Self-Adaptive Hierarchical Sentence Model (1504.05070v2)

Abstract: The ability to accurately model a sentence at varying stages (e.g., word-phrase-sentence) plays a central role in natural language processing. As an effort towards this goal we propose a self-adaptive hierarchical sentence model (AdaSent). AdaSent effectively forms a hierarchy of representations from words to phrases and then to sentences through recursive gated local composition of adjacent segments. We design a competitive mechanism (through gating networks) to allow the representations of the same sentence to be engaged in a particular learning task (e.g., classification), therefore effectively mitigating the gradient vanishing problem persistent in other recursive models. Both qualitative and quantitative analysis shows that AdaSent can automatically form and select the representations suitable for the task at hand during training, yielding superior classification performance over competitor models on 5 benchmark data sets.

• The paper presents AdaSent, a novel self-adaptive hierarchical sentence model that generates flexible, multiscale representations from words to sentences.
• AdaSent utilizes an adaptive gating mechanism and recursive composition to address limitations of previous models like fixed-length representations and gradient vanishing.
• Extensive empirical evaluation demonstrates AdaSent's superior performance over competitor models in classification tasks across five benchmark datasets.

Self-Adaptive Hierarchical Sentence Model for Natural Language Processing

The paper presents a novel approach to sentence modeling in NLP by introducing the Self-Adaptive Hierarchical Sentence Model (AdaSent). This model aims to generate flexible sentence representations through a hierarchical structure that evolves from words to phrases and finally to complete sentences, addressing several limitations inherent in previous models.

Key Contributions

The primary contributions of this paper are centered on three core aspects:

1. Novel Model Architecture: AdaSent constructs a multiscale hierarchical representation rather than abiding by a conventional flat and fixed-length vector representation. This is a significant shift from models like the continuous Bag-of-Words (cBoW), which fail to capture sentence structure due to their reliance on static vector sizes. The AdaSent model, inspired by gated recursive convolutional neural networks (grConv), recursively composes sentence representations while maintaining intermediate information at various levels of abstraction.
2. Adaptive Gating Mechanism: The proposed model employs a flexible gating mechanism that allows for adaptive representational changes depending on the task. This flexibility addresses the gradient vanishing problem typical in recursive models and enables AdaSent to outperform existing models in classification tasks on various benchmark datasets.
3. Empirical Validation: Extensive empirical analysis on five benchmark datasets demonstrates AdaSent's superiority over competitor models such as cBoW, RNN, and BRNN in classification tasks. Notably, the model's architecture is tested on datasets like MR, CR, SUBJ, MPQA, and TREC, where AdaSent consistently shows improved accuracy and robustness.

Implications and Future Developments

The empirical results not only display AdaSent's effectiveness in current NLP classification tasks but also suggest broader implications for future NLP and machine learning research. The introduction of a multiscale hierarchical representation opens avenues for richer and more nuanced sentence modeling approaches, possibly influencing other domains like machine translation and semantic matching.

Furthermore, the architecture's inherent flexibility, owing to the gating network, presents opportunities for it to be adapted beyond NLP to any domain where hierarchical data structures are applicable. Future developments could explore the extension of AdaSent’s application to larger and more varied datasets, improving its scalability and generalizability for complex language tasks.

Conclusion

The AdaSent model represents a significant step forward in the field of NLP by offering an innovative methodology for sentence representation. Its ability to mitigate common issues in recursive neural networks through hierarchical structuring and adaptive gating makes it a highly promising tool for enhancing the precision and applicability of NLP techniques in various contexts. The paper lays a foundation for further research into adaptive hierarchical structures, potentially shaping the future landscape of NLP modeling methodologies.