  Many Natural Language Processing (NLP) tasks depend on using Named Entities
(NEs) that are contained in texts and in external knowledge sources. While this
is easy for humans, the present neural methods that rely on learned word
embeddings may not perform well for these NLP tasks, especially in the presence
of Out-Of-Vocabulary (OOV) or rare NEs. In this paper, we propose a solution
for this problem, and present empirical evaluations on: a) a structured
Question-Answering task, b) three related Goal-Oriented dialog tasks, and c) a
Reading-Comprehension task, which show that the proposed method can be
effective in dealing with both in-vocabulary and OOV NEs. We create extended
versions of dialog bAbI tasks 1,2 and 4 and OOV versions of the CBT test set
available at - https://github.com/IBM/ne-table-datasets.
