Dependency-based Siamese long short-term memory network for learning sentence representations.

Area of Science:

• Natural Language Processing (NLP)
• Deep Learning
• Computational Linguistics

Background:

• Textual representations are crucial for NLP tasks like comprehension and information extraction.
• Existing models (e.g., CBOW, skip-gram) excel at short text but struggle with complex sentence structures.
• Standard Long Short-Term Memory (LSTM) networks process sequences but overlook essential sentence components (subject, predicate, object).

Purpose of the Study:

• To propose a novel model, the dependency-based LSTM (D-LSTM), for learning effective long textual representations.
• To address the limitations of standard LSTMs in capturing sentence structure for improved representation.
• To enhance the quality and informativeness of sentence representations in NLP.

Main Methods:

• Developed the D-LSTM model, which separates sentence representation into basic and supporting components.
• Utilized a pre-trained dependency parser to extract primary sentence information for the supporting component.
• Employed a standard LSTM to generate the basic sentence component.
• Introduced a weight factor to balance the contribution of basic and supporting components.

Main Results:

• The D-LSTM model generates sentence representations containing richer, more useful information compared to standard LSTMs.
• Experimental results demonstrate the superiority of D-LSTM over standard LSTM on the Sentences Involving Compositional Knowledge (SICK) dataset.
• The D-LSTM effectively captures and integrates primary sentence structure into its representations.

Conclusions:

• The D-LSTM model offers a significant advancement in learning sentence representations for NLP.
• By integrating dependency information, D-LSTM overcomes limitations of standard LSTMs for complex sentence understanding.
• This approach holds promise for improving various NLP applications that rely on nuanced textual representations.