instruction:
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.
question:
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?
answer:
Russian

question:
In this paper, we present an extraction-then-synthesis framework for machine reading comprehension shown in Figure 1 , in which the answer is synthesized from the extraction results. We build an evidence extraction model to predict the most important sub-spans from the passages as evidence, and then develop an answer synthesis model which takes the evidence as additional features along with the question and passage to further elaborate the final answers.
Question: Which framework they propose in this paper?
answer:
extraction-then-synthesis framework

question:
Furthermore, the gains in WER over the baseline are significantly larger for the Density Ratio method than for Shallow Fusion, with up to 28% relative reduction in WER (17.5% $
ightarrow $ 12.5%) compared to up to 17% relative reduction (17.5% $
ightarrow $ 14.5%) for Shallow Fusion, in the no fine-tuning scenario.
Question: What metrics are used for evaluation?
answer:
word error rate