Politeness Transfer: A Tag and Generate Approach (2004.14257v2)

Abstract: This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and generate pipeline that identifies stylistic attributes and subsequently generates a sentence in the target style while preserving most of the source content. For politeness as well as five other transfer tasks, our model outperforms the state-of-the-art methods on automatic metrics for content preservation, with a comparable or better performance on style transfer accuracy. Additionally, our model surpasses existing methods on human evaluations for grammaticality, meaning preservation and transfer accuracy across all the six style transfer tasks. The data and code is located at https://github.com/tag-and-generate.

• The paper introduces a novel politeness transfer task using a comprehensive dataset of 1.39M instances from the Enron email corpus.
• The method employs an innovative 'tag and generate' pipeline that identifies stylistic elements and generates contextually appropriate polite variations.
• Results demonstrate that the approach preserves core content and outperforms state-of-the-art models in both automatic and human evaluations.

An Expert Overview of "Politeness Transfer: A Tag and Generate Approach"

In the contemporary landscape of natural language processing, effective text style transfer is a critical task that involves changing the stylistic characteristics of a piece of text while preserving its core semantic content. The paper "Politeness Transfer: A Tag and Generate Approach" by Madaan et al. advances this domain by introducing a novel task of politeness transfer, which uniquely targets the conversion of non-polite sentences into polite ones. This is particularly salient in digital communication contexts where the subtleties of politeness and social norms play a significant role in both personal and organizational discourse.

Problem Statement and Contributions

Politeness, unlike other stylistic attributes such as sentiment or formality, is multifaceted and varies significantly across social and cultural contexts, thus complicating its inclusion as a distinct text style transfer task. The paper's primary contribution is the formal definition of politeness transfer, coupled with the construction of a voluminous dataset consisting of approximately 1.39 million instances labeled for politeness. This dataset is derived primarily from the Enron email corpus, notable for its applicability to formal American English conversations.

This work also introduces a "tag and generate" pipeline structured into two main components: a tagger that identifies and marks stylistic phrases in a source sentence, and a generator that utilizes these tagged markers to produce an output sentence with the desired style (politeness in this case). Notably, the system emphasizes content preservation while ensuring style transfer, outperforming current state-of-the-art methods across six style transfer tasks, as indicated by both automatic and human evaluations.

Methodological Advances

Central to this paper is the innovative methodological approach. The tagging mechanism implemented can adaptively identify points in a sentence where polite or non-polite elements exist or could be inserted. The generation component then fills these markers with contextually appropriate stylistic phrases, effectively capturing the nuanced character of politeness. This is fundamentally distinct from strategies focusing purely on removal or replacement of identifiable features, which overlook sentences lacking explicit stylistic markers.

The authors report that their model surpasses state-of-the-art methods in terms of automatic metrics for content retention and style transfer accuracy. Human evaluations corroborate these findings, exhibiting improvements in grammaticality, content preservation, and the fidelity of style transfer compared to extant solutions. The interpretability afforded by the intermediate outputs of tagged sentences further distinguishes this approach from latent variable models.

Implications and Future Directions

The potential implications of this research are vast. Practically, this model can significantly enhance automatic editing tools to refine digital communication in corporate and intercultural contexts, where nuanced politeness is often pivotal. Theoretically, this task challenges researchers to explore the intersection of linguistics and machine learning, particularly regarding the representation and disentanglement of complex stylistic features.

Going forward, this task could be expanded by considering multilingual politeness transfer, acknowledging the diverse manifestations of politeness across languages. Moreover, the application of this polite style transfer framework to conversational agents could improve user satisfaction in interactions. Further research could also explore the adaptability of the model across different communication channels and domains.

In summation, "Politeness Transfer: A Tag and Generate Approach" not only broadens the scope of text style transfer tasks but also provides robust solutions applicable to real-world communication challenges. The results suggest promising avenues for enhancing computational systems with a more sophisticated understanding of human communication nuances.