Unsupervised Sentence Embedding Method by Mutual Information Maximization
The paper introduces an innovative method for unsupervised sentence embedding, evaluating its efficacy without relying on labeled data. The proposed method addresses the known inefficiency of BERT in sentence-pair regression tasks due to the necessity of computing numerous pairwise evaluations, a challenge previously approached by Sentence BERT (SBERT). SBERT required high-quality labeled sentence pairs, limiting its applicability in scenarios with scarce labeled data. The authors' solution uses a novel strategy based on mutual information (MI) maximization to derive meaningful sentence embeddings in an unsupervised manner, eliminating the dependency on labeled datasets.
Methodology
The primary contribution lies in extending BERT with a self-supervised learning objective that maximizes MI between global sentence embeddings and local context embeddings. The method incorporates convolutional neural networks (CNNs) with varying kernel sizes to transform BERT token embeddings into n-gram representations. This allows the model to encode richer semantic structures compared to simple average pooled token embeddings. By maximizing MI, the encoder is encouraged to capture shared global contextual aspects uniquely representative of each sentence, employing negative sampling for contrastive learning.
Experimental Results
The paper presents empirical evidence demonstrating the superiority of their method. The proposed method significantly surpasses other unsupervised sentence embedding models in semantic textual similarity (STS) benchmarks, achieving results competitive with supervised models in certain tasks:
- Semantic Textual Similarity (STS): Across several STS evaluations, the model outperformed unsupervised baselines and rivaled supervised models like InferSent and USE in some settings. Notably, the model achieved an average Spearman rank correlation higher than unsupervised alternatives when applied without labeled data.
- Argument Facet Similarity (AFS): Demonstrated its flexibility by outperforming both BERT and SBERT when applied to a domain-specific dataset without labeled data, reinforcing the benefit of domain-specific training.
- Supervised Evaluations: Utilizing SentEval for a set of supervised tasks, the model presented competitive results with supervised systems, indicating its ability to generate effective sentence embeddings even without label dependency during pretraining.
Implications and Future Work
The implications of this research are twofold. Practically, it offers an approach to effectively manage sentence embedding tasks in domains where labeled data is sparse or expensive, thus broadening the applicability of transfer learning in NLP tasks. Theoretically, it fosters future exploration in representation learning, especially in exploiting mutual information maximization for more expressive embeddings. The authors propose an avenue for further research into semi-supervised methods, potentially enhancing transferability and performance across varied domains.
To conclude, the paper substantiates a significant advancement in unsupervised sentence representation learning, achieving notable results through a novel application of MI maximization. This approach not only challenges existing methodologies centered around supervised learning but offers a potent alternative for diverse and domain-specific applications in NLP.