Clarify a BERT Keyphrase Span-Extraction Task
Last updated: August 25, 2026
Quick Overview
Explain how to clarify a BERT keyphrase span-extraction task, detailing the requirements and approach for implementation.
Figma
August 25, 20261
0
15 solved
Explain how to clarify a BERT keyphrase span-extraction task, detailing the requirements and approach for implementation.
This question is typically asked in a technical screen for a Machine Learning Engineer position at Figma. The interviewer seeks to evaluate the candidate's understanding of natural language processing tasks, particularly how to leverage BERT for extracting key phrases from text. A strong response will demonstrate both theoretical knowledge and practical application skills in machine learning.
What the Interviewer Expects
- Clearly define the keyphrase span-extraction task.
- Explain the role of BERT in this context.
- Discuss data requirements and preprocessing steps.
- Outline the model training and evaluation process.
- Mention potential challenges and solutions.
Key Topics to Cover
How to Approach This
- Understand the bias-variance trade-off. High training accuracy but low test accuracy signals overfitting.
- Choose evaluation metrics carefully based on the problem. Accuracy alone is often insufficient.
- Feature engineering is often more impactful than model selection.
- Know when to use tree-based models (tabular data) vs neural networks (unstructured data).
- Handle class imbalance with SMOTE, class weights, or appropriate loss functions.
Possible Follow-up Questions
- What are some common challenges in keyphrase extraction?
- How would you evaluate the performance of your model?
- Can you explain how BERT handles context in text?
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Explore ML Interview PrepSample Answer
Requirements
To clarify a BERT keyphrase span-extraction task, we start by defining the requirements. The goal is to identify and extract key phrases from a given text span. This involves understanding the context...
High-Level Design
Using BERT for this task involves fine-tuning the pre-trained BERT model on our labeled dataset. BERT's architecture allows it to capture the contextual relationships between words, making it suitable...
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