Teacher: 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.
Teacher: Now, understand the problem? If you are still confused, see the following example:
We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.
Question: Are results reported only for English data?
Solution: No
Reason: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

Now, solve this instance: We then train models on WMT English-German (EnDe) without BT noise, and instead explicitly tag the synthetic data with a reserved token. We repeat these experiments with WMT English-Romanian (EnRo), where NoisedBT underperforms standard BT and TaggedBT improves over both techniques. We perform our experiments on WMT18 EnDe bitext, WMT16 EnRo bitext, and WMT15 EnFr bitext respectively. We use WMT Newscrawl for monolingual data (2007-2017 for De, 2016 for Ro, 2007-2013 for En, and 2007-2014 for Fr). For bitext, we filter out empty sentences and sentences longer than 250 subwords. We remove pairs whose whitespace-tokenized length ratio is greater than 2. This results in about 5.0M pairs for EnDe, and 0.6M pairs for EnRo. We do not filter the EnFr bitext, resulting in 41M sentence pairs.
Question: What datasets was the method evaluated on?
Student:
WMT18 EnDe bitext WMT16 EnRo bitext WMT15 EnFr bitext We perform our experiments on WMT18 EnDe bitext, WMT16 EnRo bitext, and WMT15 EnFr bitext respectively. We use WMT Newscrawl for monolingual data (2007-2017 for De, 2016 for Ro, 2007-2013 for En, and 2007-2014 for Fr). For bitext, we filter out empty sentences and sentences longer than 250 subwords. We remove pairs whose whitespace-tokenized length ratio is greater than 2. This results in about 5.0M pairs for EnDe, and 0.6M pairs for EnRo. We do not filter the EnFr bitext, resulting in 41M sentence pairs.