Question 1

+2−0Single correct

FastText differs from traditional word embedding models like Word2Vec because it can handle out-of-vocabulary (OOV) words more effectively. Which of the following best explains how FastText achieves this capability?

A.
FastText predicts context words using an entire sentence rather than just nearby words.
B.
FastText generates embeddings by averaging multiple pre-trained word vectors.
C.
FastText breaks words down into smaller subword units (character n-grams) and creates embeddings based on these subwords, allowing it to estimate embeddings for unseen words.
D.
FastText uses neural networks with an additional layer dedicated to identifying unknown words.

Question 1

+2−0Single correct

A.
FastText predicts context words using an entire sentence rather than just nearby words.
B.
FastText generates embeddings by averaging multiple pre-trained word vectors.
C.
FastText breaks words down into smaller subword units (character n-grams) and creates embeddings based on these subwords, allowing it to estimate embeddings for unseen words.
D.
FastText uses neural networks with an additional layer dedicated to identifying unknown words.

Question 1

Report a problem

Submit this paper?

Instructions

Question 1

Report a problem

Submit this paper?

Instructions