What a blast! 🎉 We won one 🏆Test of Time Award and two 🏆🏆SAC Highlight Awards at #ACL2026! Congratulations to our award winners: @ricosennrich.bsky.social, @michellewastl.bsky.social, @vamvas.bsky.social, @sinaahmadi.bsky.social and their (co-)authors! Links to the winning papers in the comments.
All my homies left for ACL/ICML and left me home managing a project *for which we're looking for coauthors* 🥸. Join us (also talk to @sethjsa.bsky.social @onadegibert.bsky.social @niyatibafna.bsky.social @patuchen.bsky.social @michellewastl.bsky.social @ayukh.bsky.social)
Excited to be at #ACL2026NLP in San Diego with two papers! Come by to learn about cross-lingual semantic differences from work with @vamvas.bsky.social & @ricosennrich.bsky.social, and how LLMs respond to expressions of belief, with Kevin Du, @clarakuempel.bsky.social, and @alexwarstadt.bsky.social
New paper out in Cognitive Science! We analyzed various predictors of local ambiguity resolution on a heterogeneous set of garden-path sentences, namely surprisal, frequency, plausibility and cloze scores (measuring the likelihood of misanalysis of the ambiguous region). 1/2
Cloze, Frequency, Surprisal, or Plausibility? A Comparative Analysis of Predictors for Local Ambiguity Resolution
This study investigated the cognitive mechanisms underlying the processing of garden-path sentences by examining the influence of verb/structural bias, cloze probability, surprisal, and plausibility....
onlinelibrary.wiley.com
🗓️ SwissText 2026 keynote speakers announced & registration open! We are delighted to welcome Prof. Dr. Alexandra Birch and Dr. Valentina Pyatkin as our keynote speakers. 📋 Register here: ema.uzh.ch/RHK4W Early-bird rates available throughout April, with additional student discounts. #NLProc 1/3
SwissText 2026
10. Juni 2026 | UZH Campus Oerlikon
ema.uzh.ch
My lab is recruiting one PhD student and one post-doctoral researcher for a start as soon as this Summer! Apply by April 1 / March 31 to be among first candidates considered. jobs.uzh.ch/job-vacancie... jobs.uzh.ch/job-vacancie...
UZH: PhD in Language AI / Natural Language Processing
You will be joining the Department of Computational Linguistics, which has 6 Research Groups and around 70 postdoctoral and student researchers in the areas of Text Technologies, Phonetics and Speech ...
jobs.uzh.ch
So happy to have presented our project on **Individual signatures of spillover** at CPL2025. Grateful to my supervisor Lena Jaeger, for her great ideas and for her kind support! Thanks to my coauthors and the audience at CPL2025. The award was a democratic vote by the audience!
Computational Linguistics @ UZH@cl-uzh.bsky.social · 8mo agoCongratulations to our PhD student @cuiding.bsky.social for winning the Best Talk Award in the Computational Psycholinguistics Meeting 2025 (CPL 2025) lnkd.in/euTX89gi!
New preprint on prosody in the brain! tinyurl.com/2ndswjwu HeeSoKim NiharikaJhingan SaraSwords @hopekean.bsky.social @coltoncasto.bsky.social JenniferCole @evfedorenko.bsky.social Prosody areas are distinct from pitch, speech, and multiple-demand areas, and partly overlap with lang+social areas→🧵
A distinct set of brain areas process prosody--the melody of speech
Human speech carries information beyond the words themselves: pitch, loudness, duration, and pauses--jointly referred to as 'prosody'--emphasize critical words, help group words into phrases, and conv...
tinyurl.com
Take a look at how we challenge state-of-the-art NLP systems to recognize token-level semantic differences across languages with our new SwissGov-RSD dataset! @vamvas.bsky.social @ricosennrich.bsky.social Paper: arxiv.org/pdf/2512.075... Dataset: huggingface.co/datasets/Zur... #NLProc
#NeurIPS2025 Check out EyeBench 👀, a mega-project which provides a much needed infrastructure for loading & preprocessing eye-tracking for reading datasets, and addressing super exciting modeling challenges: decoding linguistic knowledge 👩 and reading interactions 👩+📖 from gaze! eyebench.github.io
👀Ever wondered how visual information quality affects reading and language processing? Our new #EMNLP2025 paper with @wegotlieb.bsky.social, Lena Jäger -- “Modeling Bottom-up Information Quality during Language Processing”, bridges psycholinguistics and multimodal LLMs. 🧠💡👇 arxiv.org/pdf/2509.17047
Let's meet at #EMNLP and talk about multilingual knowledge benchmarks! ⚠️MLAMA is full of disfluent sentences ❓Reason: templated translation 💡Simple full-sentence translation improves factual retrieval up to 25% 🙌Remember to check your benchmarks with speakers! Link: arxiv.org/pdf/2510.15115
💥Introducing new paper: arxiv.org/pdf/2510.17715, QueST — train specialized generators to create challenging coding problems. From Qwen3-8B-Base ✅ 100K synthetic problems: better than Qwen3-8B ✅ Combining with human written problems: matches DeepSeek-R1-671B 🧵(1/5)
Exciting #rstats news for Bayesian model comparison: bridgesampling is finally ready to support cmdstanr, see screenshot. Help us by installing the development version of bridgesampling and letting us know if it works for your model(s): pak::pkg_install("quentingronau/bridgesampling#44")
![R code and output showing the new functionality:
``` r
## pak::pkg_install("quentingronau/bridgesampling#44")
## see: https://cran.r-project.org/web/packages/bridgesampling/vignettes/bridgesampling_example_stan.html
library(bridgesampling)
### generate data ###
set.seed(12345)
mu <- 0
tau2 <- 0.5
sigma2 <- 1
n <- 20
theta <- rnorm(n, mu, sqrt(tau2))
y <- rnorm(n, theta, sqrt(sigma2))
### set prior parameters ###
mu0 <- 0
tau20 <- 1
alpha <- 1
beta <- 1
stancodeH0 <- 'data {
int<lower=1> n; // number of observations
vector[n] y; // observations
real<lower=0> alpha;
real<lower=0> beta;
real<lower=0> sigma2;
}
parameters {
real<lower=0> tau2; // group-level variance
vector[n] theta; // participant effects
}
model {
target += inv_gamma_lpdf(tau2 | alpha, beta);
target += normal_lpdf(theta | 0, sqrt(tau2));
target += normal_lpdf(y | theta, sqrt(sigma2));
}
'
tf <- withr::local_tempfile(fileext = ".stan")
writeLines(stancodeH0, tf)
mod <- cmdstanr::cmdstan_model(tf, quiet = TRUE, force_recompile = TRUE)
fitH0 <- mod$sample(
data = list(y = y, n = n,
alpha = alpha,
beta = beta,
sigma2 = sigma2),
seed = 202,
chains = 4,
parallel_chains = 4,
iter_warmup = 1000,
iter_sampling = 50000,
refresh = 0
)
#> Running MCMC with 4 parallel chains...
