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Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.

Francisco Zamora-Martinez1, Maria Jose Castro-Bleda2

  • 11 R&D Department, das-Nano S. L., PolĂ­gono Industrial Talluntxe II, Tajonar 31192, Spain.

International Journal of Neural Systems
|April 11, 2018
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Summary
This summary is machine-generated.

This study integrates Neural Network Language Models (NNLMs) into Statistical Machine Translation (SMT) decoding for improved quality. The novel approach enhances translation performance, particularly for N-gram-based systems.

Keywords:
Neural networksembedded decodinglanguage modelingmachine translationstatistical machine translation

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Area of Science:

  • Computational Linguistics
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Neural Network Language Models (NNLMs) are effective for Natural Language Processing (NLP) tasks.
  • Statistical Machine Translation (SMT) traditionally uses NNLMs for re-scoring.
  • Integrating NNLMs directly into decoding offers potential for enhanced translation quality.

Purpose of the Study:

  • To develop and evaluate an SMT system with fully integrated NNLMs in the decoding stage.
  • To address computational challenges associated with integrating NNLMs.
  • To compare the performance of integrated NNLMs in phrase-based vs. N-gram-based SMT systems.

Main Methods:

  • Developed an SMT system that couples neural network language models (NNLMs) and translation models directly within the decoding process.
  • Implemented a novel memorization and smoothing technique for softmax constants to manage computational costs.
  • Evaluated the system on a machine translation task using various NNLM configurations and compared phrase-based and N-gram-based approaches.

Main Results:

  • The fully integrated NNLM approach in SMT decoding shows promise for improving translation quality.
  • The proposed method for handling softmax constants offers a trade-off between language model quality and computational efficiency.
  • N-gram-based SMT systems demonstrated greater benefits from the integrated NNLM approach, even with non-fully-trained models.

Conclusions:

  • Direct integration of NNLMs into the SMT decoding stage is a promising direction for advancing machine translation.
  • The computational solutions enable stronger influence of neural models on translation output.
  • Further research into N-gram-based SMT with integrated NNLMs is warranted.