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 Input:
Furthermore, the basic seq2seq assumes a strict order between generated tokens, but in reality, we should not severely punish the model when it predicts the correct tokens in the wrong order. 
 Question: Do they impose any grammatical constraints over the generated output?

Ex Output:
No


Ex Input:
If we re-train the skip-gram model and update the emoji map periodically on new escort ads, when traffickers switch to new emojis, the map can link the new emojis to the old ones, assisting anti-trafficking experts in expanding the lexicon of trafficking flags. This approach also works for acronyms and deliberate typos. 
 Question: How is the lexicon of trafficking flags expanded?

Ex Output:
re-train the skip-gram model and update the emoji map periodically on new escort ads, when traffickers switch to new emojis, the map can link the new emojis to the old ones


Ex Input:
replacing this single GRU with two different components first component is a sentence reader second component is the input fusion layer 
 Question: What changes they did on input module?

Ex Output:

For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader The second component is the input fusion layer