Learning Sentiment Memories for Sentiment Modification without Parallel Data
Table 1: Performance of the proposed method and state-of-the-art systems.
['Model', 'ACC', 'BLEU']
Both baseline models have low BLEU score but high accuracy, which indicates that they may be trapped in a situation that they simply output a sentence with the target sentiment regardless of the content. The main reason is that these methods using adversarial learning attempt to implicitly separate the emotional information from the context information in a sentence vector. However, without parallel data, it is difficult to achieve such a goal. Our proposed SMAE model takes advantage of self-attention mechanism and explicitly removes the emotional words, leading to a significant improvement of content preservation and the state-of-the-art performance in terms of both metrics.