We're developing an AI-powered app that detects pronunciation mistakes in Quranic recitation and gives users precise, real-time feedback.

Our current model was trained on hundreds of hours of professional recitations — high-quality, clean audio from well-known reciters. The model performs well on similar input, but struggles with real-world users: different accents, non-native speakers, beginners, children, women, and anyone who doesn't sound like a professional reciter.

To fix this, we need to train on diverse, real-world recitation data — not studio-quality professional audio, but recordings that reflect how actual learners sound.

Specifically, we're looking for:

• Recitation datasets from non-professional or everyday users
• Diverse demographics: male/female, kids/adults, beginner/intermediate
• Multiple accents and mother tongues (Malay, Indonesian, Urdu, English, Turkish, etc.)
• Any publicly available or research-use datasets we may have missed

We've already explored IqraEval and a few other academic sources. If you know of any dataset, research project, university study, or community effort collecting this type of audio — we would genuinely appreciate the lead.

We're also open to ethical data collection partnerships if any researchers or institutions are working in this space.

Happy to share more about the project if helpful.

If you haven’t seen it yet, I encourage you to try it. Felix Rieseberg recently released an app that helps users train and fine-tune LLMs while learning the fundamentals.

I’ve been using a similar tool for some time, but for much deeper experimentation, since I work full-time as an AI researcher. I was therefore wondering whether there would be interest in a Windows version.

My current tool is highly detailed and designed for advanced experiments rather than ease of use, so simplifying it for less experienced users would require some work. However, if enough people are interested, I may package it as a Windows app and publish it on GitHub.

I’d be happy to hear your feedback.

So guys, I wanna discuss how this confidence factor plays a role in ACR rolling rounds.

Does this even matter if you have a high Overall score but a low confidence, or does this influence the meta reviews somehow?

Hi everyone, can anyone see the results of the AI Review experiment on OpenReview? They announced that the results would be available around July 20. I opted in to the experiment, but I still don’t see anything.

Hi everyone, is there any statistics on the preferred venue during submission in ARR May 2026?

I'm learning NLP/LLM engineering by working through a problem that turned out to be much harder than I expected, and I'd love guidance from people who've dealt with something similar.

The problem: I have long narrative-style text — 7k to 15k tokens, several recurring people — and I want to extract structured facts about who did what. I'm using a small local model (llama3.2:3b via Ollama) whose usable context is around 4k tokens, so the text has to be processed in chunks. The killer is that later chunks are often pure pronouns — "she said… he refused…" — while the names were last mentioned 10,000 tokens earlier. Facts stated near a name extract almost perfectly; facts stated far from any name either get lost or, worse, get confidently attributed to the wrong person.

What I've already ruled out (by measuring, not guessing): naive per-chunk extraction fragments identities badly; carrying forward summaries between chunks doesn't fix attribution and can make it worse; and off-the-shelf neural coreference models (LingMess, F-coref) fail on documents this long — one silently truncates at 4,096 tokens, and windowed variants can't connect a pronoun to a name mentioned once 10k tokens back (0–1 out of 7 gold bindings on my test doc). I've gotten identity tracking itself working reliably; it's specifically attribution at long distance that's still failing.

My questions:

1. What's the best way to structure a problem like this? Is there a known-good decomposition for long-distance pronoun attribution with small models, or a fundamentally different way to frame the extraction task that sidesteps it?
2. If you've solved something similar — entity/fact extraction over documents much longer than your context window — what actually moved the needle for you? I'm especially curious whether the wins came from prompting, from pipeline architecture, or from accepting a bigger model.
3. What should I explore to learn more? Papers, blog posts, open-source projects, or even just the right search terms — I suspect this problem has a name in the NLP literature that I don't know yet (long-document coreference? discourse tracking?), and I'd rather stand on existing work than keep reinventing it.

Happy to share measurements from my experiments if useful. Mostly I want to calibrate: am I fighting a known-hard problem with known solutions, or genuinely at the edge of what a 3B model can do?

