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:
Introduced by BIBREF16 , in this dataset, the input query is the concatenation of a Wikipedia article title with the title of one of its section. The relevant passages are the paragraphs within that section. The corpus consists of all of the English Wikipedia paragraphs, except the abstracts. The released dataset has five predefined folds, and we use the first four as a training set (approximately 3M queries), and the remaining as a validation set (approximately 700k queries). The test set is the same one used to evaluate the submissions to TREC-CAR 2017 (approx. 1,800 queries).
Question: What is the TREC-CAR dataset?
answer:
in this dataset, the input query is the concatenation of a Wikipedia article title with the title of one of its section. The relevant passages are the paragraphs within that section

question:
We use Gaussian Processes as this probabilistic kernelised framework avoids the need for expensive cross-validation for hyperparameter selection
Question: Why is a Gaussian process an especially appropriate method for this classification problem?
answer:
avoids the need for expensive cross-validation for hyperparameter selection

question:
The Hausa data used in this paper is part of the LORELEI language pack. It consists of Broad Operational Language Translation (BOLT) data gathered from news sites, forums, weblogs, Wikipedia articles and twitter messages. We use a split of 10k training and 1k test instances. The Yoruba NER data used in this work is the annotated corpus of Global Voices news articles recently released by BIBREF22. The dataset consists of 1,101 sentences (26k tokens) divided into 709 training sentences, 113 validation sentences and 279 test sentences based on 65%/10%/25% split ratio.
Question: How much labeled data is available for these two languages?
answer:
10k training and 1k test 1,101 sentences (26k tokens)