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Neural Semantic Encoders.

Tsendsuren Munkhdalai¹, Hong Yu¹

¹University of Massachusetts, MA, USA.

Proceedings of the Conference. Association for Computational Linguistics. Meeting

|October 31, 2017

Summary

This summary is machine-generated.

Neural Semantic Encoders (NSE) are novel memory augmented neural networks for natural language understanding. NSE achieves state-of-the-art performance across five diverse NLP tasks by utilizing read, compose, and write operations with evolving memory.

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Area of Science:

Artificial Intelligence
Natural Language Processing
Deep Learning

Background:

Traditional neural networks struggle with long-term dependencies and contextual understanding in natural language processing.
Existing memory-augmented networks often have fixed memory capacities, limiting their ability to adapt to complex, evolving sequences.

Purpose of the Study:

To introduce Neural Semantic Encoders (NSE), a novel memory-augmented neural network architecture for enhanced natural language understanding.
To demonstrate the flexibility and state-of-the-art performance of NSE across a variety of NLP tasks.
To explore the benefits of variable-sized, evolving memory and multi-shared memory access in NLP models.

Main Methods:

Developed Neural Semantic Encoders (NSE) with a unique memory update rule enabling dynamic memory evolution.
Implemented read, compose, and write operations for processing input sequences within the encoding memory.
Utilized multiple and shared memory access capabilities within the NSE architecture.
Evaluated NSE on five distinct natural language tasks: inference, question answering, classification, sentiment analysis, and machine translation.

Main Results:

Achieved state-of-the-art performance on benchmarks for natural language inference, question answering, sentence classification, and document sentiment analysis.
Demonstrated significant improvements in neural machine translation, with a shared-memory model enhancing an attention-based baseline by approximately 1.0 BLEU.
Showcased the effectiveness and flexibility of the variable-sized, evolving memory mechanism.

Conclusions:

Neural Semantic Encoders represent a significant advancement in memory-augmented neural networks for natural language understanding.
The dynamic memory update rule and multi-memory access contribute to superior performance across diverse NLP applications.
NSE offers a flexible and powerful framework for tackling complex language tasks, setting a new standard in the field.