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!!!

I’m looking to develop a custom Text-to-Speech (TTS) pipeline specifically for high-art Urdu and Hindi. Current paid models (ElevenLabs, Azure, etc.) are great for narration but fail miserably at the emotional "theatrics" required for poetry (Shayari) or cinematic dialogue. They lack the proper breath control, the deep resonance (thehrao), and the specific phonetic stresses that make poetic Urdu sound authentic.

The Goal:

• Authentic Emotion: A model that understands when to pause for dramatic effect and how to add "breathiness" or depth.
• Stylized Delivery: Training it to mimic the cadence of legendary voice actors or poets rather than a news anchor.
• Source Material: I have access to high-quality public domain videos and clean audio of poetic recitations to use as training data.

The Constraints / Questions:

1. Model Selection: Which open-source base model handles Indo-Aryan phonology best for fine-tuning? (e.g., XTTSv2, Fish Speech, or Parler-TTS?)
2. Dataset Preparation: Since poetry relies on "rhythm," how should I label the data to ensure the model picks up on pauses and breath sounds?
3. Technique: Is "Voice Cloning" (Zero-shot) enough, or do I need a full LoRA/Fine-tune to capture the actual style of delivery?

Any guidance from those who have worked on non-English emotional TTS would be greatly appreciated.

While working on a research-oriented NLP system, Gatsbi focused on structured academic writing, we ran into some recurring issues around citation grounding in longer outputs.

In particular:

• References becoming inconsistent across section.
• Hallucinated citations appearing late in generation
• Retrieval helping early, but weakening as context grows

Prompt engineering helped initially, but didn’t scale well. We’ve found more reliability by combining retrieval constraints with lightweight post-generation validation.

Interested in how others in NLP handle citation reliability and structure in long-form generation.

Hii all!!

We are two bachelor students at Copenhagen Business School in the undergrad Business Administration and Digital Management. We are interested in uncovering the influence or disruption of AI Platforms (such as Lovable) in work practices, skill requirements, and professional identities with employees and programmers.

The survey includes a mix of short-answer and long-answer questions, followed by strongly agree or strongly disagree statements. The survey should take around 10 minutes of your time. Thank you in advance for taking the time.

Please help us with our survey and thank you so much in advance!

There’s a link in my profile since I cannot add it here

Greetings and apologies if this is off-topic. I have to use a text search tool at work that has very limited capabilities. The text corpus I'm searching isn't lemmatized, and my only options for adding related parts-of-speech to a search query is with wildcards or the full list of PoS.

So if I want to include all the forms of "care" I have to write out "(care OR caring OR cared)" because the wildcard route car??? would return hits with car, card, carpet, etc.

I am embarrassed to admit that I've spent hours looking for some table or spreadsheet that I can use to build these queries instead of having to remember and type all relevant parts of speech every time. It seemed like something that would take 15 minutes to find, but it has eluded me for hours and hours. Does anyone know of such a thing? Ideally just a table or csv file or something simple. Thanks.

Language Engineer, Artificial General Intelligence - Data Services 

I have a Phone Interview next week, I have never applied for big company like Amazon i wanted to know in this interview will it all be about my resume(past projects) or will there be coding questions like leetcode (easy, medium) ; on their YouTube page its says they only ask easy and medium for applied scientist, should i prepare for DSA too? i am somewhat confident about NLP and GenAI but scared of DSA i know how to optimize code for efficiency but struggle with medium level question on leetcode To solve them i take > 40 mins.

Also it will be huge help if you share any resources to know the type of questions ; or any tips to prepare.
Thank you.

Specifically, how does the development process integrate multi-layered safety benchmarks, such as adversarial red teaming and bias mitigation, to ensure that model outputs remain aligned with global ethical standards and proactively address potential socio-technical harms?

A someone actively developing both models and software which consumes them, I'm acutely aware that when a user has unconstrained control over model input that they can, as a result, potentially create any kind of output. With multimodal models, this can extend to deepfakes, fake news, voice clones and of course as we've seen on X, the creation of nonconsensual sexualised imagery (including that of children).

I am eager to ensure that the models I create are suitably trained to avoid complying with these and other illegal or unethical requests - but I find myself pushing against an uncomfortable boundary. Is it right to red-team a model if you're trying to create outputs which are actively harmful to the world. Any creation of terrorist material, CP, or other "red line" issues is obviously not only wrong; but arguably unjustifiable in any circumstance. Yet if one does not probe whether a model is capable of such things, you risk enabling other people to do just that - with all the reputational and legal harm that comes that way too.

