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]: 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?
[A]: Yes

[Q]: Probing Methodology and Modeling ::: Task Definition and Modeling ::: Baselines and Sanity Checks.
When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42). To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models.
Question: How do they control for annotation artificats?
[A]: we use several of the MCQA baseline models first introduced in BIBREF0

[Q]: Gazetteers: 6)Contributor first names; 7)Contributor last names; 8)Contributor types ("soprano", "violinist", etc.); 9)Classical work types ("symphony", "overture", etc.); 10)Musical instruments; 11)Opus forms ("op", "opus"); 12)Work number forms ("no", "number"); 13)Work keys ("C", "D", "E", "F" , "G" , "A", "B", "flat", "sharp"); 14)Work Modes ("major", "minor", "m").
Question: What task-specific features are used?
[A]:
6)Contributor first names 7)Contributor last names 8)Contributor types ("soprano", "violinist", etc.) 9)Classical work types ("symphony", "overture", etc.) 10)Musical instruments 11)Opus forms ("op", "opus") 12)Work number forms ("no", "number") 13)Work keys ("C", "D", "E", "F" , "G" , "A", "B", "flat", "sharp") 14)Work Modes ("major", "minor", "m")