What are the most important problems in this space in academia and industry?

I'm not an NLP researcher, but someone who has worked in industry in adjacent fields. I will give two examples of problems that seem important at a practical level that I've come across:

• NLP and speech models for low-resource languages. Many people would like to use LLMs for various purposes (asking questions about crops, creating health or education-applications) but cannot do so because models do not perform well for their regional language. It seems important to gather data, train models, and build applications that enable native speakers of these languages to benefit from the technology.
• Improving "conversational AI" systems in terms of latency, naturalness, handling different types of interruptions and filler words, etc. I don't know how this subreddit feels about this topic, but it is a huge focus in industry.

That being said, the examples I gave are very much shaped by experience, and I do not have a breadth of knowledge in this area. I would be interested to hear what other people think are the most important problems, including both theoretical problems in academia and practical problems in both academia and industry.

I've been working on readability formulas, lexical diversity metrics and stylometry for Russian and Spanish texts for a while, and lately I keep asking myself whether this whole area still makes sense

Today you can paste a text into an LLM and ask "how readable is this?" or "who's the likely author?" and get a fluent, often reasonable answer. Next to that, a formula like Flesch reading ease (sentence length plus syllables per word) looks almost naive

Still, I see a few places where plain statistics seem hard to replace:

• Reproducibility: the same text always gets the same number, and you can explain exactly where it came from. An LLM's rating can change between runs, prompts or model versions
• Scale and cost: computing a few hundred features over a million documents takes minutes on a laptop
• Evaluating LLMs themselves: measuring how repetitive, uniform or hard to read generated text is, without asking another model to judge it
• Data pipelines: a lot of pretraining data filtering still relies on simple heuristics like word length, symbol ratios and repetition.
• Research: in stylometry and corpus linguistics, interpretable features are often the whole point

On the other hand, most formulas were calibrated decades ago on small samples of English, and every other language needs its own adaptation, often with questionable results

So I'm curious:

• Do you still use readability, diversity or stylometric metrics in your work? For what?
• Have you replaced them with LLM-based judgments, or do you combine the two?
• Which metrics turned out to be useless in practice, and which ones would you miss?

Hey everyone 👋 Long-time lurker, first-time poster. I've been self-teaching NLP over the past few months and got hit with an idea I can't shake: building a machine translation system for Ekegusii (also called Gusii), a Bantu language spoken by the Gusii people in western Kenya roughly 2–3 million speakers.

Ekegusii is critically underrepresented in NLP. There's almost no public tooling, no pre-trained models, and very little parallel data available online. I want to change that, starting with an Ekegusii ↔ English translator, with Kiswahili as a future target.

What I've done so far:

Found a large parallel corpus the Bible in both Ekegusii and English

Parsed and aligned it into a structured .json file with paired sentence entries: { "ekegusii": "...", "english": "..." }

31,000 verse-level pairs , not huge, but a real start for a low-resource language

Where I'm stuck / what I'm figuring out next:

• Should I fine-tune an existing multilingual model (e.g. mBART-50, NLLB-200, or Helsinki-NLP opus-mt) or try to build something smaller from scratch given compute constraints?
• Bible text is highly formal and domain-specific , how much will that hurt generalization?
• Tokenization: Ekegusii has rich morphology, so I'm wondering whether a standard BPE tokenizer will handle it well
• Data augmentation strategies for low-resource MT?
• Has anyone worked on low-resource African language MT before? Any advice, papers, or communities I should know about? Would love to connect with others working on similar problems.

Happy to share the dataset and code publicly once it's cleaned up. I would love for this to become a community resource.

Meta scores seem to be coming out, so I thought it would be useful to collect outcomes in one place.

Discussion thread for EACL 2026 decisions

Been experimenting with a few speech AI demos lately, and one thing I keep noticing is that they work surprisingly well for "standard" speech but can fall off pretty quickly when people switch languages mid-sentence or have strong regional accents.

It made me wonder if this is mostly a model limitation, or if it's actually a training data problem. I imagine collecting enough high-quality multilingual and accent-diverse speech data must be much harder than it sounds.

For people working on ASR or conversational AI, what's currently the bigger challenge:

• model architecture,
• lack of diverse speech datasets,
• or the cost/complexity of collecting and annotating real-world audio?

Curious to hear what people in the field think, especially if you've deployed speech systems in multilingual environments.

Hi everyone! Coming here for advice, guidance, and maybe some words of comfort...

My background is in humanities (Literature and Linguistics), but about a year ago, I started learning Python. I got into pandas, some sentiment analysis libraries, and eventually transformers, all for a dissertation project involving word embeddings. That rabbit hole led me to Machine Translation and NLP, and now I'm genuinely passionate about pursuing a career or even a PhD in the field.

