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.

Example Input: For the Russian language, with its rich morphology, lemmatizing the training and testing data for ELMo representations yields small but consistent improvements in the WSD task.
Question: What other examples of morphologically-rich languages do the authors give?
Example Output: Russian

Example Input: The word intrusion test is expensive to apply since it requires manual evaluations by human observers separately for each embedding dimension. Furthermore, the word intrusion test does not quantify the interpretability levels of the embedding dimensions, instead it yields a binary decision as to whether a dimension is interpretable or not. However, using continuous values is more adequate than making binary evaluations since interpretability levels may vary gradually across dimensions.
Question: What advantages does their proposed method of quantifying interpretability have over the human-in-the-loop evaluation they compare to?
Example Output: it is less expensive and quantifies interpretability using continuous values rather than binary evaluations

Example Input: We have evaluated the output of Nefnir against a reference corpus of 21,093 tokens and their correct lemmas.

Samples for the reference corpus were extracted from two larger corpora, in order to obtain a diverse vocabulary:
Question: Which dataset do they use?
Example Output:
a reference corpus of 21,093 tokens and their correct lemmas