TASK DEFINITION: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
PROBLEM: The annotation projection method, which we follow in this work, is one way to address this problem. It was introduced for POS tagging, base noun phrase bracketing, NER tagging, and inflectional morphological analysis BIBREF29 but it has also been used for dependency parsing BIBREF30 , role labeling BIBREF31 , BIBREF32 and semantic parsing BIBREF26 .

SOLUTION: Do the authors test their annotation projection techniques on tasks other than AMR?

PROBLEM: The model consists of one layer of LSTM followed by three dense layers. The LSTM layer uses a dropout value of 0.2. 

SOLUTION: Do they use dropout?

PROBLEM: The best performing systems being for character unit the RNN-transducer with additional attention module, achieving 7.8% in terms of CER and 17.6% on WER. For subword units, classic RNN-transducer, RNN-transducer with attention and joint CTC-attention show comparable performance on subword error rate and WER, with the first one being slightly better on WER ($17.4\%$) and the last one having a lower error rate on subword ($14.5\%$).

SOLUTION:
Which model have the smallest Character Error Rate and which have the smallest Word Error Rate?