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.
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Question: mcdonald:11 established that, when no treebank annotations are available in the target language, training on multiple source languages outperforms training on one (i.e., multi-source model transfer outperforms single-source model transfer). Following guo:16, for each target language, we train the parser on six other languages in the Google universal dependency treebanks version 2.0 (de, en, es, fr, it, pt, sv, excluding whichever is the target language), and we use gold coarse POS tags.

Answer: How does the model work if no treebank is available?


Question: Then, it selects a subset of a 1,700-hour ( INLINEFORM2 1.1M instances) unlabeled dataset.

Answer: How much data samples do they start with before obtaining the initial model labels?


Question: 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 .

Answer:
What experiments are presented?