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: First we are comparing BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 models with CNN modelBIBREF0 and Stanford CRF modelBIBREF21. 
 Question: What is the baseline?

A: CNN modelBIBREF0 Stanford CRF modelBIBREF21
****
Q: Furthermore, the basic seq2seq assumes a strict order between generated tokens, but in reality, we should not severely punish the model when it predicts the correct tokens in the wrong order. 
 Question: Do they impose any grammatical constraints over the generated output?

A: No
****
Q: The analysis of results from Tables TABREF17 , TABREF18 and TABREF19 show that 12 of 15 best results were obtained using new word embeddings. The evaluation results presented in Table TABREF20 (the chosen best embeddings models from Table TABREF19 ) prove that the best group of word embeddings is EC. The highest type F1-score was obtained for EC1 model, built using binary FastText Skip-gram method utilising subword information, with vector dimension equal to 300 and negative sampling equal to 10. The ability of the model to provide vector representation for the unknown words seems to be the most important. 
 Question: What conclusions are drawn from these experiments?

A:
best results were obtained using new word embeddings best group of word embeddings is EC The highest type F1-score was obtained for EC1 model, built using binary FastText Skip-gram method utilising subword information ability of the model to provide vector representation for the unknown words seems to be the most important
****