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]: In our methodology to design new evaluation metrics for comparing reference summaries/translations to hypothesis ones, we established first-principles criteria on what a good evaluator should do. The first one is that it should be highly correlated with human judgement of similarity. The second one is that it should be able to distinguish sentences which are in logical contradiction, logically unrelated or in logical agreement. The third one is that a robust evaluator should also be able to identify unintelligible sentences. The last criteria is that a good evaluation metric should not give high scores to semantically distant sentences and low scores to semantically related sentences.
Question: What is the criteria for a good metric?
[A]: The first one is that it should be highly correlated with human judgement of similarity. The second one is that it should be able to distinguish sentences which are in logical contradiction, logically unrelated or in logical agreement. The third one is that a robust evaluator should also be able to identify unintelligible sentences. The last criteria is that a good evaluation metric should not give high scores to semantically distant sentences and low scores to semantically related sentences.

[Q]: There are three main components to the model: 1) input encoder 2) dynamic memory, and 3) output module. We will describe these three modules in details. The input encoder and output module implementations are similar to the Entity Network BIBREF17 and main novelty lies in the dynamic memory. We describe the operations executed by the network for a single example consisting of a document with $T$ sentences, where each sentence consists of a sequence of words represented with $K$ -dimensional word embeddings $\lbrace e_1, \ldots , e_N
brace $ , a question on the document represented as another sequence of words and an answer to the question.
Question: How is knowledge stored in the memory?
[A]: entity memory and relational memory.

[Q]: Recurrent Neural Network (RNN) Language Models
are trained to output the probability distribution of the upcoming word given a context, without explicitly representing the structure of the context BIBREF9, BIBREF10. ActionLSTM
models the linearized bracketed tree structure of a sentence by learning to predict the next action required to construct a phrase-structure parse BIBREF16. Generative Recurrent Neural Network Grammars (RNNG)
jointly model the word sequence as well as the underlying syntactic structure BIBREF18.
Question: What are the baseline models?
[A]:
Recurrent Neural Network (RNN) ActionLSTM Generative Recurrent Neural Network Grammars (RNNG)