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courses:rg:2012:riezler-iii [2012/11/19 18:56] korvas oprava ohledně tabulky p-hodnot |
courses:rg:2012:riezler-iii [2012/12/03 10:28] (current) korvas added the questions for the answers |
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| - | gg===Martin' | + | ===Martin' |
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| + | 1) | ||
| + | How would you implement approximate randomization for BLEU based on Figure 1, | ||
| + | namely the part " | ||
| + | What are the variable tuples? Can you write a more detailed pseudo (or C, | ||
| + | How would you implement the next part " | ||
| + | |||
| + | 2) | ||
| + | On a testset of 1000 sentences, systems X and Y have exactly the same output except for one sentence: | ||
| + | REF = Hello | ||
| + | MT_X= Hello | ||
| + | MT_Y= Hi | ||
| + | You computed approximate randomization test (based on Figure 1, R=10000 samples) | ||
| + | to check whether the improvement in BLEU is significant. What were the results (i.e. p-value)? | ||
| + | |||
| + | 3) | ||
| + | What would be the p-value for bootstrap test based on a) Figure 2, b) Koehn2004 (the last RG paper)? | ||
| + | This is a bit tricky. Just estimate the expected value of p-value (i.e. 1 - level_of_confidence). | ||
| + | |||
| + | 4) | ||
| + | What would be the p-value for non-strict inequality, i.e. hypothesis " | ||
| 1. The question aimed to find out whether we would repeatedly count the matching n-grams between the MT output and the reference. They can be pre-computed for each sentence and then aggregated without recurring to string matching. | 1. The question aimed to find out whether we would repeatedly count the matching n-grams between the MT output and the reference. They can be pre-computed for each sentence and then aggregated without recurring to string matching. | ||