Abstract
In this paper, we focus on the task of finegrained text sentiment transfer (FGST). This
task aims to revise an input sequence to satisfy
a given sentiment intensity, while preserving
the original semantic content. Different from
conventional sentiment transfer task that
only reverses the sentiment polarity (positive/negative) of text, the FTST task requires
more nuanced and fine-grained control of
sentiment. To remedy this, we propose a
novel Seq2SentiSeq model. Specifically,
the numeric sentiment intensity value is
incorporated into the decoder via a Gaussian
kernel layer to finely control the sentiment
intensity of the output. Moreover, to tackle
the problem of lacking parallel data, we
propose a cycle reinforcement learning
algorithm to guide the model training. In this
framework, the elaborately designed rewards
can balance both sentiment transformation
and content preservation, while not requiring
any ground truth output. Experimental results
show that our approach can outperform
existing methods by a large margin in both
automatic evaluation and human evaluation.
Our code and data, including outputs of
all baselines and our model are available
at https://github.com/luofuli/
Fine-grained-Sentiment-Transfer