14 Common Natural Language Processing Interview Questions and How to Prepare

Natural language processing (NLP) blends computer science, machine learning (ML), and linguistics to power technologies like chatbots, voice assistants, and smart search. If you're heading into an NLP interview, you're expected to do more than define concepts; you'll also need to explain how you've applied them. Preparing for these 14 common interview questions can help you connect your technical skills to real-world impact and walk in with confidence.

14 common natural language processing interview questions

An interviewer may ask any variety of questions. However, below are 14 common ones for an NLP interview, including potential variations. 

1. Tell me a bit about yourself.

What they’re really asking: Why are you the right candidate for this job?

This is a simple enough question on the surface, but what your interviewer is really asking is to give a short summary of your job history working with NLP. If you have no real NLP job history, this is a good opportunity to discuss why you’re interested in moving into an NLP-based position. 

Consider what makes NLP so interesting to you? Do you like the idea of working in a language-focused subfield of artificial intelligence (AI)? Is it the practical side of ML that interests you? Are you fascinated by how technology, such as search engines, digital assistants, and chatbots, can improve people’s lives? 

Again, this is just a way for an interviewer to ease you into things. Discuss why you’re drawn to the field and why you’re the best candidate for the job at hand.

Other forms this question might take: 

• What interests you about NLP?

• Tell me about your NLP journey.

• What makes you the best candidate for this role?

2. What are the key challenges in NLP?

What they’re really asking: Can you think critically about real-world applications of NLP?

The interviewer wants to know that you’re aware that NLP continues to evolve. Understanding both its possibilities and limitations can help you discuss it more clearly in interviews. 

This is a chance for you to show that you’re broad-minded and have considered how far the technology still has to go. Mention issues such as: 

• Biased output

• Speech misunderstanding

• Tone of voice issues

All of these issues affect users of NLP technology. This question gives you an opportunity to show that you understand how NLP affects the people who use it. 

Other forms this question might take: 

• What are some problems with NLP? 

• What difficulties might users face with NLP, and how do you fix them?

• In what areas could NLP improve? 

3. What are the different natural language processing techniques for text preprocessing?

What they’re really asking: Can you explain how NLP works at a root level to someone with little to no background in it?

With a question like this, your interviewer wants to understand a bit more about the breadth of your technical knowledge. Your interviewer may not be a subject matter expert, and this kind of question helps an interviewer assess how well you explain technical terms to a non-specialist. It also shows them how well you communicate. 

With that in mind, briefly and succinctly mention and define key text preprocessing terms such as: 

• Coreference resolution: Identifying when two separate words refer to a single entity

• Lemmatization: Learning to leave endings off words, grouping them by dictionary root

• Named entity recognition: Sorting words into categories, such as “cities”

• Part-of-speech tagging: Categorizing words as nouns, verbs, adjectives, adverbs, etc. 

• Sentiment analysis: Determining whether text is positive, negative, or neutral in tone

• Word sense disambiguation: Determining the correct meaning based on context

Communication skills remain important even in highly technical fields. That’s likely what your interviewer is assessing here. 

Other forms this question might take: 

• What are some NLP preprocessing techniques? 

• How do NLPs preprocess text? 

• What are the important parts of NLP text preprocessing? 

4. How do you handle text classification and sentiment analysis?

What they’re really asking: Can you apply specific NLP techniques to real-world business problems?

Your interviewer wants to understand your approach to a particular NLP technique because it reflects your ability to handle the primary responsibilities of the job for which you’re interviewing. In this case, your interviewer wants to discuss two techniques often used in tandem in the marketing and customer service fields. 

Here is a sample response: 

I handle sentiment analysis initially via text classification and preprocessing techniques such as tokenization, lemmatization, and stop-word removal in order to simplify a text. This makes it easier for an NLP program to understand. Then I assign a weighted score based on keyword analysis. 

This answer shows that you understand NLP both generally and in terms of specific use cases. It also shows that you can explain it in plain language, building on the previous questions the interviewer asked. 

Other forms this question might take: 

• How do you perform sentiment analysis? 

• What is important to consider when performing sentiment analysis? 

• How do you best utilize text classification and sentiment analysis?

5. What is the role of feature engineering in NLP?

What they’re really asking: Do you understand the basic value of predictive data analysis?

Here, your interviewer wants to know that you know more about a technical aspect of NLP that has a bearing on many data-centric tasks. Your answer shows that you possess a holistic understanding of how to use NLP in a broad way. 

One possible answer to use is:

The role of feature engineering is to turn numeric values into meaningful information that ML models use to make better predictions. To show you’ve got the practical implications of NLP in mind, mention that feature engineering helps improve model performance, enhance interpretability, and lower computational cost. 

Other forms this question might take: 

• How do you make data work for an NLP or ML model? 

• Why is feature engineering an important component of NLP?

• Tell me why better predictive analytics techniques are important.

6. What are the different types of word embeddings?

What they’re really asking: How does NLP make sense of raw data?

This question further tests your understanding of how NLP turns raw data into human-like language. 

In your response, you want to show your understanding that word embeddings help an NLP program make sense of data via semantic analysis—that is, a program model understands words in a sentence not as individual entities but semantically, in context. You may also want to answer the question directly by explaining different types of word embeddings, such as: 

• Co-occurrence matrix

• FastText

• GloVe 

• Term frequency-inverse document frequency (TF-IDF)

• Word2Vec

Other forms this question might take: 

• What word embedding techniques do you use? 

• What are the best word embedding techniques for NLP?

