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Searching for Search Errors in Neural Morphological Inflection
[article]
2021
arXiv
pre-print
Neural sequence-to-sequence models are currently the predominant choice for language generation tasks. Yet, on word-level tasks, exact inference of these models reveals the empty string is often the global optimum. Prior works have speculated this phenomenon is a result of the inadequacy of neural models for language generation. However, in the case of morphological inflection, we find that the empty string is almost never the most probable solution under the model. Further, greedy search often
arXiv:2102.08424v1
fatcat:txdjpecrxjdcxavwfabir3ge3a