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Compute Sentence Similarity

Last updated: Apr 6, 2026

Quick Overview

This question evaluates understanding of sentence-level and token-level embedding techniques, text preprocessing, similarity metrics, edge-case handling for empty or unknown tokens, and trade-offs between pretrained sentence encoders and averaged word embeddings.

  • medium
  • Bytedance
  • Machine Learning
  • Machine Learning Engineer

Compute Sentence Similarity

Company: Bytedance

Role: Machine Learning Engineer

Category: Machine Learning

Difficulty: medium

Interview Round: Technical Screen

Given two text inputs, design and implement a method to compute their semantic similarity. You may use either of the following approaches: 1. Encode each sentence into a single embedding using a pretrained sentence encoder, then compute cosine similarity. 2. Convert each token to a word embedding, average the token embeddings for each sentence, then compute cosine similarity between the two averaged vectors. Your answer should describe: - How the text is preprocessed - How embeddings are produced - How cosine similarity is computed - How to handle empty text or unknown tokens - The trade-offs between sentence-level encoders and average word embeddings If coding is requested, provide clear pseudocode or implementation-level steps.

Quick Answer: This question evaluates understanding of sentence-level and token-level embedding techniques, text preprocessing, similarity metrics, edge-case handling for empty or unknown tokens, and trade-offs between pretrained sentence encoders and averaged word embeddings.

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Bytedance
Jan 7, 2026, 12:00 AM
Machine Learning Engineer
Technical Screen
Machine Learning
1
0

Given two text inputs, design and implement a method to compute their semantic similarity.

You may use either of the following approaches:

  1. Encode each sentence into a single embedding using a pretrained sentence encoder, then compute cosine similarity.
  2. Convert each token to a word embedding, average the token embeddings for each sentence, then compute cosine similarity between the two averaged vectors.

Your answer should describe:

  • How the text is preprocessed
  • How embeddings are produced
  • How cosine similarity is computed
  • How to handle empty text or unknown tokens
  • The trade-offs between sentence-level encoders and average word embeddings

If coding is requested, provide clear pseudocode or implementation-level steps.

Solution

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