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.

[EX Q]: As we can see that, all variants of our CRU model could give substantial improvements over the traditional GRU model, where a maximum gain of 2.7%, 1.0%, and 1.9% can be observed in three datasets, respectively. 
 Question: Do experiment results show consistent significant improvement of new approach over traditional CNN and RNN models?
[EX A]: Yes

[EX Q]: The first part is described as Task A, the purpose of which is to identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set. We chose the best 3 results from each word embeddings group (EE, EP, EC) from Table TABREF19 presenting F1-scores for all models. Then we evaluated these results using more detailed measures for timexes, presented in BIBREF27 . 
 Question: What experiments are presented?
[EX A]: identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set  Then we evaluated these results using more detailed measures for timexes

[EX Q]: The accuracy of our model is 7.8% higher than the best result achieved by LSVM. The results show that this model can perform better than state-of-the-art baselines including hybrid CNN BIBREF15 and LSTM with attention BIBREF16 by 3.1% on the validation set and 1% on the test set. 
 Question: What are state of the art methods authors compare their work with? 
[EX A]:
ISOT dataset: LLVM
Liar dataset: Hybrid CNN and LSTM with attention