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.

Example Input: We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations.
Question: What unimodal detection models were used?
Example Output: single layer LSTM with a 150-dimensional hidden state for hate / not hate classification

Example Input: In Figure FIGREF15 , we plot the zero-resource German and Japanese test set accuracy as a function of the number of steps taken, with and without adversarial training. The plot shows that the variation in the test accuracy is reduced with adversarial training, which suggests that the cross-lingual performance is more consistent when adversarial training is applied.
Question: Do any of the evaluations show that adversarial learning improves performance in at least two different language families?
Example Output: Yes

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