instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations.
Question: What unimodal detection models were used?
answer:
single layer LSTM with a 150-dimensional hidden state for hate / not hate classification

question:
Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers.
Question: How big is dataset of car-speak language?
answer:
$3,209$ reviews

question:
Compared to reading comprehension based QA (RCQA) setup where the answers to a question is usually found in the given small paragraph, in the OpenBookQA setup the open book part is much larger (than a small paragraph) and is not complete as additional common knowledge may be required.
Question: How is OpenBookQA different from other natural language QA?
answer:
in the OpenBookQA setup the open book part is much larger the open book part is much larger (than a small paragraph) and is not complete as additional common knowledge may be required