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.

Q: The ensembles were formed by simply averaging the predictions from the constituent single models. 
 Question: How does their ensemble method work?

A: simply averaging the predictions from the constituent single models
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Q: We model interactions between observations from the environment, goal, and document using layers. We first encode text inputs using bidirectional LSTMs, then compute summaries using self-attention and conditional summaries using attention. We concatenate text summaries into text features, which, along with visual features, are processed through consecutive layers. In this case of a textual environment, we consider the grid of word embeddings as the visual features for . The final output is further processed by MLPs to compute a policy distribution over actions and a baseline for advantage estimation.  
 Question: How does propose model model that capture three-way interactions?

A:  We first encode text inputs using bidirectional LSTMs, then compute summaries using self-attention and conditional summaries using attention. We concatenate text summaries into text features, which, along with visual features, are processed through consecutive layers. In this case of a textual environment, we consider the grid of word embeddings as the visual features for . The final output is further processed by MLPs to compute a policy distribution over actions and a baseline for advantage estimation.
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Q: To design an interpretable word embedding model for small corpora, we identify novel connections between word embeddings and topic models, and adapt advances from topic modeling. Following the distributional hypothesis BIBREF23 , the skip-gram's word embeddings parameterize discrete probability distributions over words INLINEFORM0 which tend to co-occur, and tend to be semantically coherent – a property leveraged by the Gaussian LDA model of BIBREF21  We thus reinterpret the skip-gram as a parameterization of a certain supervised naive Bayes topic model (Table TABREF2 , top-right). As in LDA, this model can be improved by replacing the naive Bayes assumption with a mixed membership assumption. By applying the mixed membership representation to this topic model version of the skip-gram, we obtain the model in the bottom-right of Table TABREF2 . After once again parameterizing this model with word embeddings, we obtain our final model, the mixed membership skip-gram (MMSG)  
 Question: Which techniques for word embeddings and topic models are used?

A:
 skip-gram LDA
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