instruction:
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.
question:
Although no perfect matches exist, we decided upon the Quora duplicate question dataset BIBREF22 as the best match. To study the embeddings, we computed the euclidean distance between the two questions using various embeddings, to study the distance between semantically similar and dissimilar questions.
Question: What existing corpus is used for comparison in these experiments?
answer:
Quora duplicate question dataset BIBREF22

question:
In a nutshell, B4MSA starts by applying text-transformations to the messages, then transformed text is represented in a vector space model (see Subsection SECREF13 ), and finally, a Support Vector Machine (with linear kernel) is used as the classifier.
Question: What are the components of the multilingual framework?
answer:
text-transformations to the messages vector space model Support Vector Machine

question:
In the unsupervised scenario similarity is computed as the cosine of the produced $h_{L}$ and $h_{R}$ sentence/image representations.
Question: How they compute similarity between the representations?
answer:
similarity is computed as the cosine of the produced $h_{L}$ and $h_{R}$ sentence/image representations