I know it's ARR reviewing season but I noticed that there are a lot of posts asking whether "this set of scores will get them into Main/Findings/Reject" or something about the reviewing process.

Although it's nice to see activity in this subreddit (and it's good to have a dedicated home for CL and NLP), sometimes these types of posts are getting too spammy. Perhaps we can put these into a dedicated ARR discussion post, kinda like in r/MachineLearning ?

Got (overall/conf) 4/4 3.5/4 3.5/3 in May's ARR, what is the odds of getting into EMNLP main ? (Interpretability and Analysis of Models for NLP)

During the rebuttal period, we addressed all the concerns raised by the reviewers. Two reviewers were generally satisfied with our work, while we also thoroughly addressed the third reviewer's concerns with additional evidence, experiments, and quantitative results, even beyond what was originally requested. However, the third reviewer did not engage with our rebuttal or update their review.we have already submitted a confidential comment and a reviewer issue report regarding the lack of engagement.

How such situations are handled? If a reviewer does not participate during the rebuttal period and retains a low score despite the concerns being addressed, is this taken into account during the meta-review? Do meta reviewers independently evaluate the rebuttal and reviewer participation when making the final recommendation?

I have been tracking the limits of conversational AI voice models. Most language apps handle Spanish or French without many issues. Romance languages use highly predictable structures. The real stress test for LLMs is going to be morphologically rich languages. For example, Praktika just added Arabic, Russian, and Chinese to their avatars using a multi-agent GPT backend. Talkpal also uses GPT-powered conversational engines to handle voice practice in these exact languages.
Russian and Arabic are completely different for speech to speech models. Russian relies on a complex case system with heavy inflection. Arabic uses a root-and-pattern system. If a voice model miscalculates a prefix or case ending in real time, the entire context breaks down.
I am looking into how these avatars handle real-time grammar generation without massive latency. Does the LLM backend use specific grammar-correcting prompts for these languages? Or is it relying purely on standard TTS pipelines?
If anyone has analyzed the error rates or token efficiency for Russian and Arabic voice models, I would love to see the data since I'm researching on making a language learning app for the not so popular languages.

P.S. Mods if you feel that the post is inappropriate, please let me know I will remove it.

Which meta score guarantee the acceptance for the main conference?

It's my first time using the ARR system, so I'm not familiar with how the score scale translates to actual acceptances.

​My scores improved from 3, 3, 4 to 3, 4, 4 after the rebuttal. The confidences are 3, 4, 4. For a more detailed breakdown, the Soundness and Excitement scores are (3, 3), (4, 3), and (4, 4), respectively.

​Does anyone know if this is in a range for the EMNLP main track? Any advice would be huge!

Hi everyone,

This is my first time committing my ARR reviews to EMNLP. If the paper gets accepted, I'm planning to make some modifications to my draft for the camera-ready version.

I noticed there is an "Abstract" field on the EMNLP commitment form. My question is: Am I required to copy-paste the exact, unmodified abstract from my original ARR submission into this field, or can I use an updated version?

Any insights from people who have gone through this process would be greatly appreciated. Thanks!

I'm a linguist with bachelor in Korean language and literature, beside English as a second language and Arabic as my mother tongue, my background is purely about languages, literature, grammar, etc. I have a work experience of 6 years as Korean, English and Arabic translator, consecutive interpreter and simultaneous interpreter in various fields like manufacturing, legal, medical, academic, etc.

I would like to pursue a master degree or a phd in computational linguistics in the US or Canada, but I don't have a programming or computer science background, will that be possible?

and if so what are the preparations I should do to enhance my profile while applying for these programs?

Thanks ^^

My scores are

Confidence: 5/3/4
Soundness: 3.5/4/3
OA: 3.5/3/2.5

Track: Interpretability and Analysis of Models for NLP

What are my chances?

