instruction:
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.
question:
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 .
answer:
Do the authors test their annotation projection techniques on tasks other than AMR?


question:
Methods ::: Combining the two methods
We further propose to use the two methods together to combine their strengths. In fact, while the length token acts as a soft constraint to bias NMT to produce short or long translation with respect to the source, actually no length information is given to the network. On the other side, length encoding leverages information about the target length, but it is agnostic of the source length.
answer:
Do they experiment with combining both methods?


question:
Routines were developed to simulate input data based on the authors experience with real healthcare data. The reasons for this choice were twofold: One, healthcare data can be high in incidental complexity, requiring one-off code to handle unusual inputs, but not necessarily in such a way as to significantly alter the fundamental engineering choices in a semantic enrichment engine such as this one. Two, healthcare data is strictly regulated, and the process for obtaining access to healthcare data for research can be cumbersome and time-consuming.

A simplified set of input data, in a variety of different formats that occur frequently in a healthcare setting, was used for simulation.
answer:

What type of simulations of real-time data feeds are used for validaton?