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: We extracted Japanese word pairs from the Evaluation Dataset of Japanese Lexical Simplification kodaira. 

Answer: where does the data come from?


Question: Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings.

Answer: How big is improvement over the old  state-of-the-art performance on CoNLL-2009 dataset?


Question: In addition to it, we extract three hand-crafted polarity scores, which are minimum, mean, and maximum polarity scores, from each review. These polarity scores of words are computed as in (DISPLAY_FORM4). For example, if a review consists of five words, it would have five polarity scores and we utilise only three of these sentiment scores as mentioned. Lastly, we concatenate these three scores to the averaged word vector per review.

That is, each review is represented by the average word vector of its constituent word embeddings and three supervised scores. We then feed these inputs into the SVM approach. The flowchart of our framework is given in Figure FIGREF11. When combining the unsupervised features, which are word vectors created on a word basis, with supervised three scores extracted on a review basis, we have better state-of-the-art results.

Answer:
Which hand-crafted features are combined with word2vec?