Bilingual-GAN: A Step Towards Parallel Text Generation (1904.04742v2)

Abstract: Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively. Leveraging the two domains, we propose an adversarial latent space based model capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is shared between both languages. First two denoising autoencoders are trained, with shared encoders and back-translation to enforce a shared latent state between the two languages. The decoder is shared for the two translation directions. Next, a GAN is trained to generate synthetic "code" mimicking the languages' shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both supervised and unsupervised machine translation.

Overview of Bilingual-GAN: A Step Towards Parallel Text Generation

The research paper titled "Bilingual-GAN: A Step Towards Parallel Text Generation" investigates a novel architecture named Bilingual-GAN, which leverages Generative Adversarial Networks (GANs) to address the challenges in unsupervised machine translation and text generation. The paper builds on prior advancements in latent space representation and attention-based sequence-to-sequence models, aiming to generate parallel sentences in two languages concurrently. This is achieved by creating a shared latent space from which bilingual text can be generated.

Methodology

The Bilingual-GAN framework consists of two primary modules: a translation module and a text generation module.

1. Translation Module: This module employs a sequence-to-sequence model with shared components across two languages. It is inspired by unsupervised Neural Machine Translation (NMT) methodologies that utilize shared latent space representations. The encoder-decoder architecture uses noise-injected input sentences and employs reconstruction, cross-domain, and adversarial losses to enforce language independence within the latent space.
2. Bilingual Text Generation Module: This component utilizes the Improved Wasserstein GAN with gradient penalty (IWGAN-GP) to learn the manifold of the shared latent space generated by the translation module. The generator mimics this latent state distribution, enabling the decoding into either language, facilitated by the pre-trained translation decoder.

Experimental Results

Bilingual-GAN was evaluated on the Europarl and Multi30k datasets for English-French translation tasks. The paper presents results in both supervised and unsupervised settings:

• Translation Performance: The Bilingual-GAN demonstrates competitive BLEU scores in translating between English and French, both in supervised and unsupervised contexts. The presence of shared encoders and cross-lingual embeddings improved translation performance compared to baselines.
• Text Generation Performance: The model's ability to generate fluent text in both languages was validated using BLEU scores, perplexity metrics, and human evaluations. The results indicate that Bilingual-GAN can generate coherent text that shows some degree of parallelism, especially when leveraging parallel data during training.

Implications and Future Directions

Bilingual-GAN represents a significant contribution to the field of multilingual AI, particularly in addressing the gap between monolingual and bilingual text generation. Its approach of modeling bilingual capabilities via shared latent space offers several theoretical implications:

• The integration of GANs in capturing aligned latent representations demonstrates a potential route to tackle unsupervised translation without large parallel corpora.
• The findings suggest paths for enhancing the fluency and consistency of model outputs, crucial for real-world applications requiring bilingual prompts, translations, and cooperative AI interactions.

Practically, this research paves the way for more robust and flexible systems that can generalize across languages. Future research could explore extending Bilingual-GAN to handle multilingual settings, expanding its applicability in diverse linguistic landscapes, and integrating additional modalities such as audio and video for comprehensive multimedia translations. Researchers might also explore optimizing the latent space representations for more accurate parallelism without the reliance on explicit parallel data. By improving upon these foundational insights and methodologies, there lies considerable potential for advancing bilingual and multilingual system capabilities in the coming years.