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.

Q: As in the Sequicity framework, we report entity match rate, BLEU score and Success F1 score.
Question: What were the evaluation metrics used?

A: entity match rate BLEU score Success F1 score
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Q: The first part is described as Task A, the purpose of which is to identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set. We chose the best 3 results from each word embeddings group (EE, EP, EC) from Table TABREF19 presenting F1-scores for all models. Then we evaluated these results using more detailed measures for timexes, presented in BIBREF27 .
Question: What experiments are presented?

A: identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set Then we evaluated these results using more detailed measures for timexes
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Q: We use hyperopt library to select the hyper-parameters on the following values: LSTM layer size (16, 32, 64), dropout ($0.0-0.9$), activation function ($relu$, $selu$, $tanh$), optimizer ($sgd$, $adam$, $rmsprop$) with varying the value of the learning rate (1e-1,..,-5), and batch size (4, 8, 16)
Question: What activation function do they use in their model?

A:
relu selu tanh
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