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:
Fenerbahce We have decided to consider tweets about popular sports clubs as our domain for stance detection. Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahce (namely, Target-2) which are two of the most popular football clubs in Turkey. Then, we have annotated the stance information in the tweets for these targets as Favor or Against. For the purposes of the current study, we have not annotated any tweets with the Neither class.
Question: How were the tweets annotated?
answer:
tweets are annotated with only Favor or Against for two targets - Galatasaray and Fenerbahce

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:
As can be seen, the proposed DSC loss outperforms the best baseline results by a large margin, i.e., outperforming BERT-tagger by +1.86 in terms of F1 score on CTB5, +1.80 on CTB6 and +2.19 on UD1.4.
Question: What are method's improvements of F1 w.r.t. baseline BERT tagger for Chinese POS datasets?
answer:
+1.86 in terms of F1 score on CTB5 +1.80 on CTB6 +2.19 on UD1.4