Style Transfer from Non-Parallel Text by Cross-Alignment (1705.09655v2)

Abstract: This paper focuses on style transfer on the basis of non-parallel text. This is an instance of a broad family of problems including machine translation, decipherment, and sentiment modification. The key challenge is to separate the content from other aspects such as style. We assume a shared latent content distribution across different text corpora, and propose a method that leverages refined alignment of latent representations to perform style transfer. The transferred sentences from one style should match example sentences from the other style as a population. We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.

• The paper presents a novel cross-alignment mechanism to disentangle content and style in non-parallel text.
• It employs an encoder-decoder architecture with adversarial training to ensure style-independent content representation.
• Experimental results show competitive sentiment accuracy (78.4%) and superior Bleu scores in decipherment and word order recovery tasks.

An Essay on the Paper "Style Transfer from Non-Parallel Text by Cross-Alignment"

Overview

The paper "Style Transfer from Non-Parallel Text by Cross-Alignment" by Shen, Lei, Barzilay, and Jaakkola addresses the challenge of performing style transfer on non-parallel text. Unlike tasks such as machine translation and text summarization which rely heavily on parallel corpora, style transfer often lacks such parallel data. This paper addresses this gap by proposing a novel method that leverages refined alignment of latent representations to achieve style transfer.

Methodology

The primary goal is to disentangle the content from the style in a given sentence, and then recompose it in a different style while keeping the original content intact. The authors propose a cross-alignment mechanism to achieve this, where the content across different text corpora is assumed to follow a shared latent distribution. The process is designed to align the latent representations of sentences in different styles, ensuring that these representations retain the informational integrity of the original content.

The methodology is structured around the following components:

1. Encoder and Decoder Architecture: The model utilizes an encoder to map an input sentence into a content representation that is style-independent. This representation is then passed through a style-specific decoder that generates the final output in the target style.
2. Adversarial Training: To ensure that the disentangled representations are style-independent, an adversarial training mechanism is employed. This involves a discriminator that differentiates between sentences generated in the target style and those transferred into it from another style.
3. Cross-Alignment: This technique involves aligning generated sentences with sentences in the target style at the distributional level, further refining the content representation.

Experimental Results

The methodology was tested on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order from shuffled input.

• Sentiment Modification: The task was to convert sentences from positive to negative sentiment and vice versa. The model achieved a sentiment accuracy of 78.4%, which is competitive with state-of-the-art models such as control-gen (83.5%). Human evaluation indicated an overall transfer quality slightly favoring the proposed model over control-gen.
• Decipherment of Word Substitution Ciphers: The performance was evaluated by the Bleu score on deciphered text with varying rates of substitution (20% to 100%). The cross-aligned autoencoder outperformed other methods, achieving a Bleu score of 57.4 at a 100% substitution rate.
• Word Order Recovery: For the challenging task of recovering the original word order from completely shuffled sentences, the cross-aligned autoencoder achieved a Bleu score of 26.1, surpassing other non-parallel methods by a significant margin.

Theoretical and Practical Implications

The paper's core theoretical contribution is positing that the joint distribution of content and style can be effectively recovered from their marginal distributions if the content is highly informative. This insight is particularly relevant for natural language processing tasks where parallel data is unavailable or difficult to obtain.

Practically, the proposed method allows for style transfer in varied applications ranging from sentiment modification in user reviews to more complex tasks like language decipherment and text reordering. The flexibility and robustness of this method across different tasks highlight its potential for broad application in NLP.

Future Developments

Future research could further refine the disentanglement mechanisms, potentially incorporating more sophisticated adversarial setups or hybrid architectures that combine the strengths of VAEs and adversarial models. Moreover, exploring applications in multilingual style transfer could open new avenues, leveraging the model's robustness in content retention and style separation.

Conclusion

The paper presents a significant stride in the field of style transfer without relying on parallel data. By focusing on cross-alignment of latent representations, the authors provide a robust framework with promising results across multiple challenging NLP tasks. The implications for both theoretical advancement and practical applications are substantial, paving the way for advanced methods to handle style transfer under non-parallel constraints.