#>
#> Chain 3 finished in 0.8 seconds.
#> Chain 2 finished in 0.8 seconds.
#> Chain 4 finished in 0.8 seconds.
#> Chain 1 finished in 1.1 seconds.
#>
#> All 4 chains finished successfully.
#> Mean chain execution time: 0.9 seconds.
#> Total execution time: 1.2 seconds.
H0.bridge <- bridge_sampler(fitH0, silent = TRUE)
print(H0.bridge)
#> Bridge sampling estimate of the log marginal likelihood: -37.73301
#> Estimate obtained in 8 iteration(s) via method "normal".
#### Expected output:
## Bridge sampling estimate of the log marginal likelihood: -37.53183
## Estimate obtained in 5 iteration(s) via method "normal".
```](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:lgxrkw556dtxkplwrj5rzlli/bafkreicf6zdyyjmaxrdfyq2sekokprym3c2o5atca6i2cczcx5zec4q4hm)
We are done with the ninth Statistical Methods for Linguistics and Psychology (SMLP) summer school, Potsdam, Germany. The tenth edition is planned for 24-28 August 2026.
Honoured to receive two (!!) SAC highlights awards at #ACL2025 😁 (Conveniently placed on the same slide!) With the amazing: @philipwitti.bsky.social, @gregorbachmann.bsky.social and @wegotlieb.bsky.social, @cuiding.bsky.social, Giovanni Acampa, @alexwarstadt.bsky.social, @tamaregev.bsky.social
ACL@aclmeeting.bsky.social · last yr.
Congratulations to @sinaahmadi.bsky.social and co-authors for receiving an ACL 2025 Outstanding Paper Award for PARME: Parallel Corpora for Low-Resourced Middle Eastern Languages! aclanthology.org/2025.acl-lon...
Next week onwards, I'm teaching a five-day introductory course on Bayesian Data Analysis in Gent. Newly recorded video lectures to accompany the course are now online: vasishth.github.io/LecturesIntr...
Shravan Vasishth's Intro Bayes course home page
vasishth.github.io
📣Take part in 3rd Terminology shared task @WMT!📣 This year: 👉5 language pairs: EN->{ES, RU, DE, ZH}, 👉2 tracks - sentence-level and doc-level translation, 👉authentic data from 2 domains: finance and IT! www2.statmt.org/wmt25/termin... Don't miss an opportunity - we only do it once in two years😏
Terminology Translation Task
www2.statmt.org
Some of my colleagues are already very excited about this work!
Tiago Pimentel@tpimentel.bsky.social · last yr.If you use LLMs, tokenisation bias probably affects you: * Text generation: tokenisation bias ⇒ length bias 🤯 * Psycholinguistics: tokenisation bias ⇒ systematically biased surprisal estimates 🫠 * Interpretability: tokenisation bias ⇒ biased logits 🤔
If you're finishing your camera-ready for ACL or ICML and want to cite co-first authors more fairly, I just made a simple fix to do this! Just add $^*$ to the authors' names in your bibtex, and the citations should change :) github.com/tpimentelms/...
👀 📖 Big news! 📖 👀 Happy to announce the release of the OneStop Eye Movements dataset! 🎉 🎉 OneStop is the product of over 6 years of experimental design, data collection and data curation. github.com/lacclab/OneS...
I am so proud of this work. My first NLP experience. I learned a lot from this amazing team!!!!
Ethan Gotlieb Wilcox@wegotlieb.bsky.social · last yr.⭐🗣️New preprint out: 🗣️⭐ “Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent” with @cuiding.bsky.social , Giovanni Acampa, @tpimentel.bsky.social , @alexwarstadt.bsky.social ,Tamar Regev: arxiv.org/abs/2505.07659
⭐🗣️New preprint out: 🗣️⭐ “Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent” with @cuiding.bsky.social , Giovanni Acampa, @tpimentel.bsky.social , @alexwarstadt.bsky.social ,Tamar Regev: arxiv.org/abs/2505.07659
Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent
This paper argues that the relationship between lexical identity and prosody -- one well-studied parameter of linguistic variation -- can be characterized using information theory. We predict that lan...
arxiv.org
Excited to share our preprint "Using MoTR to probe agreement errors in Russian"! w/ Metehan Oğuz, @wegotlieb.bsky.social, Zuzanna Fuchs Link: osf.io/preprints/ps... 1- We provide moderate evidence that processing of agreement errors is modulated by agreement type (internal vs external agr.)
OSF
osf.io
