instruction:
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
question:
We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities.
answer:
What annotations are available in the dataset - tweat used hate speach or not?

question:
We extracted Japanese word pairs from the Evaluation Dataset of Japanese Lexical Simplification kodaira.
answer:
where does the data come from?

question:
The best performing systems being for character unit the RNN-transducer with additional attention module, achieving 7.8% in terms of CER and 17.6% on WER. For subword units, classic RNN-transducer, RNN-transducer with attention and joint CTC-attention show comparable performance on subword error rate and WER, with the first one being slightly better on WER ($17.4\%$) and the last one having a lower error rate on subword ($14.5\%$).
answer:
Which model have the smallest Character Error Rate and which have the smallest Word Error Rate?