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 Q]: In this section, we introduce an invertibility condition for our neural projector to tackle the optimization challenge. Specifically, we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists.  
 Question: What is the invertibility condition?
[EX A]: The neural projector must be invertible.

[EX Q]: 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 A]: Introduce a "Refinement Adjustment LSTM-based component" to the decoder

[EX Q]: We use the MedWeb (“Medical Natural Language Processing for Web Document”) dataset BIBREF4 that was provided as part of a subtask at the NTCIR-13 Conference BIBREF5. The data is summarised in Table TABREF1. There are a total of 2,560 pseudo-tweets in three different languages: Japanese (ja), English (en) and Chinese (zh). 
 Question: How big is dataset used for fine-tuning model for detection of red flag medical symptoms in individual statements?
[EX A]:
a total of 2,560 pseudo-tweets in three different languages: Japanese (ja), English (en) and Chinese (zh)