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:
After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset.
Question: How many sentence transformations on average are available per unique sentence in dataset?
answer:
27.41 transformation on average of single seed sentence is available in dataset.

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:
Despite these shortcomings in using WER as a metric of success, the WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD based on the given reading passage and answer. WER can be used for initial analyses that can lead to deeper insights as discussed further below.
Question: Why did they choose WER as evaluation metric?
answer:
WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD WER can be used for initial analyses