Incorporating Stylistic Lexical Preferences in Generative Language Models

• URL: http://arxiv.org/abs/2010.11553v1
• Date: Thu, 22 Oct 2020 09:24:05 GMT
• Title: Incorporating Stylistic Lexical Preferences in Generative Language Models
• Authors: Hrituraj Singh, Gaurav Verma, Balaji Vasan Srinivasan
• Abstract summary: We present an approach to induce certain target-author attributes by incorporating continuous multi-dimensional lexical preferences of an author into generative language models. Our experiments demonstrate that the proposed approach can generate text that distinctively aligns with a given target author's lexical style.
• Score: 10.62343151429147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recent advances in language modeling have resulted in powerful generation models, their generation style remains implicitly dependent on the training data and can not emulate a specific target style. Leveraging the generative capabilities of a transformer-based language models, we present an approach to induce certain target-author attributes by incorporating continuous multi-dimensional lexical preferences of an author into generative language models. We introduce rewarding strategies in a reinforcement learning framework that encourages the use of words across multiple categorical dimensions, to varying extents. Our experiments demonstrate that the proposed approach can generate text that distinctively aligns with a given target author's lexical style. We conduct quantitative and qualitative comparisons with competitive and relevant baselines to illustrate the benefits of the proposed approach.

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