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]: A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks.

While the co-occurrence representation yields good results in classification scenarios, some important features are not considered in the model. For example, long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach BIBREF12.
Question: What is the reason that traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text?
[EX A]: long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach

[EX 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?
[EX A]: entity memory and relational memory.

[EX Q]: Consequently, our architecture is based on Embeddings from Language Model or ELMo BIBREF10 Concretely, we use a pre-trained ELMo model, obtained using the 1 Billion Word Benchmark which contains about 800M tokens of news crawl data from WMT 2011 BIBREF24 . Subsequently, the contextualized embeddings are passed on to a BiLSTM with 2,048 hidden units. We aggregate the LSTM hidden states using max-pooling, which in our preliminary experiments offered us better results, and feed the resulting vector to a 2-layer feed-forward network, where each layer has 512 units. The output of this is then fed to the final layer of the model, which performs the binary classification.
Question: What type of model are the ELMo representations used in?
[EX A]:
A bi-LSTM with max-pooling on top of it