Accepted at ACL main! Come chat about dialectal MT at our poster today at 4 pm. Also, check out this largely bug-free package for generating your own synthetic dialectal data: pypi.org/project/dial...

You have a budget to human-evaluate 100 inputs to your models, but your dataset is 10,000 inputs. Do not just pick 100 randomly!🙅 We can do better. "How to Select Datapoints for Efficient Human Evaluation of NLG Models?" shows how.🕵️ (random is still a devilishly good baseline)

🔈When LLMs solve tasks with a mid-to-low resource input or target language, their output quality is poor. We know that. But can we put our finger on what breaks inside the LLM? We introduce the 💥 translation barrier hypothesis 💥 for failed multilingual generation with LLMs. arxiv.org/abs/2506.22724

We know that speech LID systems flunk on accented speech. But why? And what can we do about it? 🤔 Our work arxiv.org/abs/2506.00628 (Interspeech '25) finds that *accent-language confusion* is an important culprit, ties it to the length of feature that the model relies on, and proposes a fix.

Presented DialUp (MT, dialect continua, robustness, etc.; arxiv.org/abs/2501.16581) to some new people this week! Thanks Hale and @schmidtsciences.bsky.social for inviting me up to New York 🥯 Saw some magnolias too :)

Dialects lie on continua of (structured) linguistic variation, right? And we can’t collect data for every point on the continuum...🤔 📢 Check out DialUp, a technique to make your MT model robust to the dialect continua of its training languages, including unseen dialects. arxiv.org/abs/2501.16581