• What is your preferred NLP word embedding technique?

7. How do you build a language model?

What they’re really asking: What does your creative process look like?

The best approach for this question is to mention a few steps in order, like this: 

1. I gather unstructured text data from a wide variety of sources. 

2. I preprocess that data. 

3. I use pre-processed data and ML to train my NLP model based on use cases.

4. I deploy my NLP model.

You might even mention how you refine your model with new data to keep improving its performance. This highlights your commitment to accuracy and continuous improvement. 

Other forms this question might take: 

• What is your method for building an NLP model? 

• Where do you start building an NLP model? 

• How do you know when to deploy your NLP model? 

8. What is the difference between rules-based and statistical NLP?

What they’re really asking: Tell me about different NLP programming techniques.

Here, your interviewer wants to know that you are familiar with a variety of NLP models and training techniques. In many roles, you'll need to collaborate with teams that use a range of approaches. 

For example, rules-based NLP involves preprogramming specific rules, much like memorizing grammar rules when learning a new language. It’s often considered a basic form of NLP best used alongside statistical NLP, where models learn patterns in input data and predict the likelihood of one word following another. 

This type of answer shows that you understand the technical differences and how to apply them effectively in modern NLP development. 

Other forms this question might take: 

• What are some different types of NLP?

• How does statistical NLP work versus rules-based NLP?

• Can rules-based and statistical NLP work together? 

9. How do you evaluate the performance of NLP models?

What they’re really asking: Do you know how to fix NLP model issues?

This is a somewhat more technical question. First of all, you might mention why you might evaluate your NLP model. Mention issues such as: 

• Accuracy 

• Bias

• Data drift

Then, mention evaluation techniques related to classification and regression metrics. 

Other forms this question might take: 

• What techniques do you use to evaluate your NLP model?

• Why is it important to evaluate your NLP model? 

• What are some NLP evaluation metrics you use?

10. What is the role of deep learning in NLP?

What they’re really asking: Do you have a holistic understanding of how NLP, deep learning, and AI interact both theoretically and practically?

Here, your interviewer wants you to branch out into a larger explanation of NLP, explaining how modern AI technology makes complex decisions, and how deep learning affects natural language processing models. Mention, for instance, that without deep learning neural networks, you wouldn’t be able to train NLP on large enough stores of data to get it to output the human-like content it’s known for. 

One approach to this question is to discuss a well-known form of deep learning-based NLP, such as ChatGPT. 

Other forms this question might take: 

• How important is deep learning in NLP?

• What applications does deep learning allow for in NLP models? 

• How do you use deep learning in creating NLP models?

11. What is text summarization, and how is it done?

What they’re really asking: To what extent do you consider user experience when creating NLP models?

With this question, your interviewer wants to know if you understand a practical NLP use case: summarizing long texts into key points that are easier for people to read. It also gives you another chance to show that you think about user experience and not just technical implementation. 

Mention both abstractive and extractive approaches to text summarization. You can also highlight how summarization supports data-driven decisions in fields like academia, law, and marketing. 

Other forms this question might take: 

• Is NLP effective for text summarization?

• How would you program an NLP model to effectively summarize text?

• Who uses text summarization, and how can you improve their experience?

12. How do you handle text generation and machine translation?

What they’re really asking: Are you comfortable working with NLP in a variety of different ways?

Text generation and machine translation are two more common NLP use cases. In your response, you want to show your understanding of best practices, such as making sure an NLP model’s output is grammatically correct, contextually relevant, and useful in terms of the use case at hand. Then, briefly explain the techniques you use to ensure these results.

Other forms this question might take: 

• How do you evaluate the output of text generation and machine translation? 

• Is NLP good for text generation or machine translation? 

• Do you prefer NLP for text generation and machine translation over older ML models?

13. What is the future of NLP?

What they’re really asking: Are you considering the ramifications of NLP use and how you, as an employee, can improve it?

Your interviewer isn’t asking you to reliably predict the future here. Rather, they’re likely asking your opinion about what's on the horizon in a general way: What challenges will NLP face, where is there room for improvement, what use cases might NLP be brought to bear on, and how can you, personally, affect change in a positive way? 

This is a chance to show that you think critically about natural language processing and even artificial intelligence in general. For example, you could mention ethical challenges such as bias, the environmental impact of NLP, and model explainability.

You might also mention advances in NLP, such as improved language translation, more capable digital assistants, and personalized search experiences. This shows that you’ve thought about NLP’s place in the world and what good it might do for people. 

Other forms this question might take: 

• What challenges will NLP face in the future? 

• What are the downsides of NLP?

• How will NLP improve in the future?

14. What questions do you have for me?

Before going into an interview, always prepare two or three questions you can ask at the end. The interviewer wants to know if you’re right for the company, and you want to know if the company is a place where you want to work. More than that, asking questions lets an interviewer know you’ve researched the company, shows your critical thinking, and helps you stand out from what may be a large pool of applicants. 

For an interview regarding a role developing NLP models, you might ask about specific NLP models the company uses or where they see themselves in the NLP world. You could also ask about how they measure success in the role or what attributes they expect to see in the ideal candidate. You could also choose to focus on team dynamics by asking a question about how individuals within the company work collaboratively.

Prepare for your NLP interview with Coursera

These 14 common natural language processing interview questions can help you prepare thoughtful, confident responses that show your skills and experience.

If you want to learn more about natural language processing, consider the Deep Learning Specialization from DeepLearning.AI on Coursera. You’ll learn to build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks, and apply deep learning to applications.