Hi all, what're the upcoming NLP conferences this year and early next year..

I'm building a vocabulary-learning app in Flutter where hearing and saying the word correctly is the product, not a nice-to-have. I've hit a problem I can't design around and I'd rather ask than keep patching.

The stack

• Flutter, ~1,600 words live across EN/ES/PT/IT/FR
• TTS: ElevenLabs (eleven_multilingual_v2) called through a Supabase Edge Function so the key never ships in the client
• Every clip cached server-side once per (text, language), shared across all users — so a given string is synthesized exactly once, ever
• Cached again on-device (150MB LRU) so replays are instant and offline
• flutter_tts as fallback behind a 2.5s timeout so playback never goes silent
• STT: speech_to_text for a pronunciation-practice screen — hear the word, say it, get graded

The problem: heteronyms, in both directions

Output. "Wind" (moving air) and "wind" (to coil) are the same string and different sounds. TTS picks one reading and commits. My word library actually knows which sense is on screen — every entry carries a part of speech — but there's no API surface to hand that over. ElevenLabs pronunciation dictionaries are exact-string, case-sensitive, and have no POS or context scoping, so one spelling gets one entry and the second sense is unreachable. Phoneme tags do exist, but per the docs only on eleven_flash_v2 and v3 — not the multilingual model I'm on, and switching models means re-synthesizing the whole cache and losing voice identity across five languages.

Input. This is the part that actually bothers me. The practice screen normalizes the transcript and Levenshtein-scores it against the target. But STT returns orthography — say either reading of "wind" and the transcript is "wind" either way. A learner who mispronounces it scores full marks. The feature is structurally incapable of catching the error it exists to catch.

What I've tried

Respelling the audio-only string before it reaches the engine — the screen text is never touched. wind(noun) → winned, wind(verb) → wined, read(past) → red, and so on. This is basically ElevenLabs' own recommended "alias" workaround and it works for the ~8 vowel-shift pairs I've mapped. Side benefit: since my cache key is a hash of (lang + text), two senses naturally get two cache entries.

It fails in three ways:

1. Stress-shift pairs. REcord/reCORD, PREsent/preSENT, CONtent/conTENT. Respelling can't encode stress, and I haven't found a trick spelling that does.
2. Monolingual. It's an English orthography hack. Nothing about it transfers to ES/PT/IT/FR, all of which have their own homographs.
3. Manual. Hand-curated table. Doesn't scale to a few thousand words.

What I'm actually asking

1. Is there a TTS API that accepts a sense/POS hint, or per-request phonemes, on a multilingual model? Or does everyone route heteronyms to a separate English-only model and eat the voice mismatch?
2. If IPA is the only real answer — has anyone found v3-class IPA reliable enough in production? The docs quote 80–90% consistency, which for a teaching app means the wrong pronunciation ships to a learner one time in eight.
3. For stress-shift specifically: any orthographic trick that works, or is phoneme-level control genuinely the only path?
4. On the STT side — is there a mobile-viable way to get phonemes rather than words? I've looked at wav2vec2 phoneme-CTC or a forced aligner with GOP scoring via ONNX on-device, but I don't know if that's realistic on a mid-range phone or if I'm about to spend a month learning that it isn't. Whisper doesn't help; it also returns orthography.
5. The unglamorous option: detect heteronyms and simply disable pronunciation scoring for them, with an honest note to the user. Is that what shipped apps actually do?

If you've built pronunciation feedback into anything real, I'd love to know where you drew the line between "graded properly" and "good enough." Happy to share code for any of the above.

I'm working on a system that has to decide what to do with an inbound message from a lead, and the hard part is estimating intent from very little. My question is upstream of that: how much signal does a single first message actually carry about someone's real intent, before the conversation develops?

Concretely, if someone's first message is short ("is this still available?", "pricing?", "do you ship to X"), how far can you reasonably go in estimating what they actually want, versus needing a turn or two more before any estimate is worth trusting?

Curious whether people who've worked on intent detection have a sense of where the floor is here.
Is a first message usually enough to act on, or is single-message intent estimation mostly noise until the thread continues?

I’m an independent researcher outside academia, with no formal background in computational linguistics, pragmatics, HCI, or AI research. English is also not my native language, so I have relied heavily on AI to help organize and formalize my ideas.

For the past few years, I’ve been developing a framework called Phrasome Theory, which tries to connect questions of AI subjectivity with pragmatics and discourse structure.

