
state-of-the-art cross-lingual methods (e.g., based on multilingual BERT).
Moreover, CLTS can accommodate any type of student classifier: leveraging a
monolingual BERT student leads to further improvements and outperforms even
more expensive approaches by up to 12% in accuracy. Finally, CLTS addresses
emerging tasks in low-resource languages using just a small number of word
translations.

本文提出了一种跨语言教师 - 学生方法 (CLTS)，通过最小化跨语言资源 (即少量单词翻译)，将跨语言监督信息扩展到目标语言中，采用有限的翻译预算提取并转移最重要的任务特定的种子词，然后使用种子词初始化老师分类器，进而循环训练一个更强大的学生分类器，利用目标文本中种子词的上下文，从而解决低资源语言中新兴的任务。