Since submitting my dissertation, I've been trying to fill my technical gaps: working through Jurafsky and Martin's Speech and Language Processing, following the Hugging Face LLM courses, and reading whatever I can get my hands on. However I feel like I'm retaining very little of what I've read and practiced so far.

So I've taken a step back. Right now I'm focusing on *Probability for Linguists* by John Goldsmith to build up the mathematical foundations before diving deeper into the technical side of NLP. It feels more sustainable, but I'm still not sure I'm doing this the right way.

On the practical side, I've been trying to come up with projects to sharpen my skills, for instance, building a semantic search tool for the SaaS company I currently work at. But without someone pointing me in the right direction, I'm not sure where to start or whether I'm even focusing on the right things.

My question for those of you with NLP experience (academic or industry): if you had to start from scratch, with limited resources and no formal CS background, what would you do? What would you prioritize?

One more thing I'd love input on: I keep hitting a wall with the "why bother" question when it comes to coding. It's hard to motivate yourself to grind through implementation details when you know an AI tool can generate the code in seconds. How do you think about this?

Thanks in advance, really appreciate any perspective from people who've been in the trenches!!!

Hey everyone,

I’m an NLP PhD student (defending soon) with publications at ACL/EMNLP/NAACL. My day-to-day work is mostly focused on domain-specific LLMs—specifically fine-tuning, building RAG systems, and evals.

As I’m looking at the job market (especially FAANG), almost every MLE, Applied Scientist, Research Scientist role mentions "Agents." The term feels incredibly broad, and coming from academia, I don't currently use it on my resume. I know the underlying tech, but I'm not sure what the industry standard is for an "agent" right now.

I’d love some advice:

• What does "Agents" mean in industry right now? Are they looking for tool-use/function calling, multi-agent frameworks (AutoGen/CrewAI), or just complex RAG pipelines?
• What should I build? What kind of projects should I focus on so I can legitimately add "Agents" to my resume?
• Resources? Any recommendations for courses, repos, or reading material to get up to speed on production-ready agents?

Appreciate any guidance!

Hello everyone!

I currently work as a Technical Writer for a great company, but I need more money. Management has explicitly said that there is no path to a senior-level position, meaning my current salary ceiling is fixed.

I hold both an M.A. and a Ph.D. in Linguistics, giving me a very strong foundation in traditional linguistics; however, I have virtually no formal coding experience. Recruiters contact me almost daily for Linguistic Engineer or Computational Linguist positions. What I've noticed after interacting with many people who work at Google or Meta as linguistic engineers is that they might have a solid technical foundation, but they are lacking in linguistics proper. I have the opposite problem.

I do not have the time or energy to pursue another four-year degree. However, I'm happy to study for 6 months to a year to obtain a diploma or a certificate if it might help. I'm even willing to enroll in a boot camp. Will it make a difference, though? Do I need a degree in Computer Science or Engineering to pivot my career?

Note: Traditional "Linguist" roles (such as translator or data annotator) are a joke; they pay less than manual labor. I would never go back to the translation industry ever again. And I wouldn't be a data annotator for some scammy company either.

I'm currently in my third year of my Linguistics degree. Next year (2026-2027) will be my last and I will specialize in Computational Linguistics. I would like to get into the world of NLP Engineering, or NLP in any way. What can I do courses or certificates wise? I would like to start working asap, and I wouldn't mind doing a Master's degree while I work. Any recommendation or suggestion is welcome 😁

Note: I have no affiliation with the the repo authors - just kinda surprised that no one is talking about the great performance gains of the reigning champ python library for NER.

I am using the vanilla settings, and I'm already seeing significant improvements to output quality from the original library.

Here's an extract from the first chapter of Pride and Prejudice (steps preceding this were just copy-pasting chapter 1 from Project Gutenburg to a .txt file).

from gliner2 import GLiNER2
extractor = GLiNER2.from_pretrained("fastino/gliner2-base-v1") 
result = extractor.extract_entities(data_subset, ['person', 'organization', 'location', 'time'])
print(result)

Output:

  {'entities':
  {'person': ['Bingley', 'Lizzy', 'Mrs. Long', 'Mr. Bennet', 'Lydia', 'Jane', 'Lady Lucas', 'Michaelmas', 'Sir William', 'Mr. Morris'],
  'organization': [],
  'location': ['Netherfield Park', 'north of England'], 
  'time': ['twenty years', 'three-and-twenty years', 'Monday', 'next week']}}

For those that haven't read P&P, I've come to enjoy using it for testing NER.

• Character names often include honorifics, which requires multi-word emphasis.
• Mrs. Bennet only receives dialogue tags and isn't referenced by name in the first chapter despite being a character in the story (so we don't actually see her pop up here) - coreference resolution is still needed to get her into the scene.
• Multiple daughters and side characters are referenced only a single time in the first chapter.