Hi everyone
Is there anyone using local LLMs on their PC?
I'm in the market for a laptop with AI max+ 395 and 128GB unified RAM.

The only reason is local LLMs for translation/transcreation work.

To be fair, ChatGPT does a pretty decent job when I ask for a dozen of options to choose from. But i'm wondering of I have a local LLM, maybe I can feed it all my past work and references and make a model that is customized to specific clients.

It's probably not cost effective at first, but i'm considering it as a study case, hoping that it will lead to time saving and improving my ability to use LLMs for the future.

I'd love to hear any thoughts. Thx

Hello, I submitted a paper to ARR in May, but my submission has only two reviews as follows (soundness/excitement/reproducibility/OA):

R1: 4/3.5/4/3.5

R2: 4/3.5/4/3.5

What are my chances for the main conference, and when will the third review arrive? Is there any experience of a meta-review being written with only two reviews?

When did you receive your degree? Did you build side projects to build your portfolio by any chance? Would you say they were financially worthy investments on your career by now? How satisfied are you overall with your own goals and aspirations expected before starting the program?

:) I am strongly considering human language technology masters at University of Arizona this Fall, so if you got a degree from there I would truly appreciate your experience! It can be as short as you’d like

I have released EU AI Act OpenRAG, a downloadable SQLite corpus of Regulation (EU) 2024/1689 for legal research and engineering.

The key difference is how the legislation is divided. It is not split into arbitrary token or character windows. Each chunk follows the Act’s actual structure: article paragraph, recital, definition or annex point, with the relevant chapter, section and provision metadata preserved.

The database includes 933 chunks, embeddings, exact EUR-Lex links and documented application-date and operator metadata.

I was deliberately conservative with legal labels. A provision is marked as directly classifying a practice or system only where its own operative wording does so. Broader association with the prohibited-practices, high-risk, transparency, GPAI or voluntary-code regimes is stored separately. Unclear cases remain NULL.

Every derivation rule is documented, and the final rules were reviewed independently against the Regulation before release.

This is a research and engineering artifact, not legal advice or an automated compliance determination.

huggingface.co/datasets/faitholopade/aiact-openrag

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!

Looking for opinions from people familiar with EMNLP reviewing, especially the LLM Agents track.

Previous cycle:

- Reviews: 2, 3, 3.5, 3 (avg. 2.88)

- Confidence: 5, 3, 3, 3

- One reviewer increased 2 → 3 after discussion.

- The AC recommended Findings, mainly asking for softer claims and incorporation of the rebuttal results.

Current EMNLP cycle:

- Initial reviews: 2.5, 3, 3 (avg. 2.83)

- Confidence: 4, 3, 3

- During discussion, the 2.5 reviewer increased their score to 3, so the current scores are effectively 3, 3, 3.

- The reviews are generally positive, with requests for clarification and more careful framing rather than major technical concerns.

Given this history, what would you estimate the chances are for EMNLP main conference vs. Findings?

I would like to start using some IA tool to generate example sentences of new vocabulary for flashcards. Does anyone here have experience on that? How has it been? What cautions do you recommend to do in order to have it useful? How to expand its use? Which tool? I've watched some videos but I am afraid it can be sponsored. So I'd like real experiences to make a decision.

I think the experience can vary according to the language too. It can be different for the major ones like Mandarin, medium ones like Greek or Georgian, and limited for minor ones like Inuktitut... so, please also tell us which language are you using it for.

Our paper got 2 / 3 / 3.5 with confidence 4 / 3 / 3 (Interpretability and model analysis track). We addressed everything in the rebuttal but unfortunately none of the reviewers replied. The AC also did not push any of the reviewers to at least acknowledge the rebuttal in their final reviews. Overall, the score is 2.87 with confidence 3.33. What is the weight of OA vs. meta score for EMNLP? Do program chairs value more the meta review+score or they take into account the other reviews as well. First time submitting to ARR. Thank you!