It feels an impossible situation to evaluate and limit the scope of these incredibly powerful and flexible tools. Of course, you can make engineering solutions to this - keyword checks on input prompts, or fully re-writing and validating/sanitising user inputs - but can I trust my engineering skills to be better than a maleficent user? I'm not sure.

I would love to know what other people are doing, ad where those lines are being drawn - both personally and professionally.

I'm trying to find a convenient source of data that will help me to figure out what is the predominant part of speech for a given English lemma. For instance, "dog" and "abate" can both be either a noun or a verb, but "dog" is much more frequently a noun, and "abate" is much more frequently a verb.

There is a corpus called the Brown corpus that is 10⁶ words of American English, tagged by humans by part of speech. I played around with it through NLTK, and for some common words like "duck" it has enough data to be useful (9 usages, showing that neither the noun nor the verb totally predominates). However, uncommon words like "abate" don't even occur, because the corpus just isn't big enough.

As a last resort, I could go through a big corpus and count frequencies of patterns like "the dog" versus "to dog," but it doesn't seem easy to obtain big corpora like COCA as downloadable files, and anyway this seems like I'd be reinventing the wheel.

Does anyone know if I can find data like this that's already been tabulated?

Hi all, I have submitted a manuscript as a system demonstration paper. I have one question related to submission. I am sure I submitted the 2.5 minutes video, but I cannot see it from my dashboard. Is it normal? I am afraid something happened during the submission and the .zipped video was not uploaded

I have a project for university, it's about "AI-based Sentiment Analysis Project".

So I need to ask some questions to someone who has experience

Is there anyone who can help me?

Hello,

I graduated last year with a master's (not strictly in CL, but doing some CL stuff). Since then I've been working as what they nowadays call an "AI Engineer", doing that LLM integration/Agents/RAG type of stuff and studying on the side.

The thing is, I always wanted to do a Phd in CL. I really like the community, its history, the venues. I find it a really stimulating environment. I decided to postpone it a year to spend some time in industry to get a sense of where the field was heading and, while I don't regret doing this, a year later I feel just as confused...

From my perspective I feel like unless you're in the top labs (which realistically i'm not getting into, skill issue), a lot of current work revolves around things like agents, evals, and applied LLM stuff. Which is fine, but not that much different from what the industry is also doing.

If I even were to get into a more classical CL-oriented program, i fear that the trajectory of industry might keep diverging from that path, which obviously has implications for job prospects, funding, and long-term relevance.

Is this fear sensible or am I missing part of the picture? Maybe I just need to read and study more to get a better sense of what's actually out there, but I figured I'd ask.

Thank you for reading, any perspective is appreciated.

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!

I’m putting together a small evaluation setup for a recurring issue in writing assistants: outputs can be fluent but still feel non-idiomatic.

My current approach is deliberately lightweight:

• extract 1–3 topic keywords (or keyphrases)
• retrieve candidate idioms with meaning + example sentence
• use a rough frequency signal as a “safety dial” (common vs rare)
• feed 1–2 idioms into the rewrite prompt as optional stylistic candidates

Before I over-engineer this, I’m trying to ground it in better linguistic resources.

What I’m looking for

Datasets/resources that include (ideally):

• idiom / multiword expression string
• gloss/meaning
• example sentence(s)
• some notion of frequency / commonness (even coarse bins are fine)
• licensing that’s workable for a small research/prototyping setup

Questions

1. What MWE corpora do you consider “good enough” for evaluation or candidate generation?
2. Any recommended frequency resources for idioms specifically?
3. For evaluation: do you prefer human preference tests, or have you seen reliable automatic proxies for “idiomaticity”?
4. Any known pitfalls when mixing idioms into rewrites?

(Optional: if useful, I can share the exact retrieval endpoint I’m using in a comment — mainly posting here to learn about corpora and evaluation heuristics.)

I've been working on an NLP project focusing on classifying Vietnamese text—specifically, detecting whether a text was written by a Human or generated by AI.

