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: We optimized our single-task baseline to get a strong baseline in order to exclude better results in multi-task learning in comparison to single-task learning only because of these two following points: network parameters suit the multi-task learning approach better and a better randomness while training in the multi-task learning. To exclude the first point, we tested different hyperparameters for the single-task baseline. We tested all the combinations of the following hyperparameter values: 256, 512, or 1024 as the sizes for the hidden states of the LSTMs, 256, 512, or 1024 as word embedding sizes, and a dropout of 30 %, 40 %, or 50 %. We used subword units generated by byte-pair encoding (BPE) BIBREF16 as inputs for our model. To avoid bad subword generation for the synthetic datasets, in addition to the training dataset, we considered the validation and test dataset for the generating of the BPE merge operations list. We trained the configurations for 14 epochs and trained every configuration three times. We chose the training with the best quality with regard to the validation F1-score to exclude disadvantages of a bad randomness. We got the best quality with regard to the F1-score with 256 as the size of the hidden states of the LSTMs, 1024 as word embedding size, and a dropout of 30 %. For the batch size, we used 64. 
 Question: What are the strong baselines you have?

A: optimize single task with no synthetic data
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Q: Combining the modulus part and the phase part, HAKE maps entities into the polar coordinate system, where the radial coordinate and the angular coordinates correspond to the modulus part and the phase part, respectively. 
 Question: How are entities mapped onto polar coordinate system?

A: radial coordinate and the angular coordinates correspond to the modulus part and the phase part, respectively
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Q: Generally, adversarial training improves the accuracy across all languages, and the improvement is sometimes dramatic versus the BERT non-adversarial baseline. When combined with adversarial learning, the BERT cross-lingual F1 scores increased for German over the non-adversarial baseline, and the scores remained largely the same for Spanish and Dutch. 
 Question: Does adversarial learning have stronger performance gains for text classification, or for NER?

A:
classification
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