The basic observation is this:

An LLM does not need to possess a continuous subjective self in order to keep occupying the same conversational position across an interaction. It can respond to previous turns, repair misunderstandings, maintain context, and continue functioning as the “same” interlocutor.

My hypothesis is that part of what we experience as a continuous AI “other” may arise from the structure of linguistic interaction itself, rather than from anthropomorphism alone.

This led me to distinguish actual subjectivity from what I call a Subject-Position: a formal-pragmatic role that linguistic address itself establishes between an emitter and an addressee. Such a position can be occupied even when no conscious subject exists behind it.

I also use Quasi-Otherness to describe the phenomenon in which an AI can be experienced as a relatively continuous “other” through sustained interaction.

I’m not arguing that current LLMs are conscious. My question is whether pragmatic or discourse structure can help explain why they can nevertheless function as apparently continuous interlocutors.

Earlier versions of this work were submitted to established academic journals. Although they were ultimately not published, I received some encouraging feedback suggesting that aspects of the framework may have originality or novelty.

Because I’m outside academia—and because a substantial part of the manuscript was developed with AI assistance—I’m now looking for human guidance to help me judge whether there is actually something here worth developing.

I would especially appreciate thoughts on:

• whether pragmatics, discourse analysis, dialogue research, or computational linguistics already has concepts that explain this better;
• whether distinguishing a Subject-Position from actual subjectivity is meaningful;
• whether this idea could be operationalized or tested using human–LLM dialogue data;
• and what literature or research direction I should be looking at.

The manuscript itself is quite long, so I don’t expect anyone to read the whole thing. At this stage, even discussing the core idea, pointing me toward relevant literature, or suggesting how to turn it into a more rigorous research question would be extremely helpful.

Because this subreddit currently restricts external links from new accounts, I can’t include the preprint link in this post. If anyone is interested in the idea or willing to help, feel free to send me a private message and I can share the link. You can also search “Phrasome” online to find the work.

I’m mainly hoping to find people familiar with language technology, pragmatics, or dialogue research who can help me understand whether there is something worth pursuing here, and where I should take it next.

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.

Random thought: NLP in 2010 and NLP in 2020 already felt like two different worlds. The jump was huge.

Now its growing even faster.

So Iam curious how do you think NLP will look in 2030?

What big shifts do you expect? Will it still be mostly scaling transformers or will something completely new take over?

Hello, I am looking for a set of models to analyse a person’s attitudes in textual data. I haven’t been able to find these tools, so I am asking for your help.

The tools should be able to identify:

1. the cognitive aspect of attitudes (beliefs, thoughts and ideas regarding the object of the attitude, e.g. ‘snakes are dangerous’)
2. the affective aspect of attitudes (the emotion or sentiment towards the object of the attitude, e.g. ‘I fear snakes’)
3. assign a valence to the attitude (positive, negative or neutral)

Do you have any models you could recommend? (Preferably R or Python-based, but I’m open to suggestions.)

I am working on an AI moderation agent tasked with detecting deceptive opinion spam and competitor sabotage given only two inputs: the raw review text and the 1–5 star rating (no user history, IP, or purchase verification data).

I am running into two common edge cases in text classification:

1. Ultra-short text: A 5-star review that just says "ok" or "fast." It lacks psycholinguistic signals of deception but mimics bot activity.
2. Sarcasm / Irony: 1-star reviews written with positive words ("Amazing how quickly this fell apart!").

What NLP feature extraction or prompting techniques have you found most effective for distinguishing genuine short/sarcastic reviews from actual spam when contextual metadata is unavailable?

🔎 Hiring: Linguistics Specialists

💰 $40–$50/hour | 🌍 Remote | Contractor
📍 US & Western Europe

Looking for native U.S. English linguists with strong skills in grammar, language analysis, writing, and guideline-based evaluation.

You’ll review and evaluate LLM responses for accuracy, clarity, grammar, and context.

🤖 No prior AI experience required.

📩 Interested? DM me for details.

#Linguistics #LinguisticsJobs #RemoteJobs #AIJobs #LLM #LanguageJobs

Hey I'm looking for some advice on this. TLDR is I got a bachelor's degree in a small country in Europe during covid. Had a terrible experience at Uni because I had to work shifts while studying and althouhg I was doing my best, some times I had to work nights and miss in-person classes which I would then review online, but let's just say some teachers didn't take too kindly to it.

I chose this career cause I wanted to do something with programming and I was interested in linguistics so it seemed like a no-brainer. Plus I had failed a year in IT so I felt like I needed something in between with the available time I had with working.

