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 have evaluated the output of Nefnir against a reference corpus of 21,093 tokens and their correct lemmas.

Samples for the reference corpus were extracted from two larger corpora, in order to obtain a diverse vocabulary: 
 Question: Which dataset do they use?
answer:
a reference corpus of 21,093 tokens and their correct lemmas


question:
What is the impact of pre-trained representations with less transcribed data? In order to get a better understanding of this, we train acoustic models with different amounts of labeled training data and measure accuracy with and without pre-trained representations (log-mel filterbanks). The pre-trained representations are trained on the full Librispeech corpus and we measure accuracy in terms of WER when decoding with a 4-gram language model. Figure shows that pre-training reduces WER by 32% on nov93dev when only about eight hours of transcribed data is available. Pre-training only on the audio data of WSJ ( WSJ) performs worse compared to the much larger Librispeech ( Libri). This further confirms that pre-training on more data is crucial to good performance. 
 Question: Do they explore how much traning data is needed for which magnitude of improvement for WER? 
answer:
Yes


question:
For the labeled data, we use audio from the IEMOCAP dataset, which comes with labels for activation and valence, both measured on a 5-point Likert scale from three distinct annotators. 
 Question: Which multitask annotated corpus is used?
answer:

IEMOCAP