Beam Search-based Decoding System

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A Beam Search-based Decoding System is a decoding system that implements a beam search-based decoding algorithm.



References

2018

2016

2014

  • (Sutskever et al., 2014) ⇒ Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. (2014). “Sequence to Sequence Learning with Neural Networks.” In: Advances in Neural Information Processing Systems.
    • QUOTE: We search for the most likely translation using a simple left-to-right beam search decoder which maintains a small number B of partial hypotheses, where a partial hypothesis is a prefix of some translation. At each timestep we extend each partial hypothesis in the beam with every possible word in the vocabulary. This greatly increases the number of the hypotheses so we discard all but the B most likely hypotheses according to the model’s log probability. As soon as the “<EOS>” symbol is appended to a hypothesis, it is removed from the beam and is added to the set of complete hypotheses. While this decoder is approximate, it is simple to implement. Interestingly, our system performs well even with a beam size of 1, and a beam of size 2 provides most of the benefits of beam search (Table 1).