AMR - TST : A bstract M eaning R epresentation-based Text Style Transfer (2023)

Abstract: Abstract Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. In this paper, we propose the AMR-TST, an AMR-based text style transfer (TST) technique. The AMR-TST converts the source text to an AMR graph and generates the transferred text based on the AMR graph modified by a TST policy named style rewriting. Our method combines both the explainability and diversity of explicit and implicit TST methods. The experiments show that the proposed method achieves state-of-the-art results compared with other baseline models in automatic and human evaluations. The generated transferred text in qualitative evaluation proves the AMR-TST have significant advantages in keeping semantic features and reducing hallucinations. To the best of our knowledge, this work is the first to apply the AMR method focusing on node-level features to the TST task.

## related work text style transfer aims to revise the specific styles or attributes of the source texts while preserving the non-stylistic content @xcite . due to the lack of a parallel corpus, implicit and explicit unsupervised methods are the mainstream techniques for this task @xcite . the implicit methods enable the model to map the text to the latent space through the encoder to obtain the disentangled representation, separate the content and attributes, and perform attribute transfer. hu et al. @xcite combined vae with an attribute discriminator to control the attributes of the target sentence through structured encoding and provided feedback to optimize the generated sentence through the attribute discriminator. luo et al. @xcite regarded the mapping between the source and target text as a dual learning task and achieved style transfer by setting reward mechanisms of style accuracy and content retention in reinforcement learning. considering the fact that the style features of a sentence are usually reflected in unique phrases, explicit methods can achieve explainable text style transfer by only changing the stylistic words or phrases while retaining the style-independent parts. li et al. @xcite first proposed the drg framework, which achieves style transfer by deleting style words from texts, retrieving target texts similar to the source content, and generating target texts by combining target style features. since sentiment words with higher attention weights in sentiment classification, xu et al. @xcite used an attention-based classifier to separate content and sentiment words for text style transfer. most research on amr focuses on amr parsing and generation, such as using graph neural networks @xcite and pre-training language models @xcite to improve performance. it is gratifying to note that more recent research integrates amr with downstream nlg tasks. t-star @xcite is a contemporaneous work with us, which transfers the text's style by training style-specific amr encoder and decoder. in comparison, the amr-tst achieves the text style transfer with a simple and reliable style rewriting algorithm, avoiding potential semantic bias during the complex retraining process. kapanipathi et al. @xcite introduced the amr into knowledge base question answering (kbqa) for delegating the complexity of understanding natural language questions to amr parsers, which relieves the pressure of labelling large amounts of data in kbqa. all this research proves the potential of amr to power various nlp tasks. ## ablation study in this section, we conduct the ablation study to verify the positive impact of the proposed style rewriting algorithm. specifically, we evaluate the performance of models without the style words expander ("w/o se") and style gating unit ("w/o sg") components, as well as the model that has neither of these components ("w/o sr"). the results of this study are shown in the results show that the proposed amr-tst achieves the best results in gm compared to the models without the style rewriting components, demonstrating the positive impact of the style rewriting components on the model performance. specifically, for the ac f and ac b metrics, there is a gradual decrease from w/o se, w/o sg, to w/o sr. this phenomenon demonstrates that the style rewriting algorithm and its style words expander and style gating unit components actively promote the style transfer from the source text to the target text. in contrast, among the metrics bleu s , bleu r , rl s , and rl r that reflect content preservation, the w/o sr model has better results, proving the style rewriting algorithm can promote the model to rewrite the source text to the greatest extent according to the target style, resulting in reduced content consistency between the transferred text and the source text. in addition, the results of w/o sr model on the bleu s and rl s metrics also prove that amr can better reconstruct source text while standardizing semantic representation. amr-tst achieves competitive results in the ppl metric, which proves that the target style words generated by the style rewriting algorithm are in line with the natural expression habits of humans. ## conclusion this paper proposes the amr-tst, an abstract meaning representation-based text style transfer 4237 i ve got a chance to make a living on a small scale and i am now a millionaire. amr-tst there is nothing good to be said at all for this unattractive innovation. i've concluded that i'm conserving my money. future research can expand the scope of our method to other text style transfer tasks by designing more flexible style rewriting components. for instance, adding or removing specific stylistic nodes or structures in the amr graph can result in a more diverse and controllable text style transfer.