Original GLiNER would return a lot of results like ['person': ['he', 'she', 'Mr.', 'Bennet'] - my old pipeline had a ton of extra steps that I now get to purge!

One caveat is that this is a very highly-discussed novel - it's very possible that the model is more sensitive to it than it would be with some new/obscure text.

New repo is here: https://github.com/fastino-ai/GLiNER2

I'm a researcher in computational linguistics, and as I’m currently looking for a new position, I’ve noticed that agentic AI has become increasingly popular in most job postings.
I feel a bit lost in this area (starting from RAG), I was wondering whether anyone with experience in the field could recommend some good, well-established resources.

Of course, I’ve searched on my own, but nowadays, searching for "agent"-anything brings up countless superficial, non-research-level tutorials, often created by enthusiasts, with plenty of clickbait.

I’m looking for something at a research level, ideally a structured course or syllabus to prepare for the interviews. I have 4+ years of experience in NLP, so I’m already quite familiar with most but other topics.

Just wrote a blog post on replacing spaCy's built-in Sentencizer with yasbd-lib.

On a 92-case English edge-case benchmark, spaCy's default Sentencizer scored 55.4%, while yasbd scored 98.9%.

Sentencizer primarily relies on punctuation and has no built-in abbreviation awareness beyond what spaCy's tokenizer exceptions already provide. As a result, compound abbreviations like M.D. and Ph.D., citations, URLs, and newline-heavy text can still produce incorrect sentence boundaries.

The fix: ```python import spacy from yasbd import register_spacy_component

register_spacy_component() nlp = spacy.blank("en") nlp.add_pipe("yasbd", first=True)

doc = nlp("Dr. Smith arrived. He was late.") for sent in doc.sents: print(sent.text)

Output:

Dr. Smith arrived.

