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.

Example Input: after a shared CLWE space has been induced, the task is to retrieve target language translations for a test set of source language words retrieve target language translations for a test set of source language words BLI is cast as a ranking task in our BLI setup with only one correct translation for each "query" word, MAP is equal to mean reciprocal rank (MRR)
Question: How does BLI measure alignment quality?
Example Output: we use mean average precision (MAP) as the main evaluation metric

Example Input: We propose a new unsupervised model, Sent2Vec, for learning universal sentence embeddings Formally, we learn a source (or context) embedding INLINEFORM0 and target embedding INLINEFORM1 for each word INLINEFORM2 in the vocabulary, with embedding dimension INLINEFORM3 and INLINEFORM4 as in ( EQREF6 ). The sentence embedding is defined as the average of the source word embeddings of its constituent words, as in ( EQREF8 ). We augment this model furthermore by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words, i.e., the sentence embedding INLINEFORM5 for INLINEFORM6 is modeled as DISPLAYFORM0
Question: How do the n-gram features incorporate compositionality?
Example Output: by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words

Example Input: In CBOW architecture the task is predicting the word given its context and in SG the task in predicting the context given the word. We built 16 models of word embeddings using the implementation of CBOW and Skip-gram methods in the FastText tool BIBREF9 .
Question: What is specific about the specific embeddings?
Example Output:
predicting the word given its context