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.

Q: We first evaluate our converted embedding models on multi-language lexical similarity and relatedness tasks, as a sanity check, to make sure the word sense induction process did not hurt the general performance of the embeddings. Then, we test the sense embeddings on WSD task.

A: Was any extrinsic evaluation carried out?
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Q: Paraphrase Identification (PI) is the task of determining whether two sentences are paraphrase or not. We have implemented the cross-lingual variant of kernel functions for PI and RE tasks as described in section SECREF3 and measured the accuracy of models by testing them on the parallel data set.

A: What classification task was used to evaluate the cross-lingual adaptation method described in this work?
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Q: Baselines. We use one strong non-DNN baseline, NBSVM (with unigrams or bigrams features) BIBREF23 and six DNN baselines. The first DNN baseline is CNN BIBREF25, which does not handle noisy labels. The other five were designed to handle noisy labels.

The comparison results are shown in Table TABREF12. From the results, we can make the following observations. (1) Our NetAb model achieves the best ACC and F1 on all datasets except for F1 of negative class on Laptop.

A:
Is the model evaluated against a CNN baseline?
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