The Best Questions To Ask in NLP Engineer Interviews

When you’re hiring a natural language processing (NLP) engineer, your goal is to bring on the right person with the knowledge and experience that can best help your team. However, the landscape of NLP work has rapidly changed and evolved over the last several years due to technological advancements.

This guide will show you the right NLP interview questions to ask in interviews as you evaluate different candidates. By the time you’re done reading, you’ll know exactly what you need to do to feel fully confident about the data scientist you’re adding to your team.

Table of contents:

1. NLP engineer interview basics
2. Text preprocessing
3. Advanced algorithms and models
4. NLP tasks and applications
5. Evaluation metrics and performance
6. Tools and libraries
7. Future trends and challenges

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NLP engineer interview basics

At some point during the interview (likely at the beginning), you’ll want to ask questions to get a feel for the candidate’s general knowledge of computer science and natural language understanding (NLU) related topics. Here are some straightforward NLP interview questions to consider.

What experience do you have working with Python?

Python is a programming language often used in data analysis and building software. Python is essential for completing various NLP tasks, and many common Python tools and libraries are found inside the natural language toolkit (NLTK). This open-source forum is full of libraries, programs, and helpful resources to produce NLP programs.

What do you know about machine learning?

In some ways, natural language processing is a subset of a larger discipline called machine learning, and both play a critical role in the development of artificial intelligence (AI) software. An NLP engineer should have a solid understanding of machine learning and its relationship with NLU and data science.

Have you ever done any statistical analysis?

Statistical analysis involves collecting and studying large volumes of data to spot trends and provide helpful insights and conclusions. Since NLP models often use statistical data to improve their operational capacity, an effective NLP engineer will understand how to incorporate data science into the machine learning algorithms they build.

Text preprocessing

Text processing involves automating various tasks through the analysis of electronic text. Since NLP encompasses the syntactic interpretation of conversational language in a way computer algorithms can understand, proficiency in this area is essential for NLP engineers. To assess a candidate’s qualifications in this area, consider asking questions like the ones below.

What words would you consider essential “stop words”?

Stop words are frequently used articles and prepositions that search engines are trained to remove. If these words were left in search queries, they would require additional processing time and take up unnecessary database space. The specific words that a system considers “stop words” can vary from system to system, and it’s interesting to hear each engineer’s thoughts on what words should make this list for the best possible syntactic interpretation.

Do you prefer to use stemming or lemmatization?

Stemming breaks down various forms of a word into the basic root word. Because of stemming, a computer will understand English words like “organizing,” “organized,” and “organizer,” all based on the same base form of “organize.” Using base forms aids in normalization, makes text easier to process, and allows the end result to happen more quickly.

Lemmatizing is similar in that it helps with normalization. Its difference from stemming, however, is that lemmatizing produces a more coherent word or sentence that is more natural and easy to understand. Usually, stemming is preferred when speed is the end goal, while lemmatization is better when you’re hoping to create an end result with a grammatical structure that is understandable for the average person.

Another relevant topic is word embedding, or converting words into numeric representations. Word embedding allows words with similar meanings to have the same numerical assignment or representation. When you incorporate word embedding, you can process data more quickly. You may also talk about part-of-speech (POS) tagging here, but POS tagging could also be relevant within NLP tasks and applications.

How does tokenization impact the rest of your NLP pipeline?

Tokenization is usually viewed as the first component of an effective NLP pipeline. Through tokenization, large chunks of text are broken down into individual words, sentences, and other meaningful elements for better semantic analysis. In some cases, tokens may be referred to as n-grams, with the “n” representing the number of words, texts, or symbols in succession.

Asking a question about n-grams or tokenization will help you see how a candidate usually structures their tokenization process and uses it to feed into the rest of their syntactic and semantic analysis. You’ll also gain clarity about how they could use tokenization to support the best possible syntax or machine translation.

Advanced algorithms and models

Having a dialogue with questions and answers about advanced language models will help you evaluate a candidate’s knowledge and experience in key tasks relevant to NLP engineers.

What neural networks have you built in the past?

Artificial neural networks are a sub-field of machine learning. They enable computers to process datasets and allow them to gradually improve their functionality over time through training. These systems can recognize patterns and solve common problems. As the baseline of artificial intelligence and a key branch of machine learning, this skill set is essential for working in NLP.

When you ask a candidate about what neural networks they have built or have experience working on, you’ll learn about their skills and experience in this area while also gaining insight into their beliefs and perspectives on the topic.

Do you prefer supervised or unsupervised deep learning?

Deep learning automatically processes and extracts valuable findings from datasets, but how it works depends upon the level of supervision or oversight the algorithms need. Data from unsupervised systems is raw and unlabeled. These systems require less ongoing supervision or intervention, and can glean powerful insights from large quantities of data, but the algorithms may be less predictable or accurate after training.

On the other hand, supervised deep learning models require more attention but can produce more refined systems. They are trained on a process- or rule-based system that annotates or labels the unstructured data, and have a clear optimization objective. This enables them to be trained until their output meets a specified criteria.

This may also be the time to discuss information extraction, or the automated selection of specific data points from a body of text. Since information extraction involves developing and using specific methods, it’s a relevant topic to address alongside deep learning. Remember that this is different from information retrieval, or the process of returning information to the user.

How familiar are you with transformer models?

Transformer models are specific neural networks that learn how to track relationships in various types of data. They use advanced mathematical techniques to spot various data elements and how they influence each other. They have been growing in popularity since first introduced in a Google paper in 2017 and are a key player in driving advances within AI.

NLP tasks and applications

These NLP interview questions will allow your candidate to share how they could achieve specific goals or outcomes by putting their knowledge and experience within NLP into practice.