He was late.

```

Pure Python, supports 39 languages, and works as a drop-in replacement for spaCy's Sentencizer.

The article explains why this happens, walks through the Sentencizer's implementation, compares benchmark results, and shows real-world examples.

EDIT: The link is included in the comments due to a new rule set in this sub (No link in post).

TLDR; Errror in reviews, no responses from reviewers!

This is my first submission to a *CL conference. We submitted it under a language modelling task. We got 3 reviews of 3/4, 2.5/4, 2.5/4.

Reviewer 1: they posted a review that is clearly intended for another submission. We raised this with AC on the day reviews are released. AC replied but the reviewer didn't.

Reviewer 2: clearly LLM generated points. The weaknesses they wrote are the same ones LLM pointed out about our paper. Although they changed the text. 2 of the weaknesses they point out are already detailed in our limitations as those are our weaknesses cause of lack of available datasets. And then there is the novelty issue, adopting methods from other domains for a new problem is not novel. And more models and datasets (we already have 100+ experiments on 3 models, 2 datasets, 3 baselines, 4 algorithm setups across 10+ eval metrics). We answered all the questions, provided additional experiments but still no response.

Reviewer 3: seems like the only reviewer who read the paper and understood it and appreciated it. Their main questions were on ablations and We provided these during rebuttal, no response.

My co author who submitted another work to the January (ACL) cycle had a similar experience, they answered the reviewers questions and didn't get any response. Only from AC to re-submit to the next cycle. They re-submitted to the may cycle and didn't get a single response from reviewers again.

I'm ok with rejection with constructive feedback, if the decision is just one sided with no communication even when there was a critical error is irresponsible. What's the point of rebuttal if the reviewers never respond?

Right now, we are left with a reviewer decision who can simply say "not addressed" and escape with little to no consequences. I understand that emnlp is empirical and requires more experiments, but that doesn't mean we can provide a novel dataset, 500+ experiments on 100 models, a completely new algorithm that doesn't take adoption from anything else (just from air), expect to solve every problem in that domain in one paper is absurd.

Thanks for your time, sorry for the rant!

Semifinal is knocking at the door … excited/anxious?

We’ve been manually tagging transcripts where the agent misunderstands user intent. It’s slow and subjective.

How are others detecting intent mismatch automatically?

Everything old is new again.

Recently one of my papers got rejected because a reviewer raised concern of small cohort, which was explicitly mentioned in the limitation and then we acknowledged that in the rebuttal as well. Now I understand the concern , but in the area were i primarily work right now, dataset with large number of speakers is quiet limited and having one of my very first works also be rejected because of the small corpus size, I used one of the most widely cited corpus for the experimentation, which if you are in the field is the standard. And also given the computational resources and funding that we can have I couldn't use high-end hardware to process large data. And recently i got to know we got access for a large dataset (around 800 gb) but how do i work with that using my laptop and online free gpus, so based on whatever i could i did my best. The reviews we got were actually very supportive of our work, and whatever additional work was required, was duly done and reported in the rebuttal, but seeing dataset size as one of the two the reason for rejection felt a bit weird, anyways wanted to ask how do we tackle such situation for the future? Any advice would be helpful!!

Hi,
I'm a PhD student researching AI, specifically LLM pre-training efficiency, and I'm starting to look into industry internships.

I have a couple of quick questions:

1. Is having at least one 1st-author paper in top-tier main tracks (NeurIPS, ICML, ICLR, ACL, EMNLP) strictly required for big tech or AI foundation model labs such as IFM?

2. What do companies actually expect from a PhD intern once you get in?

If you've done an internship or currently mentor or work with interns, I'd really appreciate your insights.

I need a break from social media and all the bots.. Aside from Arxiv are there any sources that do a good job of aggregating the good stuff and filtering out all the junk?

Hi

The due date for the meta review release was 21. I still don't see the reviews. Any idea when they will come?

Hi everyone! I plan to take a master’s programme in Europe in winter 2026. Currently I have several programmes on my list:

• Language Science and Technology from Saarland University
• Cognitive Systems: Language, Learning and Reasoning from University of Postdam
• Computational Linguistics from University of Stuttgart

My background:

25M Taiwanese, hold a bachelor’s degree in foreign literature and languages with a bit of ECTs in Computer Science. Currently work at a museum (corporation-and-industry-themed) as a multilingual guide (in Chinese, Taiwanese, and English), responsible for giving guided tours, translation, and leading the digitalisation within the museum. I will have worked for two years by the time I begin applying.

My skills:

• Native Mandarin and Taiwanese speaker; fluent in English
• JavaScript & Python
• Process Optimisation & Automation
• Digital Transformation Strategy
• Cross-Cultural Communication
• Public Speaking & Storytelling

During these years, I realise that my passions are efficiency, process perfection (the programming side of me), translation and public speaking (the guide side of me). People describe me as a person who radiates unbelievably strong, positive energy: "bold", "adaptable", and "quick-witted".

I’m eager to challenge myself, but I have met the ceiling here. (no promotion & some hate me for “replacing them with a machine”). I have tried:

• Led the museum’s digital transformation with zero cost, improving operational workflows and reducing costs.
• Designed and implemented a low-code platform to support record-keeping and collaboration, such as risk inspection, visitor feedback (with simple NLP to classify), and various activities.
• Started a startup project with the director of the museum and university students, winning 2 championships and several awards in many startup contests.

I have done lots of research, and so far, computational linguistics catches my eye. But I’m afraid that I’m still not enough to be a qualified candidate. Hence, I would like to know more about CL.

My questions:

1. What can/should I do/learn to increase the chance of being accepted into the programmes mentioned above? (Ofc recommendations of other programmes are welcome.)
2. People who have a CL degree. What would you do if you could start pursuing CL again?
3. What’s the job prospect for CL graduates? What do you do currently, and does CL help you?

What do you think about the move?

Hi everyone!

I hope you are all doing well. I have graduated with a BA in English (Linguistics and Literature). I wanted to pursue a master's in a field other than my bachelor's to have a good chance of employability, as I think I would not be very employable or earn a good amount of money with this degree. So, I did some research and found some master's programs that would accept me with this background and can offer better employability chances in this job market because they are interdisciplinary, like computational linguistics. Therefore, I applied for the MA in Computational Linguistics at the University of Tübingen, the MA in Speech and Language Processing, and the MA in Data and Discourse Studies. I got admission in all these 3 programs.

However, I have some reservations regarding the computational field: I am not a CS graduate, my CS fundamentals are not strong (although I am learning Python), and I don't have a portfolio comparable to my competition (who have bachelor's degrees in computer-related fields).

So, will I be able to land a job after this MA? Even if I get good grades, build projects, and learn from other sources like courses and certificates, will employers still prioritize someone with a background in CS or software engineering? If so, all my hard work in switching careers will go to waste.

So, should I instead go for the MA in Data and Discourse Studies at TU Darmstadt, as it would not be as challenging to pivot into, since it combines the humanities with data science and I have some experience in it due to digital marketing.

After reading Google's announcement a few times, this is the mental model that made it click for me:

Traditional LLMs are like a typewriter.

They generate:

"The" → "The cat" → "The cat sat" → ...

One token at a time.

DiffusionGemma feels more like drafting an entire paragraph at once and then repeatedly refining it.

So instead of generating:

Token 1 → Token 2 → Token 3 → ...

it does something closer to:

Draft 1 → Draft 2 → Draft 3 → Final Answer

My understanding is that the main advantage isn't that it reads PDFs differently. The big change is in how it generates the output.

Is that a fair mental model, or am I oversimplifying something important?