2 research outputs found
A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution
Aspect-based sentiment classification (ASC) is an important task in
fine-grained sentiment analysis.~Deep supervised ASC approaches typically model
this task as a pair-wise classification task that takes an aspect and a
sentence containing the aspect and outputs the polarity of the aspect in that
sentence. However, we discovered that many existing approaches fail to learn an
effective ASC classifier but more like a sentence-level sentiment classifier
because they have difficulty to handle sentences with different polarities for
different aspects.~This paper first demonstrates this problem using several
state-of-the-art ASC models. It then proposes a novel and general adaptive
re-weighting (ARW) scheme to adjust the training to dramatically improve ASC
for such complex sentences. Experimental results show that the proposed
framework is effective \footnote{The dataset and code are available at
\url{https://github.com/howardhsu/ASC_failure}.}
SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics
We propose SentiBERT, a variant of BERT that effectively captures
compositional sentiment semantics. The model incorporates contextualized
representation with binary constituency parse tree to capture semantic
composition. Comprehensive experiments demonstrate that SentiBERT achieves
competitive performance on phrase-level sentiment classification. We further
demonstrate that the sentiment composition learned from the phrase-level
annotations on SST can be transferred to other sentiment analysis tasks as well
as related tasks, such as emotion classification tasks. Moreover, we conduct
ablation studies and design visualization methods to understand SentiBERT. We
show that SentiBERT is better than baseline approaches in capturing negation
and the contrastive relation and model the compositional sentiment semantics.Comment: ACL-202