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:
This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame.
Question: Do they evaluate only on English data?

Ex Output:
Yes

Ex Input:
To better analyze model generalization to an unseen, new domain as well as model leveraging the out-of-domain sources, we propose a new architecture which is an extension of the ARED model. In order to better select, aggregate and control the semantic information, a Refinement Adjustment LSTM-based component (RALSTM) is introduced to the decoder side. The proposed model can learn from unaligned data by jointly training the sentence planning and surface realization to produce natural language sentences.
Question: What is the difference of the proposed model with a standard RNN encoder-decoder?

Ex Output:
Introduce a "Refinement Adjustment LSTM-based component" to the decoder

Ex Input:
Experimentally, on three benchmark datasets for machine translation WMT2014, WMT2016 and IWSLT-2014, FlowSeq achieves comparable performance with state-of-the-art non-autoregressive models, and almost constant decoding time w.r.t. the sequence length compared to a typical left-to-right Transformer model, which is super-linear.
Question: What are three neural machine translation (NMT) benchmark datasets used for evaluation?

Ex Output:
WMT2014, WMT2016 and IWSLT-2014