A Neural Knowledge Language Model [article]

Sungjin Ahn, Heeyoul Choi, Tanel Pärnamaa, Yoshua Bengio
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
more » ... generate such knowledge-related words by copying from the description of the predicted fact. In experiments, we show that the NKLM significantly improves the performance while generating a much smaller number of unknown words.
arXiv:1608.00318v2 fatcat:hxug555mmfhxleu77uvmul75om