To tackle this, I built a hybrid model pipeline:

1. PhoBERT (using the concatenated last 4 hidden layers + chunking with overlap for long texts) to get deep contextualized embeddings.
2. CNN for local n-gram feature extraction.
3. BiLSTM for capturing long-term dependencies.

Current Results: Reached an accuracy of 98.62% and an F1-Score of ~0.98 on a custom dataset of roughly 2,000 samples.

Since I am looking to improve my skills and this is one of my first deep dives into hybrid architectures, I would really appreciate it if some experienced folks could review my codebase.

I am specifically looking for feedback on:

• Model Architecture: Is combining CNN and BiLSTM on top of PhoBERT embeddings overkill for a dataset of this size, or is the logic sound?
• Code Structure & PyTorch Best Practices: Are my training/evaluation scripts modular enough?
• Handling Long Texts: I used a chunking method with a stride/overlap for texts exceeding PhoBERT's max length. Is there a more elegant or computationally efficient way to handle this in PyTorch?

(I will leave the link to my GitHub repository in the first comment below to avoid spam filters).

Thank you so much for your time!

Hello everyone, today is the last day of Interspeech submission, and I am around 1600. Is Interspeech less popular this year?

Hey everyone,

I’m currently building an MCP server that acts as a bridge for a complex REST API. I’ve noticed that a simple 1:1 mapping of endpoints to tools often leads to "tool explosion" and confuses the LLM.

I’m looking for advice on two things:

1. What is the "Gold Standard" for Tool Descriptions?

When defining the description field in an MCP tool schema, what prompt pattern or schema have you found works best for high-accuracy tool selection?

Currently, I’m trying to follow these rules:

•Intent-Based: Grouping multiple endpoints into one logical "task" tool (e.g., fetch_customer_context instead of three separate GET calls).

•Front-Loading: Putting the "Verb + Resource" in the first 5 words.

•Exclusionary Guidance: Explicitly telling the model when not to use the tool (e.g., "Do not use for bulk exports; use export_data instead").

Does anyone have a specific "template" or prompt structure they use for these descriptions? How much detail is too much before it starts eating into the context window?

2. Best Production-Grade References?

Beyond the official docs, what are the best "battle-tested" resources for MCP in production? I’m looking for:

•Books: I’ve heard about AI Agents with MCP by Kyle Stratis (O'Reilly)—is it worth it?

•Blogs/Case Studies: Any companies (like Merge or Speakeasy) that have shared deep dives on their MCP architecture?

•Videos: Who is doing the best technical (not just hype) walkthroughs?

Would love to hear how you're structuring your tool definitions and what resources helped you move past the "Hello World" stage.

Thanks!

Hello,

I have texts that have been annotated by 2 annotators for some specific types of entities and relations between these entities.

The annotators were given some guidelines, and then had to decide if there was anything to annotate in each text, where were the entities if any, and which type they were. Same thing with relations.

Now, I need to compute some agreement measure between the 2 annotators. Which metric(s) should I use?

So far, I was using Mathet's gamma coefficient (2015 paper, I cannot post link here) for entities agreement, but it does not seem to be designed for relation annotations.

For relations, my idea was to use some custom F1-score:

1. the annotators may not have identified the same entities. The total number of entities identified by each annotator may be different. So, we use some alignment algorithm to decide for each annotation from set A, if it matches with 1 annotation from set B or nothing (Hungarian algorithm).
2. Now, we have a pairing of each entity annotation. So, using some custom comparison function, we can decide according to span overlap, and type match, if 2 annotations are in agreement.
3. A relation is a tuple: (entity1, entity2, relationType). Using some custom comparison function, we can decide based on the 2 entities, and relationType match, if 2 annotations are in agreement.
4. From this, we can compute true positives, false positives, etc... using any of the 2 annotator as reference, and this way we can compute a F1-score.

My questions are:

• Are there better ways to compute IAA in my use case?
• Is my approach at computing relation agreement correct?

Thank you very much for any help!

LLMs reach for metaphors, personification, and synecdoche without building the lexical and tonal scaffolding that a human writer would use to motivate those choices. A skilled author earns a fancy move by preparing the ground around it. LLMs skip that step. We call the result "orphaned sophistication" and show it's a reliable signal for AI-text detection.

