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This paper presents a Semantic Attribute Modulation (SAM) for languagemodeling and style variation. The semantic attribute modulation includesvarious document attributes, such as titles, authors, and document categories.We consider two types of attributes, (title attributes and categoryattributes), and a flexible attribute selection scheme by automatically scoringthem via an attribute attention mechanism. The semantic attributes are embeddedinto the hidden semantic space as the generation inputs. With the attributesproperly harnessed, our proposed SAM can generate interpretable texts withregard to the input attributes. Qualitative analysis, including word semanticanalysis and attention values, shows the interpretability of SAM. On severaltypical text datasets, we empirically demonstrate the superiority of theSemantic Attribute Modulated language model with different combinations ofdocument attributes. Moreover, we present a style variation for the lyricgeneration using SAM, which shows a strong connection between the stylevariation and the semantic attributes.

SAM: Semantic Attribute Modulation for Language Modeling and Style Variation