CS7643 Quiz 4 Latest Update 2025-2026 Exam 65

CS7643 Quiz 4 Latest Update 2025-2026 Exam 65 Questions with 100% Verified Correct Answers Guaranteed A+

Collobert and Weston Vector Idea - CORRECT ANSWER: a word and its context is a

positive training sample; a random word in that sample context gives a negative training sample

Conditional language models and how to train them (teacher/student forcing), language

metrics (how to calculate them) - CORRECT ANSWER:

Debiasing word2vec - CORRECT ANSWER: - identify gender subspace with gendered

words

• project all words onto this subspace
• subtract those projections from the original word

Problem: Not that effective and bias pervades the word embedding space

Embedding - CORRECT ANSWER: A learned map from entities to vectors that encodes

similarity

Evaluating Word Embeddings Extrinsic - CORRECT ANSWER: - Evaluation on real task

• Can take a long time to compute
• Unclear if the subsystem is the problem or its interaction
• if replacing exactly one subsystem with another improves accuracy -> winning

Evaluating Word Embeddings Intrinsic - CORRECT ANSWER: - Evaluation on a

specific/intermediate subtask 1 / 2

• Fast to compute
• Helps to understand the system
• Not clear if really helpful unless correlation to real task is established

Example: Evaluate word vectors by how well their cosine distance after addition

captures intuitive semantic and syntactic analogy questions

Graph Embedding - CORRECT ANSWER: Optimize the objective that connected nodes

have more similar embeddings than unconnected nodes.

Task: convert nodes to vectors

• effectively unsupervised learning where nearest neighbors are similar
• these learned vectors are useful for downstream tasks

Graph Embedding is Slow: Reason and Solution - CORRECT ANSWER: - Training time dominated by computing scores for "fake edges"

• Corrupt a sub-batch of edges with the same set of random nodes

Graph Embeddings Loss Function - CORRECT ANSWER: - Margin loss between the

score of an edge f(e) and a negative sampled edge f(e')

• Negative sampled edges are constructed by taking real edge and replacing either the

source or destination vertex with a random node

• the score of an edge f(e) is a similarity (dot product) between the source embedding

and a transformed version of the destination embedding

• f(e) = cos( theta(s) , theta(d) + theta(r) )
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