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A Neural Knowledge Language Model
[article]
2017
arXiv
pre-print
Current language models have a significant limitation in the ability to encode and decode factual knowledge. This is mainly because they acquire such knowledge from statistical co-occurrences although most of the knowledge words are rarely observed. In this paper, we propose a Neural Knowledge Language Model (NKLM) which combines symbolic knowledge provided by the knowledge graph with the RNN language model. By predicting whether the word to generate has an underlying fact or not, the model can
arXiv:1608.00318v2
fatcat:hxug555mmfhxleu77uvmul75om