The first challenge NMT faces is Beam search, closely related to this sequential model. As explained in Beam Search Strategies for Neural Machine Translation, “new sentences are translated by a simple beam search decoder that finds a translation that approximately maximizes the probability of a trained NMT model. The beam search strategy generates the translation word-by-word from left to right while keeping a fixed number (beam) of active candidates at each time step.”
However, the challenge of employing beam search in NMT is that it does not always guarantee optimal translation match solutions, albeit being more efficient than the exhaustive search (Lecture 8 on Machine Translation, Sequence-to-sequence and Attention, by Abigail See, Standford educational resources).
Wider beams (larger values) generally yield better results, however, NMT systems will occasionally pick a bad initial word, resulting in a higher probability of biased, less accurate responses.
NMT Challenge 3: Long sentences
As stated in Long Sentence Preprocessing in Neural Machine Translation, “to build an efficient neural machine translation (NMT) system, it is essential to have an accurate and massive bilingual corpus for training, and ensure the continuous improvement of the methods and techniques used in the translation system. Despite multiple advantages, one challenging issue for current neural network translation systems is long sentence processing.”
One of the key differences between existing NMT models is how they incorporate information on word positions in the input.
Koehn & Knowles argue that “NMT systems have lower translation quality on very long sentences but do comparably better up to a sentence length of about 60 words.” And that “the introduction of the attention model remedied this problem somewhat. (…) While overall NMT is better than SMT, the SMT system outperforms NMT on sentences of length 60 and higher. Quality for the two systems is relatively close, except for the very long sentences (80 and more tokens). The quality of the NMT system is dramatically lower for these since it produces too short translations (length ratio 0.859, opposed to 1.024).”
Despite the skyrocketing technological progress of NMT and related AI driven Translation and Localization venues, Neural Machine Translation has yet to completely overcome its roadblocks to become “bulletproof”, the top 5 challenges we have mentioned being just ones of the many at hand.
Robust linguistic behaviours, consistent data training in and out-of-domain testing grounds, accurate alignment, and rare word match may yet be the promising solutions to attain the Neural Machine Translation optimization our cross-linguistic Translation and Localization future ever so longs for.
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