How do you incorporate sentiment analysis?

Sentiment analysis goes beyond the text on the page and seeks to interpret the tone behind the message. It enables NLP systems to evaluate whether a message is positive, negative, or neutral, which impacts the response it generates. Sentiment analysis also helps capture information objectively, although further training may be necessary to ensure that systems avoid any potential biases.

To take your conversation a step further, you can make connections to related disciplines such as semantics analysis, dependency parsing, or part-of-speech tagging. These are all important components of text summarization and speech recognition.

What is your preferred method for NER training?

Named entity recognition (NER) is a component of NLP that involves identifying key information within the text and fitting it into a set of predetermined categories. To incorporate NER, the engineer could train the language model for multi-class classification or implement a conditional random field through NLP speech tagger and the natural language toolkit. Each option has its benefits and downsides, and you can learn a lot about a candidate by hearing what approach they might take.

How many NLP chatbots have you created in the past?

NLP chatbots have the potential to understand and respond to human prompts in a way that mimics natural conversation with human language. To take this question further, you could ask the candidate to describe one specific chatbot they are especially proud of or one they wish they would have developed differently based on the experience they have now.

Evaluation metrics and performance

A great NLP engineer will ensure that your existing systems run smoothly while also looking for ways to improve them. These questions and answers will help you understand how effectively a candidate could assess the effectiveness of NLP models and enable them to run even better than before.

How do you improve existing TF-IDF models?

Term frequency-inverse document frequency (TF-IDF) measures how relevant a particular word is inside a sentence, paragraph, or document. The rating will increase the more often a word occurs, and the general commonality of the word is also considered.

By improving TD-IDF models, you’ll create NLP systems that are more efficient and effective. When you ask a candidate this question, you’ll have a chance to gauge what they already know about TF-IDF while also hearing your thoughts about how the tool can be used in the best way possible.

You may also ask about candidates’ understanding of the bag of words model since the bag of words also has to do with the frequency of word occurrence. Latent semantic indexing may also be relevant here.

What data points are most important to review?

There are a number of data points you can consider when measuring the current effectiveness of NLP processing tools and models. Typically, these fall into two categories: intrinsic and extrinsic evaluators. Intrinsic evaluators consider the success of specific subtasks, and extrinsic evaluators look at the system as a whole.

Regardless, when you ask this question, you’ll learn what the candidate values in system evaluation and what they will specifically measure and analyze as they look to improve your processes.

Tell us about a time when you significantly increased the performance of an NLP system

While the above questions are more theoretical in nature, this question gives the candidate a chance to flex their muscles and share one of their greatest success stories. As the interviewer, you’ll get to hear about their real-world experience in NLP engineering, which gives you a chance to consider the value they could bring to your organization or team.

Tools and libraries

These questions will help you understand how candidates use various resources and tools to improve their work and enhance their learning. If you interview several candidates, you can develop a robust list of resources for future use simply by learning more about what others in the field are using.

How do you use NLTK and spaCY in NLP tasks?

We’ve already discussed the natural language toolkit (NLTK) above. spaCy is an open-source library that helps NLP and machine learning engineers do real work with a simple and easy-to-install library and a variety of plugins. Learning how familiar a candidate is with each of these tools will give you a greater understanding of how they could enhance their effectiveness by using these resources.  

What open-source contributions have you made to the NLP community?

This question goes beyond the first question about familiarity with networks, and probes to consider what contributions they have made to various open-source databases in the past. This will reveal both their proficiency in improving code that others have built, as well as their desire to share their knowledge and insight with others.

What other tools and resources do you find most helpful?

This question will help you see how the candidate includes other resources in their work while also giving you a broader knowledge base of what tools exist to help with NLP work. You’ll better understand how they are working to broaden their knowledge and sharpen their skills by hearing about what books, websites, blogs, and other avenues they are using to enhance their abilities.

Future trends and challenges

The past few years have represented major growth and development in the field of NLP, and the landscape should continue to shift in the coming years as well. Before you wrap up the interview, take some time to hear each candidate’s thoughts on the industry's future.

What’s your take on the future of GPT models?

Generative pre-trained transformers (GPT) models such as the popular ChatGPT have impacted the world of AI. Improvements continue to take shape and enhance the capabilities of what this form of AI can do.

As you hear each candidate’s thoughts on how these models could continue to deepen and develop, you’ll get a sense of what they think could be possible in the field as well as what role they think they could play in these ongoing advancements.

How do you see the NLP landscape evolving?

This question will give you a chance to hear about how the candidate sees the field changing in the near or distant future. This may impact the way that they work, or it could simply reflect their beliefs, hopes, or expectations for future growth or adaptation in the industry.

How could AI technology look different in 10 years?

While impossible to predict with full accuracy, this question sheds light on what the candidate sees as the future of the field. It will also give you a sense of whether they are generally optimistic about NLP and machine learning or if they are somewhat cynical or cautious about the future.

Find talented NLP engineers on Upwork

Throughout this article, we’ve covered several potential interview questions as well as the reasoning behind bringing up these topics with NLP engineers. The steps in this article can help you facilitate productive conversations with candidates in a way that helps you make decisions about who might be the best fit to join your team.

If you’re still looking for candidates to interview, you’ll find many qualified individuals online at Upwork. Consider using Upwork to look for the best NLP engineers to hire. With Upwork, you’ll be one step closer to taking your team’s NLP expertise and data science capabilities to the next level.

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Disclosure: Upwork is an OpenAI partner, giving OpenAI customers and other businesses direct access to trusted expert independent professionals experienced in working with OpenAI technologies.

Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.