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.

Q: To account for sequences of words and characters that might carry useful information, we extracted word unigrams, bigrams, and trigrams as features.
Question: Which features do they use to model Twitter messages?

A: word unigrams, bigrams, and trigrams
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Q: For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to.
Question: Along which dimension do the semantically related words take larger values?

A: dimension corresponding to the concept that the particular word belongs to
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Q: We manually investigate the errors made by artificial neural networks for morphological inflection in a target language after pretraining on different source languages. For our qualitative analysis, we make use of the validation set. Therefore, we show validation set accuracies in Table TABREF19 for comparison. We manually annotate the outputs for the first 75 development examples for each sourcetarget language combination. All found errors are categorized as belonging to one of the following categories.
Question: How is the performance on the task evaluated?

A:
Comparison of test accuracies of neural network models on an inflection task and qualitative analysis of the errors
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