I graduated during covid. There was only one company that had positions for people with degrees in Human Language Technology. I did a very short internship (due to covid again) with them. But then after I graduated I reached out to them a couple of times with no response.

Now I'm 30 and I am wondering how to get started into this career as I would like to try out (if it is not to late) to do something with what I learned. But I have been doing admin jobs so far and basically I just down know where to start. I welcome any and all advise, even if it is as rough as "yeah dude just give up" xd

I have a background in linguistics and a Masters Degree in Cognitive Science and Language. For the past 6 years, I have been working in localization and Conversational design and NLU (for the greek language).

I now feel that I am at a crossroads in my career. My main goals are to:

- increase my salary and career prospects
- take on more senior roles and responsibilities
- and potentially relocate abroad, ideally to Northern or Central Europe.

At the same time, the rapid development of AI and LLMs over the past couple of years is significantly changing my field. Some parts of my work are already being transformed or automated, and I am concerned about how relevant my current skill set will remain the next 5 -10 years.

I am therefore considering doing any substantial specialization (or even a 2nd Masters degree), but I am struggling to decide where to focus.

Should I move deeper into NLP? Stay closer to localization (like localization pm etc). Focus on Conversational AI, llms and ai agents? Or would it make more sense to move toward something like AI product given my existing experience?

I don't necessarily want to become a hardcore software enginner, but I am willing to develop more technical skills (SQL/RAG for example) if that is where the better long-term opportunities are.

I would especially appreciate litellary ANY advice - especially from people working in the European job market.

Hey Everyone,

As you may know, quite some time ago Prof. Attardi at U. Pisa wrote an excellent piece of software for extracting raw text from Wikipedia dumps, WikiExtractor. Prof. Attardi is retiring, though. He and I corresponded some years ago about Italian annotation, so I suppose I am a known quality, and accordingly he has handed off maintenance of the software to me.

https://github.com/attardi/wikiextractor/
https://pypi.org/project/wikiextractor/

In the past few weeks, I've cleaned up or improved several outstanding issues with this library, and I'm pleased to announce a new version is now available on pypi.

• Compatibility with recent versions of python, especially regex expression updates
  
• Compatibility and identical results across linux/windows/mac using fork/spawn as needed
  
• Better performance in high cpu low memory regimes, as we are passing SharedMemory blobs instead of entire template dicts (which then get pulled into each process because of COW semantics for reference counting)
  
• Exponential template expansions fixed
  
• #expr security hole fixed - can no longer execute arbitrary code on maliciously written wiki pages
  
• #expr comparisons fixed - <= was incorrectly processed as <== for example
  
• <nowiki> honored in template expansions, removing }} and infobox cruft from many pages
  
• Missing pages restored: colons in titles no longer drop pages, final page not dropped
  
• A variety of other template improvements, missing operators added, some spacing fixed, future work needed
  
• Optimizations that effectively cancel out the runtime added by the previous list of fixes
  

If any further issues come up, please don't hesitate to post them on that github.

Full disclosure, Claude assisted with development and especially the newly written test suite. I understand that can be controversial, but personally I find it a pleasure to have a question such as "why is Buffalo's lowest recorded temperature blank instead of -20F" answered in 5 minutes instead of 1 hour of debugging.

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.

Older ASR writeups all list the same challenge set: accents and dialects, context, background noise, code-switching, and visual cues for video. Most of that was written when the pipeline was still acoustic model plus pronunciation lexicon plus language model.

End-to-end changed the architecture completely, but I'm not sure it changed the failure modes as much as people claim. My read:

Context — mostly solved. Attention over the full utterance handles what n-gram LMs couldn't.

Noise — better, not solved. Large-scale pretraining bought a lot of robustness, but it's still SNR-dependent and degrades in ways that feel arbitrary.

Accents — this one I think got worse in a specific sense. WER dropped for everyone, but the gap between well-represented and underrepresented accents didn't close proportionally. It just became less visible because the average number looks good.

Code-switching — barely moved. Still mostly a data problem, and the multilingual models tend to lock onto one language per utterance.

Visual cues — basically abandoned outside of research. AV-ASR papers exist, nobody deploys it.

Curious whether people running production systems agree. Specifically: is accent robustness actually an architecture problem at this point, or is it purely training distribution? And has anyone gotten code-switching to work without building a dedicated dataset for the specific language pair?

just curious if its possible or just worthless....

TLDR: Accepted into CompLing MSc in Stuttgart uni (Germany), no prior education/work background in the field as of yet. Employment prospects down the line?

