A Self-enhancement Multitask Framework for Unsupervised Aspect Category
  Detection
Thi-Nhung Nguyen Hoang Ngo Kiem-Hieu Nguyen Tuan-Dung Cao
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.