REVAS — AI-Powered Peer Review Feedback for Academics REVAS analyzes the weakness section of your peer review, scoring each paragraph on actionability, helpfulness, grounding, and verifiability.

🗓️ The ARR March review deadline is approaching: April 20 AoE.
Finishing up your review? Run it through REVAS, a peer review assistant that makes your suggestions more actionable, flags unsupported claims, and grounds your feedback in the paper.
👉 revas.mbzuai.ac.ae

#MemoryModay #NLProc Uma et al. (2020) highlights 'A Case for Soft Loss Functions' efficacy using soft labels & crowd annotations in AI tasks, outshining top-tier methods.

To accommodate ACL decisions, we are further extending the commitment deadline for pre-reviewed ARR submissions to April 7!

The paper acceptance notifications will be out by the 6th of April, AoE. The PCs are working hard throughout the holiday season to finalize the decisions.

Apologies for the delay!

2026 Manchester Application and registration

The deadline for submission to the Political Networks conference is this Friday. It's taking place Aug 4-7, in Manchester. sites.google.com/view/confpol...

#TBT #NLProc '[MASK]? Making Sense of Language-Specific BERT Models' by @deboranozza.bsky.social, Bianchi & @dirkhovy.bsky.social (2020), explores language-specific vs universal BERT models.

- Optional: question your life choices but show up to do it again the next week anyway

I realized how much DMing is like being a professor/chairing a committee. You:
- make a brilliant plan for 2+ hours of fun
- prep lots of material
- immediately get derailed by questions/arguments/etc.
- keep it together to make the most of the time together
- end up not using most of the material

Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers Hanh Nguyen, Dirk Hovy. Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019). 2019.

#MemoryModay #NLProc 'Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers,' by Nguyen & @dirkhovy.bsky.social decodes speaker reviews for user preferences using topic models. Domain knowledge needed for market analysis.

A slide showing that the posterior is proportional to the likelihood times the prior

I wrote a blog post on my experience using AI for slide generation

Basic idea: write your lecture notes first, then prompt the LLM to produce corresponding slides in reveal.js (h/t @chenhaotan.bsky.social). I'm picky about my slides but was happy with the results!
alexanderhoyle.com/posts/ai-sli...

Identifying Linguistic Areas for Geolocation Tommaso Fornaciari, Dirk Hovy. Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019). 2019.

#TBT #NLProc Fornaciari, @dirkhovy.bsky.social's 'Identifying Linguistic Areas for Geolocation' explores using social media writing for geolocation via Point-to-City (P2C).

Wish I could be at @eaclmeeting.bsky.social, but the lab is well represetned. If you are there, come and say hi!

Dense Node Representation for Geolocation Tommaso Fornaciari, Dirk Hovy. Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019). 2019.

#MemoryModay #NLProc 'Dense Node Representation for Geolocation' by Fornaciari & @dirkhovy.bsky.social reveals efficient geolocation methods using node2vec & doc2vec models. Greater network size, less parameters.

Geolocation with Attention-Based Multitask Learning Models Tommaso Fornaciari, Dirk Hovy. Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019). 2019.

#TBT #NLProc 'Geolocation with Attention-Based Multitask Learning Models' by Tommaso Fornaciari, @dirkhovy.bsky.social (2019) reveals how online political talks can become one-sided. Breaking out of our bubbles! #SocialMedia

Chpater 8: @dirkhovy.bsky.social, M Gerondeau & J Globisz on text data and natural language processing.
A very useful chapter on why text is such a rich source for CSS, and how NLP can help with exploration, prediction, and generation; if used thoughtfully and with clear research goals.

Just read this great piece - paulgp.com/2026/03/16/r... by @paulgp.com and it got me thinking.

It feels like there is a lot of moral(?) ambiguity and ambivalence around the use of LLMs for academics.

So far, I've avoided having LLMs do basically any of my research writing ...

The Social and the Neural Network: How to Make Natural Language Processing about People again Dirk Hovy. Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media. 2018.