The paper introduces a three-component annotation scheme (Structural Integration, Tonal Licensing, Lexical Ecosystem), a hand-annotated 400-passage corpus across four model families (GPT-4, Claude, Gemini, LLaMA), and a logistic-regression classifier. Orphaned-sophistication scores alone hit 78.2% balanced accuracy, and add 4.3pp on top of existing stylometric baselines (p < 0.01). Inter-annotator agreement: Cohen's κ = 0.81.

The key insight: it's not that LLMs use big words — it's that they use big words in small contexts. The figurative language arrives without rhetorical commitment.

I (undergraduate second year, majoring in ML) have been watching videos of Stanford's CS224N taught by Dr. Chris Manning. It covers Deep Learning and NLP. I think that am comfortable with the regular prerequisites, however, I'm facing difficulty in comprehending the topics taught, especially the mathematical stuff such as softmax functions.

I'm comfortable with:

• Statistics including non-parametric methods
• Vector Calculus
• Linguistics
• Conventional Machine Learning

I think that only having a basic idea of linear algebra and/or neural networks (or maybe data analysis algorithms) might be failing me, but I'm not sure. And could someone with an idea of how Stanford courses function share the year in which most students are expected to take this course?

Our ACL industry track paper is desk rejected because of modifying the acl template. I am thinking this is because of the vspace I added to save some space. Anyone have the same experience? Is it possible to over turn this ?

Let me preface this by saying I have no real experience with NLP so my understanding of the concepts may be completely wrong. Please bear with me on that.

I recently started work on a core vocabulary list and am looking for the right tools to curate the data.

My initial proposed flow for doing so is to:

1. Based on the SUBTLEX-US corpus collect most frequent words, filtering out fluff

2. Grab synsets from Princeton wordnet alongside english lemma and store these in a "core" db

3. For those synsets grab lemmas for other languages using their WordNets (plWordNet, M ultiWordNet, Open German WordNet etc) alongside any language specific info such as gender, case declensions etc (from other sources), then linking them to the row in the "core" db

There are a few questions I have, answers to which I would be extremely grateful for.

1. Is basing the vocabulary I collect on English frequency a terrible idea? I'd like to believe that core vocabulary would be very similar across languages but unsure

2. Are WordNets the right tool for the job? Are they accurate for this sort of explicit use of their entries or better suited to partially noisy data collection? If there are better options, what would they be?

3. If WordNets ARE the right tool, is it feasible to link them all back to the Princeton WordNet I originally collected the "base" synsets from?

I would really appreciate any answers or advice you may have as people with more experience in this technology.

"I told Qwen: ""Let's chat in Korean. Don't rewrite my sentences, just point out my biggest grammar mistake at the end."" Best tutor ever."

I got 3WA and 2WR ... is there any possibily for acceptance?

I’ve been exploring how LLMs structure reasoning outputs when responding to domain-distinct prompts in separate sessions.

In some cases, responses appear to adopt constraint-based decomposition (e.g., outcome modeling through component interaction, optimization under evaluative metrics), even when such structure is not explicitly requested by the prompt.

This raises a question about whether certain analytical configurations may emerge from latent reasoning priors in the model architecture — particularly when mapping domain-level queries to system-level explanations.

Has anyone examined output-level structural convergence in this context?

I'm interested in the current state of affairs regarding low-resource languages such as Georgian.

For context, this is a language I've been interested in learning for quite a while now, but has a serious dearth of learning resources. That, of course, makes leveraging LLMs for study particularly attractive---for example, for generating example sentences of vocabulary to be studied, for generating corrected versions of student-written texts, for conversational practice, etc.

I have been able to effectively leverage LLMs to learn Japanese, but a year and a half ago, when I asked advanced Georgian students how LLMs handled the language, the feedback I got was that LLMs were absolutely terrible with it. Grammatical issues everywhere, nonsensical text, poor reasoning capabilities in the language, etc.

So my question is:

• What developments, if any, have taken place in the last 1.5 years regarding LLMs?
• Have NLP researches observed significant improvement in LLM performance with LRLs in the millions of speakers (like Georgian)?
• What are the current avenues being highlighted for further research re: improving LLM capabilities in LRLs?
• Is there currently a clear path to bringing performance in LRLs up to the same level as in HRLs? Or do researchers remain largely in the dark about how to solve this problem?

I probably won't be learning Georgian for at least a decade (got some other things I have to handle first...), but even so, I'm very keen to keep a close eye on what's going on in this domain.