TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning (2104.06979v3)

Abstract: Learning sentence embeddings often requires a large amount of labeled data. However, for most tasks and domains, labeled data is seldom available and creating it is expensive. In this work, we present a new state-of-the-art unsupervised method based on pre-trained Transformers and Sequential Denoising Auto-Encoder (TSDAE) which outperforms previous approaches by up to 6.4 points. It can achieve up to 93.1% of the performance of in-domain supervised approaches. Further, we show that TSDAE is a strong domain adaptation and pre-training method for sentence embeddings, significantly outperforming other approaches like Masked LLM. A crucial shortcoming of previous studies is the narrow evaluation: Most work mainly evaluates on the single task of Semantic Textual Similarity (STS), which does not require any domain knowledge. It is unclear if these proposed methods generalize to other domains and tasks. We fill this gap and evaluate TSDAE and other recent approaches on four different datasets from heterogeneous domains.

• The paper presents TSDAE, a Transformer-based sequential denoising autoencoder that reconstructs original sentences from noisy inputs to learn robust embeddings.
• It reports up to 6.4 points improvement over existing unsupervised methods across tasks like information retrieval, re-ranking, and paraphrase identification.
• The approach achieves 93.1% of supervised method performance, highlighting its potential to reduce dependency on expensive labeled data.

Analyzing TSDAE: Unsupervised Sentence Embedding with Transformer-based Sequential Denoising Auto-Encoder

The paper presents an advanced approach for unsupervised sentence embedding using a Transformer-based Sequential Denoising Auto-Encoder (TSDAE). This innovative technique addresses the significant challenge in sentence embedding: the lack of labeled data which is often expensive to create. TSDAE sets itself apart by leveraging pre-trained Transformers and an encoder-decoder architecture to produce robust sentence embeddings.

Core Contributions

1. TSDAE Architecture:
   • TSDAE employs a denoising auto-encoder approach by introducing noise into input sentences and training a model to reconstruct the original sentences. This method ensures the embeddings capture the semantic essence required for reconstruction.
   • The architecture constrains the decoder to leverage only a fixed-size sentence representation, fostering the production of meaningful embeddings.
2. Evaluation Across Diverse Domains:
   • Unlike many previous methods focusing primarily on the Semantic Textual Similarity (STS) tasks, TSDAE is evaluated across multiple tasks, including Information Retrieval, Re-Ranking, and Paraphrase Identification.
   • The results demonstrate that TSDAE can outperform current state-of-the-art unsupervised methods by up to 6.4 points on varied datasets, showing robustness across different domains.
3. Comparison and Performance:
   • TSDAE achieves up to 93.1% of the performance of in-domain supervised methods, indicating its efficacy even in scenarios with minimal labeled data.
   • Empirically, TSDAE outperforms other unsupervised methods such as Masked LLM (MLM), BERT-flow, and SimCSE in both unsupervised learning and domain adaptation contexts.

Numerical Results and Claims

The paper offers substantial numerical evidence supporting TSDAE's superiority. It is noteworthy that TSDAE nearly matches supervised methods on domain-specific tasks using only unlabeled data, and it shows exceptional adaptability as a pre-training method, outperforming other approaches in this setup.

Implications and Future Directions

TSDAE's impressive performance underscores its potential to significantly reduce dependency on labeled datasets, making it valuable in domains where such data is scarce or costly. The approach could pave the way for widespread application in industry settings, where domain-specific adaptability is crucial.

Looking ahead, the research may fuel further examination into enhancing denoising auto-encoders and exploring their synergies with other neural architectures. Moreover, evaluating TSDAE on even broader tasks could further cement its place in the toolkit of sentence embedding methodologies.

Conclusion

The paper solidifies TSDAE as a robust and versatile tool for unsupervised sentence embedding, challenging existing paradigms that rely heavily on labeled data. Its adaptability across domains and potential for domain adaptation mark a significant advancement in the field of NLP, offering much promise for diverse applications in AI and industry.