Code-mixing and script-mixing are prevalent across online social networks and
multilingual societies. However, a user's preference toward code-mixing depends
on the socioeconomic status, demographics of the user, and the local context,
which existing generative models mostly ignore while generating code-mixed
texts. In this work, we make a pioneering attempt to develop a persona-aware
generative model to generate texts resembling real-life code-mixed texts of
individuals. We propose a Persona-aware Generative Model for Code-mixed
Generation, PARADOX, a novel Transformer-based encoder-decoder model that
encodes an utterance conditioned on a user's persona and generates code-mixed
texts without monolingual reference data. We propose an alignment module that
re-calibrates the generated sequence to resemble real-life code-mixed texts.
PARADOX generates code-mixed texts that are semantically more meaningful and
linguistically more valid. To evaluate the personification capabilities of
PARADOX, we propose four new metrics -- CM BLEU, CM Rouge-1, CM Rouge-L and CM
KS. On average, PARADOX achieves 1.6 points better CM BLEU, 47% better
perplexity and 32% better semantic coherence than the non-persona-based
counterparts.

通过发展一种基于个人意识的生成模型，该研究试图生成类似于现实生活中个体的混合语言文本，并且提出了一个重新校准生成的序列以模拟真实混合语言文本的对齐模块，以实现生成的混合语言文本在语义上更有意义和在语言上更有效。