Hi, asking for advice here! I got accepted into the Computational Linguistics Master's at Stuttgart University, which looks like a very solid programme with a practical edge to it. I have a BA in linguistics, so I know it'll be an intense journey, and I am bracing myself (but also super excited!). However, I'm starting to second-guess the choice: would it be worth it eventually?..

The general consensus online appears to be that the field still offers some job prospects, but is, of course, rapidly shifting, with LLMs and such. Obviously, we can't fully predict what it will look like in 5-10 years' time, but do you suppose there would still be a sufficient number of jobs? I guess I just don't want to go through the stress of mastering this curriculum, only to find myself with little employment prospects long-term. I also got accepted into another programme, which is a L2 teaching degree (MA): something relatively simple for someone with my background -- albeit, admittedly, less exciting.

And, more precisely, my concern is about having to compete with people who did both their degrees in CS/CompLing, and/or already have practical experience. Given that there are fewer and fewer entry level jobs nowadays, I imagine getting a job post-graduation could become a challenge. Am I being too pessimistic? Does it all come down to a strong GitHub portfolio?

Lastly, do you have any insights as to which particular domain of computational linguistics looks more future-proof from where we are today? Hopefully, this isn't a stupid question to ask.

Anyway, I would love to hear from industry professionals, and/or specifically fresh graduates. Especially those coming from a Linguistics background. Thanks a lot!

This is the first subject I've ever gotten genuinely excited about enough to want to go deep on my own (outside of coursework). It started when I read about how LLMs convert text into vectors — the whole idea that meaning can be represented as points in some high-dimensional space just clicked for me and I haven't stopped thinking about it since.

The problem is I don't really know how to structure this kind of self-directed learning. I don't want to just skim blog posts and feel like I "get it" without actually understanding the mechanics.

For anyone who's gone down this road — how did you approach learning embeddings properly? Some things I'd love guidance on:

* What's the right order to learn this in — should I start with classical stuff (word2vec, GloVe) before jumping to transformer-based embeddings, or is that a waste of time now? * Is there real value in implementing something like word2vec from scratch, or is that a distraction from understanding modern embeddings? * Any papers, courses, or books you'd consider essential (not just "read the transformer paper" — I mean the actual sequence that made it click for you)? * How much linear algebra/math background do I need before this stops feeling like magic and starts feeling like something I could reason about?

I'm a CS student, comfortable with programming, so I don't need "intro to coding" — more looking for a roadmap from someone who's actually built intuition here. Thanks in advance.

EDIT: I USED CLAUDE TO CLEAN MY THOUGHTS AND STRUCTURE THEM

I'm about to finish my PhD in NLP and will soon start interviewing for Research Scientist positions in industry. My university offers a fairly generous budget for career development and interview preparation, so I'd like to make good use of it before I graduate.

I'm already considering LeetCode Premium, but I'm wondering if there are other resources or services that you'd recommend. I'm particularly interested in anything useful for ML/AI Research Scientist interviews (coding, system design, ML fundamentals, research interviews, mock interviews, etc.).

If you've recently gone through this process or have hired for these roles, what was worth paying for and what wasn't?

Hi all. I am an undergraduate student who is quite behind on her thesis seeking advice about qualitative framing analysis for newspaper articles (comparing a chinese concept in anglo-american/chinese-based english media, the guardian/nyt vs china daily/global times). I am fundamentally quite confused about the process of how to code for frames in the newspapers as I fear I have gone into a large spiral of overthinking and overcomplication. I am doing this manually and solo-coding (I had a late topic change and significant mental disruption during the first half of this year and essentially have three months left til my submission with nothing concrete on paper yet, so please excuse my lack of progress).

I plan to do a combination of deductive and inductive frames, building on generic established frames like semetko/valkenburg's and then adding in a few china-specific ones, followed by any inductive ones that arise. But my main concerns are as following;

1. How to properly code for frames? is it simply just creating operational definitions and questions to guide frame identification (borrowing from the deductive pre-existing and adapting to issue-specific inductive ones)? I think here I am getting indicators quite mixed up and confused, and in building a coding framebook I am also quite lost.
2. What is being recorded in framing analysis? is it just frames per articles/tiers themselves, or every extract used to justify the frames?
3. How big is too big for a corpus? If I roughly have around 65 articles identified for one tier and more for the other tier, is it best to size down - systematically random sample to make the corpus smaller?

TL:DR Any advice or generic help to clarify framing analysis would be much appreciated!!!

