Unsupervised Pretraining for Sequence to Sequence Learning (1611.02683v2)

Abstract: This work presents a general unsupervised learning method to improve the accuracy of sequence to sequence (seq2seq) models. In our method, the weights of the encoder and decoder of a seq2seq model are initialized with the pretrained weights of two LLMs and then fine-tuned with labeled data. We apply this method to challenging benchmarks in machine translation and abstractive summarization and find that it significantly improves the subsequent supervised models. Our main result is that pretraining improves the generalization of seq2seq models. We achieve state-of-the art results on the WMT English$\rightarrow$German task, surpassing a range of methods using both phrase-based machine translation and neural machine translation. Our method achieves a significant improvement of 1.3 BLEU from the previous best models on both WMT'14 and WMT'15 English$\rightarrow$German. We also conduct human evaluations on abstractive summarization and find that our method outperforms a purely supervised learning baseline in a statistically significant manner.

Overview of Unsupervised Pretraining for Sequence to Sequence Learning

The paper under consideration presents a methodological advancement in sequence to sequence (seq2seq) models via unsupervised pretraining, offering significant improvements in model generalization and efficacy. Seq2seq models are foundational to various applications like machine translation and summarization, yet they often overfit when limited labeled data is available. The authors propose to initialize the weights of the encoder and decoder of a seq2seq model using pretrained LLMs and subsequently fine-tune them with labeled data, thereby reducing overfitting and enhancing model performance.

Methodology

The proposed method involves a two-step process: pretraining and fine-tuning. Initially, separate LLMs are trained on large corpora of unlabeled text in both source and target languages. These pretrained LLMs are then used to initialize the weights of the seq2seq encoder and decoder. The model is thereafter fine-tuned with labeled data, with adjustments made to the seq2seq objective and monolingual LLMing objectives to optimize performance and prevent catastrophic forgetting.

Additional technical improvements, including the use of residual connections and multi-layer attention mechanisms, are incorporated to further enhance model performance. These modifications ensure smoother gradient flow and better utilization of learned features from lower layers of the model.

Experimental Results

The efficacy of the approach has been evaluated across two primary tasks: machine translation (English to German) and abstractive summarization. In the machine translation task, the introduction of unsupervised pretraining resulted in state-of-the-art performance, outperforming previous models by 1.3 BLEU points on the WMT'14 and WMT'15 English to German datasets. For summarization, the approach demonstrated statistically significant improvements over supervised baselines, both qualitatively, based on human evaluations, and quantitatively, using ROUGE scores.

Ablation Studies

The paper includes comprehensive ablation studies to dissect the contributions of the pretraining components. These studies unveil several key insights, such as the vital role of pretraining the decoder in machine translation, the compound advantages of pretraining the entire model, and the critical nature of utilizing sizable, unlabeled datasets for effective pretraining.

Implications and Future Prospects

The integration of unsupervised pretraining in seq2seq models carries practical implications for enhancing model robustness and accuracy in scenarios with sparse labeled data. The methodology, while benchmarked on language tasks, holds potential applicability across a broader range of seq2seq problems, hinting at a versatile framework for various domains relying on complex data transformations.

Future research may delve into refining the pretraining objectives further, exploring domain-specific adaptations, and examining the synergy of this approach with other semi-supervised or unsupervised learning strategies. Another potential avenue is the integration with diverse architectures beyond RNN-based models to evaluate the adaptability and performance enhancements of such pretrained models.

The paper offers a substantive contribution to the field by addressing key limitations of seq2seq models and setting a foundation for subsequent research into leveraging unsupervised pretraining for improved generalization in neural networks.