#MemoryModay #NLProc 'Make Natural Language Processing About People Again' by @dirkhovy.bsky.social (2018) uncovers how AI models portray different religions and emotions. #AIEthics

Joel Tetreault (not on here) also has a great talk on the topic, with lots of interesting anecdotes

Comparing Bayesian Models of Annotation Silviu Paun, Bob Carpenter, Jon Chamberlain, Dirk Hovy, Udo Kruschwitz, Massimo Poesio. Transactions of the Association for Computational Linguistics, Volume 6. 2018.

#MemoryModay #NLProc 'Comparing Bayesian Models of Annotation' by Paun et al. dives into corpus annotation, evaluating six models' predictiveness and accuracy. Essential for navigating annotators and item difficulties.

📢 Call for Abstracts!
Towards a Safer Web for Women (co-located with #WebSci26)
📍 Braunschweig 🇩🇪 | 26 May 2026
Theme: Preventive approaches to women’s online safety
🗓 Deadline: 27 March 2026
🔗 forms.gle/tYheEgSwGecf...
🌐 tsww26.github.io

Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning Sotiris Lamprinidis, Daniel Hardt, Dirk Hovy. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.

#TBT #NLProc 'Predicting News Headline Popularity' by Lamprinidis, Hardt, @dirkhovy.bsky.social (2018) shows neural networks perform similar to Logistic Regression in prediction.

One of my favorite studies of the last few years! Great read (albeit with a side of worrying implications for surveys)

One of my favorite interdisciplinary projects (with @questoph.bsky.social). Plus: colorful maps!

Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting Dirk Hovy, Christoph Purschke. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.

#TBT #NLProc 'Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting' by @dirkhovy.bsky.social and Christoph Purschke (2018) highlights how social class and background impact technology performance. #TechInclusion

4/7 We argue these aren't separate bugs. They're four facets of the same problem:

🔴 Probabilistic — can't match requested distributions
🟠 Semantic — confidence ≠ correctness
🔵 Distributional — output diversity collapse
🟢 Metacognitive — can't assess its own competence

1/7 🧵 The GPT-4 technical report featured detailed calibration curves.

Since then, not a single major model release has reported calibration. The field quietly stopped measuring whether models know what they don't know.

Our new position paper argues this is a mistake. Here's why.

We were thrilled to host @mtutek.bsky.social at our lab last week.
His talk "From Internals to Integrity: How Insights into Transformer LMs Improve Safety, Interpretability, and Explanation Faithfulness" led to great discussions! 👏
#Transformers #AISafety #ExplainableAI #MLResearch #NLProc

Call for Virtual Registration Subsidies Official website for the 2026 Conference of the European Chapter of the Association for Computational Linguistics

Call for Virtual Registration Subsidies for #EACL26 🌍

⚠️ Not for paper registrants
📝 Apply by Feb 27, 2026 (AoE)
📩 Decisions by Mar 2, 2026

2026.eacl.org/calls/virtua...

Don’t register before hearing back if you apply!

Table titled “Taxonomy for evaluation of AI in mental health applications,” organized into columns for quality criteria (validity and reliability) and real-world use (implementation and maintenance). Rows distinguish support types: assessment, intervention, and information synthesis. Each cell lists detailed evaluation questions, such as construct and criterion validity, consistency across populations and time, feasibility, effectiveness, usability, acceptability, safety, and unintended consequences, providing a structured framework for assessing AI systems in mental health contexts.

🔎🧩 𝗕𝗲𝘆𝗼𝗻𝗱 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀: 𝗛𝗼𝘄 𝘁𝗼 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗠𝗲𝗻𝘁𝗮𝗹 𝗛𝗲𝗮𝗹𝘁𝗵 𝗔𝗜 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝘆
AI for mental health is a high-stakes area: its evaluation needs to meet the highest expectations.

The new preprint 𝘙𝘦𝘴𝘱𝘰𝘯𝘴𝘪𝘣𝘭𝘦 𝘌𝘷𝘢𝘭𝘶𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘈𝘐 𝘧𝘰𝘳 𝘔𝘦𝘯𝘵𝘢𝘭 𝘏𝘦𝘢𝘭𝘵𝘩, written by an interdisciplinary team spanning AI [...]

Honored to give my first keynote at #IRCDL2026 on February 19th.

I’ll talk about how LLMs have shifted from productivity tools to everyday sources of info & personal guidance and what that means for risk, trust, bias, and alignment.

ircdl2026.unimore.it