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.

Ex Input:
Finally, we performed hyperparameter tuning by adjusting the number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks.
Question: What hyperparameters have been tuned?

Ex Output:
number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks

Ex Input:
We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edgesthese may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges).
Question: Did they use a relation extraction method to construct the edges in the graph?

Ex Output:
No

Ex Input:
The basic concept is to use a phone-discriminative model to produce frame-level phonetic features, and then use these features to enhance RNN LID systems that were originally built with raw acoustic features. The initial step is therefore feature combination, with the phonetic feature used as auxiliary information to assist acoustic RNN LID. This is improved further, as additional research identified that a simpler model using only the phonetic feature as the RNN LID input provides even better performance. We call this RNN model based on phonetic features the phonetic temporal neural LID approach, or PTN LID. As well as having a simplified model structure, the PTN offers deeper insight into the LID task by rediscovering the value of the phonetic temporal property in language discrimination.
Question: What is the main contribution of the paper?

Ex Output:
Proposing an improved RNN model, the phonetic temporal neural LID approach, based on phonetic features that results in better performance