Adapting Language Models for Non-Parallel Author-Stylized Rewriting

Given the recent progress in language modeling using Transformer-based neural models and an active interest in generating stylized text, we present an approach to leverage the generalization capabilities of a language model to rewrite an input text in a target author's style. Our proposed approach adapts a pre-trained language model to generate author-stylized text by fine-tuning on the author-specific corpus using a denoising autoencoder (DAE) loss in a cascaded encoder-decoder framework. Optimizing over DAE loss allows our model to learn the nuances of an author's style without relying on parallel data, which has been a severe limitation of the previous related works in this space. To evaluate the efficacy of our approach, we propose a linguistically-motivated framework to quantify stylistic alignment of the generated text to the target author at lexical, syntactic and surface levels. The evaluation framework is both interpretable as it leads to several insights about the model, and self-contained as it does not rely on external classifiers, e.g. sentiment or formality classifiers. Qualitative and quantitative assessment indicates that the proposed approach rewrites the input text with better alignment to the target style while preserving the original content better than state-of-the-art baselines.Comment: Accepted for publication in Main Technical Track at AAAI 2

Lost and Found in Translation: How firms use anisomorphism to manage the institutional complexity of CSR

Prior research on the internationalization of firms from emerging countries has fruitfully invoked institutional theory to emphasize the legitimacy benefits that firms that obtain from showing isomorphism with international norms such as Corporate Social Responsibility (CSR). Without denying the intuitive appeal for these firms to communicate acceptance of CSR, we suggest that firms face a legitimacy trade-off, where the hoped-for legitimacy benefits of isomorphism must be weighed against other home-country institutional considerations. We advance and test this notion that firms will navigate this institutional complexity by engaging in anisomorphism, i.e., espousing general acceptance with international values but selective “translation” based on home country differences. We test our predictions by analyzing firms’ communication of CSR, using a unique dataset comprised of 245 firms observed over the period from 2000 to 2018. Consistent with our predictions, we find that firms from countries more reliant on natural resource extraction (e.g., mining and fossil fuel industries) de-emphasize the environmental component of CSR, and firms from more autocratic countries de-emphasize the human rights component of CSR. Additionally, and consistent with our presumption of firms’ weighing the international versus home-country legitimacy trade-off, we find that these main effects are sensitive to changes in firms’ levels of internationalization