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courses:rg:2012:encouraging-consistent-translation [2012/10/17 11:43] dusek |
courses:rg:2012:encouraging-consistent-translation [2012/10/17 11:59] dusek |
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| The list of discussed topics follows the outline of the paper: | The list of discussed topics follows the outline of the paper: | ||
| ==== Sec. 2. Related Work ==== | ==== Sec. 2. Related Work ==== | ||
| - | |||
| **Differences from Carpuat 2009** | **Differences from Carpuat 2009** | ||
| * It is different: the decoder just gets additional features, but the decision is up to it -- Carpuat 2009 just post-edits the outputs and substitutes the most likely variant everywhere | * It is different: the decoder just gets additional features, but the decision is up to it -- Carpuat 2009 just post-edits the outputs and substitutes the most likely variant everywhere | ||
| Line 17: | Line 16: | ||
| * The authors do not state their evidence clearly. | * The authors do not state their evidence clearly. | ||
| * One sense is not the same as one translation | * One sense is not the same as one translation | ||
| - | ==== Sec. 3. Exploratory analysis ==== | ||
| + | ==== Sec. 3. Exploratory analysis ==== | ||
| **Hiero** | **Hiero** | ||
| * The idea would most probably work the same in normal phrase-based SMT, but the authors use hierarchical phrase-based translation (Hiero) | * The idea would most probably work the same in normal phrase-based SMT, but the authors use hierarchical phrase-based translation (Hiero) | ||
| Line 39: | Line 38: | ||
| ==== Sec. 4. Approach ==== | ==== Sec. 4. Approach ==== | ||
| - | |||
| The actual experiments begin only now; the used data is different. | The actual experiments begin only now; the used data is different. | ||
| Line 59: | Line 57: | ||
| * but rules are very similar, so we also need something less fine-grained | * but rules are very similar, so we also need something less fine-grained | ||
| * C2 is a target-side feature, just counts the target side tokens (only the "most important" | * C2 is a target-side feature, just counts the target side tokens (only the "most important" | ||
| - | * It may be compared to Language Model features, but is trained only on the target part of the bilingual | + | * It may be compared to Language Model features, but is trained only on the target part of the bilingual |
| * C3 counts occurrences of source-target token pairs (and uses the "most important" | * C3 counts occurrences of source-target token pairs (and uses the "most important" | ||
| Line 65: | Line 63: | ||
| * They need two passes through the data | * They need two passes through the data | ||
| * You need to have document segmentation | * You need to have document segmentation | ||
| - | * Since the frequencies are trained on the training | + | * Since the frequencies are trained on the tuning |
| + | |||
| + | ==== Sec. 5. Evaluation and Discussion ==== | ||
| + | **Choice of baseline** | ||
| + | * Baselines are quite nice and competitive, | ||
| + | * MIRA is very cutting-edge | ||
| + | |||
| + | **Tuning the feature weights** | ||
| + | * For the 1st phase, " | ||
| + | * This is in order to speed up the experiment, they don't want to wait for MIRA twice. | ||
| + | |||
| + | **Different evaluation metrics** | ||
| + | * The BLEU variants do not differ that much, only in Brevity Penalty for multiple references | ||
| + | * IBM BLEU uses the reference that is closest to the MT output (in terms of length), NIST BLEU uses the shortest one | ||
| + | * This was probably just due to some technical reasons, e.g. they had their optimization software designed for one metric and not the other | ||