  We study the problem of stock related question answering (StockQA):
automatically generating answers to stock related questions, just like
professional stock analysts providing action recommendations to stocks upon
user's requests. StockQA is quite different from previous QA tasks since (1)
the answers in StockQA are natural language sentences (rather than entities or
values) and due to the dynamic nature of StockQA, it is scarcely possible to
get reasonable answers in an extractive way from the training data; and (2)
StockQA requires properly analyzing the relationship between keywords in QA
pair and the numerical features of a stock. We propose to address the problem
with a memory-augmented encoder-decoder architecture, and integrate different
mechanisms of number understanding and generation, which is a critical
component of StockQA.
  We build a large-scale dataset containing over 180K StockQA instances, based
on which various technique combinations are extensively studied and compared.
Experimental results show that a hybrid word-character model with separate
character components for number processing, achieves the best performance. By
analyzing the results, we found that 44.8% of answers generated by our best
model still suffer from the generic answer problem, which can be alleviated by
a straightforward hybrid retrieval-generation model.
