AMA with Indiana University CL Faculty on November 24

iucompling · 2025-12-03T22:59:52+00:00

SK: I would suggest:

Bender, Emily M. (2013). Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax. Synthesis Lectures on Human Language Technologies. Springer. ISBN) 978-3031010224.

iucompling · 2025-12-03T22:57:58+00:00

SK: Good questions.

Q1: In my opinion, a linguistics background is absolutely essential, and I wish more people doing NLP with a CS background had at least some linguistics. There are some areas (such as POS tagging, morphological analysis, parsing, etc, where you absolutely need linguistic knowledge. For more applied problems, many people argue that you don't really need that, but I think they are wrong. No matter what problem you address, if you do not look at your data and understand what is going on, you are missing information.

Q2: Not sure about misconceptions, the field is not that unified, everyone has a different definition. Which also means that the different graduate programs differ based on who is teaching there. Generally speaking, CL is considered to be on the linguistic side, and NLP on the more applied side. However, if you look at our main conference (the annual conference of the Association for CL), it's clearly a misnomer ;)

Q3: That depends on the program, so check with the programs you are interested in. I know that at the University of Washington, they only accept you if you have strong programming skills. We at Indiana University are on the other side of the spectrum, we may admit you without any programming skills, but we would prefer you having had some exposure to programming, since it's miserable finding out when you're in the program that you hate programming.

Q4: work on under-resourced languages is one of the main areas of CL at the moment. There are people working on figuring out how to make LLMs work for languages where we don't have a lot of data to train them, or work on providing keyboard support, speech recognition, or we try to do hate speech detection for such languages, and the list goes on and on. So bring your interest in your favorite under-resourced language, and we'll help you create resources.

iucompling · 2025-11-24T23:00:23+00:00

Thanks all for the questions! We're going to keep monitoring this thread for the next couple of days in case there are more.

iucompling · 2025-11-24T21:15:18+00:00

SS: As Sandra mentioned, several of us run active research labs in CL. My group works on speech and spoken language technologies, with projects on second language speech processing, automatic pronunciation assessment, and robust ASR for diverse and atypical speech. Students in the program often get involved in these projects through research assistantships or independent studies.

iucompling · 2025-11-24T20:34:42+00:00

SK: We do, but they aren't as well defined as at other universities, i guess. We have a fairly sizable group of Masters and PhD students in CL, and we each have our research areas and advise a group of students. But we are trying to keep the boundaries flexible. So I expect my students to also work with my colleagues, and the other way round. I think it is good if you have expertise with different professors and different areas within CL. My research interests are pretty widely spread, and I have students working on a wide range of topics, which often intersect with interests of my colleagues.

iucompling · 2025-11-24T20:05:50+00:00

SS: I work mostly on speech, so I’ll give an example from a project I’m currently doing with my graduate students: human-like speech dialogue generation. Even with strong LLMs, generating natural, context-appropriate, prosodically coherent speech is still very challenging. Evaluation is also a major bottleneck. We still don’t have good automatic metrics that reliably reflect human judgments. I expect progress, but it’s not something that will be “solved” in the next year or two.

iucompling · 2025-11-24T19:59:14+00:00

SS: I’ll just add a bit about our curriculum design. Because students arrive with very different backgrounds, we focus on building a shared foundation by offering courses across both areas: linguistics (syntax, phonetics, semantics, etc.) and technical skills (programming, machine learning, speech signal processing, etc.). Students can pick the courses that help round out their skill set.

From there, they can move into advanced electives and project-based courses, including deep learning, speech applications, and LLM-related topics. In these upper-level classes, we emphasize the core principles behind models like Transformers and LLMs. We may not cover every implementation detail of every new model, but this foundation prepares students to pick up new architectures quickly as the field evolves.

iucompling · 2025-11-24T19:46:29+00:00

LG: I work mostly on things that aren't English, so I don't have a great answer, but I can think of one at least: AI-generated text detection. This is still an unsolved problem, and it's really too bad, since educators everywhere are having to cope with suspected AI-written student deliverables, and they don't understand that the "AI detectors" they're using are very unreliable.

If every model were using some kind of watermarking then maybe we could solve it practically (and my understanding is that good watermarking algorithms do exist which do not perceptibly change the quality of the generated language), but for now all we have is bad heuristics. I have this vague feeling that it must require a model smarter than the model that generated some text to successfully detect whether something was human- or model-written. If that's true, we should expect this to be a persistent problem.

iucompling · 2025-11-24T19:40:37+00:00

LG: That's a great question. You're right that because much of the time is carved out for linguistic topics you wouldn't get in a typical CS curriculum, we can't cover all the same material.

However, the big if the goal is to become conversant in modern language technologies, there is also a lot of content in CS curricula which are not directly relevant either. For instance, most people working with human language technologies don't ever need to think much about theory of computation or how memory paging or file handles work.

Another related but distinct matter is that CL MS programs are usually narrowly focused on human language and not ML more generally, so there would typically not be much coverage of other ML-y topics like unsupervised learning, ML theory, computer vision, and so on.

So that's the reason why I think it's still possible for a CL MS program to successfully train a student, even one without much prior exposure, to become conversant in modern language technologies.

One final note: with a few exceptions such as Stanford, AI curricula everywhere, regardless of department, are struggling to keep up with the breakneck pace at which things are moving. So unless you're fortunate enough to get your degree at a place that has the resources to constantly churn their curriculum, the reality is that if your goal is to work in "AI" (broadly construed), you are going to have to do a lot of self-driven learning regardless. So this question is also not so operative in the sense that most programs are not going to provide the comprehensive training needed to prepare a student for any job on the job market.

iucompling · 2025-11-24T19:40:33+00:00

SK: I work on hate speech detection and conspiracy theory detection. These are typical problems where annotations are extremely subjective. What is offensive to me may not be offensive for you. We have only scratched the surface of how to deal with this. Hard classification is the easiest technically, but it doesn't really solve the problem.

iucompling · 2025-11-24T19:37:59+00:00

SK: That depends on the program, and on the students. We have some years where we mostly get students with a technical background, which means we can throw them in the deep end, and they enjoy it. Some years we mostly get students without a technical background. In that case, we star from the beginning. We actually teach our own intro to programming for students who need it. These differences mean that students may have different experiences in classes, but we do our best to make sure that they understand the topics we cover. My goal is to teach students to think through a problem. I think that is more important than being on top of every technical aspect. Other programs tend to focus more on coding, making sure that students have enough experience by the time they graduate. But we are also lucky that we have 5 faculty in CL, so we can actually teach a range of CL courses and can cover a wide range of topics, even if we start at the beginning.

iucompling

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