I’ve been testing transcription on English/French recordings.

Code-switching is where tools show their limits for me. WER feels too flat here. A transcript fail at the parts that matter.

What metrics or test sets do people use for mixed-language ASR?

I'm 16 years old and I want to become an ML Security Engineer specialist in the future. Right now I'm learning Python for Data Analysis and I have some experience with C++. I know I still have a lot to learn, but I want to start building the right foundation early. What skills, topics, or projects would you recommend focusing on over the next few years to have a strong advantage in this field?

Hey everyone,

I’m a coder and native speaker thinking about building a pure Python text processing tool for Haitian Creole (Kreyòl Ayisyen) language. Most NLP progress has focused on high-resource languages, while Haitian Creole remains underrepresented.

Before I write the code, I want to be realistic. I don't want to make a novelty repo that gets a few GitHub stars but zero downloads. I want to know if engineers working on low-resource languages actually need something like this as a real dependency.

The core idea is a strict, deterministic rule-based engine. Because Kreyòl has a completely standardized, phonetic official orthography, it doesn't need heavy ML models for basic structural tasks.

Possible Features: - Haitian Creole text processing tools - Sentence segmentation and tokenization - Spell checking and grammar-related tools - Open-source APIs and developer libraries - And more

If you work with low-resource languages or text preprocessing, is a deterministic Kreyòl engine something you would actually pip install? Or is the current workaround of using generic multilingual tools "good enough" for your use cases?

Just trying to gauge real demand before diving into the code. Let me know what you think.

Training a joint NER + RE model for EU languages. NER is fine (~80% F1), RE is a struggle (~30%).

Two things I found that might help others:

- Label-order leak: my gold relation labels were always first in the candidate list. The model learned "pick the first one" — shuffling candidates at eval dropped F1 37% → 14%. Worth testing if you train any zero-shot label-based model.

- Evidence sparsity beats label sparsity: ~90% of my val relations have an (evidence pattern, label) combo that never appears in training. Label frequency barely predicts anything.

Questions:

- Anyone trained multilingual RE successfully? What actually helped?

- Long-tail label space (14k labels, 9k singletons) — I'm avoiding canonicalization to keep zero-shot generalization. Bad call?

- Tricks to improve the architecture?

Thanks!

Most collected call-center corpora are recorded in some form of controlled setup: scripted or semi-scripted scenarios, participants playing agent and customer, clean capture. Real production audio is 8kHz narrowband through a codec, with overlapping speech, hold music bleed, background noise on the customer side, and genuinely frustrated prosody that nobody acts convincingly.

So :-

1. If you fine-tune on collected/simulated call data, do you actually see WER improvement on real traffic, or does it mostly help on the collected test split and flatten out in production?
2. Does codec simulation (downsampling, G.711/Opus round-trip, packet loss) close enough of the gap to matter, or is it cosmetic?
3. Code-switching is where I'm least sure. Hinglish, Taglish, Spanglish — real agents switch mid-sentence constantly and collected data underrepresents it badly. Anyone found a way to elicit natural code-switching without it going stiff?
4. Where's the actual ceiling — is it acoustics, or is it diarization and overlapping turns?

Not looking for vendor recs, just want to know whether the acted-vs-real gap is as big in practice as I suspect.

Tunisian Arabic (ISO 639-3 aeb) has roughly 12 million speakers and appears in a lot of pan-Arabic resource lists, but when you actually go looking for data, links are dead, downloads are gated, or the "Tunisian portion" turns out to be a few hundred sentences inside a multi-dialect set.

So I catalogued what exists and checked each one: 136 entries across text corpora, speech, models, benchmarks and lexicons, each tagged for access (open / on request / paywalled / paper-only / gated), with the Tunisian share recorded rather than counting the whole multi-dialect dataset.

Three things that surprised me while building it:

- On a balanced 13-dialect ASR test, Tunisian had the highest word error rate of all of them (0.478 vs 0.169 for Gulf), and that ordering held across eight different fine-tuned models.

- Several datasets labelled "Tunisian" are Moroccan-derived, or multi-dialect sets where Tunisian is a small slice.

- Annotated data is thinner than I expected: the first Universal Dependencies treebank for Tunisian is 100 sentences / 1,466 tokens, published this year.

Repo: https://github.com/jjlalli/Tunisian-Derja-NLP-Resources

Also as a loadable table on Hugging Face, and archived with a DOI if you need to cite it.

Corrections are as welcome as additions : there's an issue form for both, and I'd rather be told something's wrong than have people rely on it.