One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment (2020)

Abstract: The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform’s activity. In human music comments, there exists high distinction and diverse perspectives for the same song. In other words, for a song, different comments stem from different musical perspectives. However, to date, this characteristic has not been considered well in research on automatic comment generation. The existing methods tend to generate common and meaningless comments. In this paper, we propose an effective multi-perspective strategy to enhance the diversity of the generated comments. The experiment results on two music comment datasets show that our proposed model can effectively generate a series of diverse music comments based on different perspectives, which outperforms state-of-the-art baselines by a substantial margin.

## introduction in recent years, neural networks have achieved great success in natural language generation (nlg), which can be applied in many real-world scenarios, such as poetry generation, dialogue generation, comment generation, and so on. music comment generation is a sub-task of nlg. high-quality comments can effectively increase the popularity of music and the activity of the platform @xcite . however, there is a gap between human comments and generated comments based on the existing model. the music comment generation task's existing models tend to generate general but meaningless comments, such as "it is nice". as an example, an excerpt from human music comments and automatically generated comments based on the seq2seq model is given in figure 1 . obviously, these human comments on the left are more attractive and diverse, benefiting from the creation with multiple musical perspectives: the listener's behavior, song melody, version of music, and style of lyrics. in human music comments, there exists excellent distinction and diverse perspectives for the same music content. there goes a saying that there are a thousand hamlets in a thousand people's eyes. as a result, the music comment generation is typically regarded as a one-to-many generation task. nowadays, researchers have noticed such problems and tried to solve them via multiple methods in dialogue generation. some of them have utilized topics, keywords, meta-words, and other information during the generation process to improve performance @xcite @xcite . some researchers try to optimize the decoding process @xcite or reorder the candidate sequences after the decoding @xcite . however, these approaches cannot significantly improve the model's performance on diversity. some methods model one-to-many relationships by multiple latent variables and conform to the dialogue generation scenario @xcite @xcite . they tried to add multiple latent mechanisms or multi-mapping mechanisms between the encoder and decoder of the seq2seq architecture. these one-to-many mapping modules can capture a variety of similar generation modes to a certain extent in dialogue generation. nevertheless, there is so much overlap of aspects between the text generated through these mapping modules, such as topics and description methods, which is more intolerable in comment generation than in dialogue generation. this paper aims to bridge the gap between human and machine comments via a multi-perspective mechanism. we define the different language styles, views or aspects of the human comment creation as the musical perspectives, such as emotional perspectives, content theme perspectives, lyrics style perspectives, etc. besides, compared with other scenarios, the distinction between various comments needs to be more significant in this task for the same input content. in detail, to better simulate human behaviors and generate more diverse comments, we construct an effective multi-perspective mechanism. the proposed model consists of a music sequence information encoder with a multi-perspective extraction mechanism and a decoder to generate different comments. there is a significant difference between the training stage and the inference stage in our model. in the training stage, the model extracts the perspective that is more conform to the music content and selects the perspective suitable for optimization through the posterior information of the target comment. besides, we also design a distinction loss function between the perspective extraction components. our model maximizes the difference between the extraction components by minimizing the loss so that each component exerts a different effect. in the inference stage, the model can separately generate different comments based on different perspectives that are optimized in the previous training stage. our proposed model not only fits the situation of generating music comments but also dramatically reduces the duplication and redundancy between perspective components. finally, it can simulate multiple perspectives and generate diverse comments. overall, the contributions of this paper are listed as follows: ## conclusion this paper proposes an effective multi-perspective strategy to enhance automatic music comment and achieve that one comment is from one perspective. the strategy solves the problem of generating common but meaningless comments in the automatic music comment to some extent. we reform the one-tomany modeling mechanism and make it fit the situation of generating music comment. in conclusion, our model bridges the gap between human and machine comments via the multi-