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.

[EX Q]: TABREF5 displays the output behavior of models that we damaged to resemble the damage that causes aphasia. Because the output languages in all of our domains use tokens to represent meanings in many cases, it is expected that the analog to Wernicke's area is responsible for maintaining a high precision.
Question: Do they perform a quantitative analysis of their model displaying knowledge distortions?
[EX A]: Yes

[EX Q]: We train language models on two languages: One model is estimated on the German newscrawl distributed by WMT'18 comprising 260M sentences or 6B tokens. Another model is trained on the English newscrawl data comprising 193M sentences or 5B tokens. We learn a joint Byte-Pair-Encoding (BPE; Sennrich et al., 2016) vocabulary of 37K types on the German and English newscrawl and train the language models with this vocabulary. We consider two benchmarks: Most experiments are run on the WMT'18 English-German (en-de) news translation task and we validate our findings on the WMT'18 English-Turkish (en-tr) news task. For WMT'18 English-German, the training corpus consists of all available bitext excluding the ParaCrawl corpus and we remove sentences longer than 250 tokens as well as sentence-pairs with a source/target length ratio exceeding 1.5. This results in 5.18M sentence pairs. We tokenize all data with the Moses tokenizer BIBREF8 and apply the BPE vocabulary learned on the monolingual corpora.
Question: What dataset do they use?
[EX A]: German newscrawl distributed by WMT'18 English newscrawl data WMT'18 English-German (en-de) news translation task WMT'18 English-Turkish (en-tr) news task

[EX Q]: We attribute the significant performance gain of our model over the baseline to a combination of several factors as described below:
Question: Do they train a different training method except from scheduled sampling?
[EX A]:
Answer with content missing: (list missing)
Scheduled sampling: In our experiments, we found that models trained with scheduled sampling performed better (about 0.004 BLEU-4 on validation set) than the ones trained using teacher-forcing for the AVSD dataset. Hence, we use scheduled sampling for all the results we report in this paper.

Yes.