TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: entity memory and relational memory.

PROBLEM: The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging.
Question: Does the paper report the accuracy of the model?

SOLUTION: No

PROBLEM: For the 2019 Workshop on Neural Generation of Text (WNGT) Efficiency shared task BIBREF0, the Notre Dame Natural Language Processing (NDNLP) group looked at a method of inducing sparsity in parameters called auto-sizing in order to reduce the number of parameters in the Transformer at the cost of a relatively minimal drop in performance.
Question: What is WNGT 2019 shared task?

SOLUTION:
efficiency task aimed at reducing the number of parameters while minimizing drop in performance