Screenshot of the first page of the paper titled "Responsible AI Considerations in Text Summarization Research: A Review of Current Practices", authored by Yu Lu Liu, Meng Cao, Su Lin Blodgett, Jackie Chi Kit Cheung, Alexandra Olteanu and Adam Trischler.

🚚 Moving threads about my #nlp papers from Twitter to here 🚚

How and when, and with which issues, does the text summarization community engage with responsible AI? 🤔 In this #EMNLP2023 paper, we examine reporting and research practices across 300 summarization papers published between 2020-2022 🧵

A paper on the topic by Max Glockner, Ieva Raminta Staliūnaitė, James Thorne, Gisela Vallejo, Andreas Vlachos and Iryna Gurevych was accepted to TACL and has just been presented at #EMNLP2023.

📄 arxiv.org/abs/2104.00640 

➡️ bsky.app/profile/ukpl...

At #EMNLP2023, our colleague Jonathan Tonglet (@tongletj.bsky.social) presented his master thesis, conducted at the KU Leuven. Find out more about »SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA« in the thread 🧵 below:

➡️ bsky.app/profile/ukpl...

Many models produce outputs that are hard to verify for an end-user. Our new #emnlp2023 paper won an outstanding paper award! 🏆🎉

We show that providing a quality estimation model, can make a user better at deciding when to rely on the model.

Paper: arxiv.org/pdf/2310.169...

A group photo from the poster presentation of »AmbiFC: Fact-Checking Ambiguous Claims with Evidence«, co-authored by our colleague Max Glockner, Ieva Staliūnaitė, James Thorne, Gisela Vallejo, Andreas Vlachos and Iryna Gurevych. #EMNLP2023

A successful EMNLP Meeting has come to an end! A group photo of our colleagues Yongxin Huang, @tongletj.bsky.social, Aniket Pramanick, Sukannya Purkayastha, Dominic Petrak and Max Glockner, who represented the UKP Lab in Singapore! #EMNLP2023

Graph showing the performance at 12 tasks of the Pythia suite of models at various stages during training. Five tasks (TruthfulQA-MC1, TruthfulQA-MC2, Hindsight Neglect, Memo Trap, and Pattern Match Suppression) show clear evidence of inverse scaling, and three others (Redefine, Repetitive Algebra, and Resisting Correction) show inverse scaling for the largest model.

Looking forward to the final day of EMMLP! Let me know if you want to chat about our Findings paper: “Emergent inabilities? Inverse scaling over the course of pretraining” arxiv.org/abs/2305.14681 #EMNLP #EMNLP2023

You can find our paper here:
📃https://arxiv.org/abs/2311.00408 
and our code here:
💻https://github.com/UKPLab/AdaSent

Check out the work of our authors Yongxin Huang, Kexin Wang, Sourav Dutta, Raj Nath Patel, Goran Glavaš and Iryna Gurevych! (7/🧵) #EMNLP2023

If the base PLM is domain-adapted with another loss, the adapter won’t be compatible any more, reflected in a performance drop. (6/🧵) #EMNLP2023

What makes the difference 🧐 ?
We attribute the effectiveness of the sentence encoding adapter to the consistency between the pre-training and DAPT objectives of the base PLM. (5/🧵) #EMNLP2023

AdaSent decouples DAPT and SEPT by storing the sentence encoding abilities into an adapter, which is trained only once in the general domain and plugged into various DAPT-ed PLMs. It can match or surpass the performance of DAPT→SEPT, with more efficient training. (4/🧵) #EMNLP2023

Domain-adapted sentence embeddings can be created by applying general-domain SEPT on top of a domain-adapted base PLM (DAPT→SEPT). But this requires the same SEPT procedure to be done on each DAPT-ed PLM for every domain, resulting in computational inefficiency. (3/🧵) #EMNLP2023

In our #EMNLP2023 paper we demonstrate AdaSent's effectiveness in extensive experiments on 17 different few-shot sentence classification datasets! It matches or surpasses the performance of full SEPT on DAPT-ed PLM (DAPT→SEPT) while substantially reducing training costs. (2/🧵)

Need a lightweight solution for few-shot domain-specific sentence classification?

We propose AdaSent!
🚀 Up to 7.2 acc. gain in 8-shot classification with 10K unlabeled data
🪶 Small backbone with 82M parameters
🧩 Reusable general sentence adapter across domains
(1/🧵) #EMNLP2023

Which factors shape #NLProc research over time? This was the topic of the talk by our colleague Aniket Pramanick at #EMNLP2023!

Learn more about the paper by him, Yufang Hou, Saif M. Mohammad & Iryna Gurevych here: 📄 arxiv.org/abs/2305.12920

Some new theoretical and empirical results from Tiago, Clara Meister, @wegotlieb.bsky.social, me, and Ryan Cotterell on surprisal and word lengths. I was particularly intrigued to see that surprisal from better LMs correlate less with word length than from worse LMs. #EMNLP2023

Screenshot of paper's title. Paper title is: "Revisiting the Optimality of Word Lengths"

Are you interested in word lengths and natural language’s efficiency? If yes, check out our new #EMNLP2023 paper! It has everything you need: drama, suspense, a new derivation of Zipf’s law, an update to Piantadosi et al’s classic word length paper, transformers... 😄

arxiv.org/abs/2312.03897

If you are around at #EMNLP2023, look out for our colleague Sukannya Purkayastha, who presented today our paper on the use of Jiu-Jitsu argumentation in #PeerReview, authored by her, Anne Lauscher (Universität Hamburg) and Iryna Gurevych.

📑 arxiv.org/abs/2311.03998

Check out the full paper on arXiv and the code on GitLab – we look forward to your thoughts and feedback! (9/9) #NLProc #eRisk #EMNLP2023

Paper 📄 arxiv.org/abs/2211.07624
Code ⌨️ gitlab.irlab.org/anxo.pvila/s...

We also illustrate how our semantic retrieval pipeline provides interpretability of the symptom estimation, highlighting the most relevant sentences. (8/🧵) #EMNLP2023

Our approaches achieve good performance in two Reddit benchmark collections (DCHR metric). (7/🧵) #EMNLP2023

With this aim, we introduce two data selection strategies to detect representative sentences, both unsupervised & semi-supervised.

For the latter, we propose an annotation schema to obtain relevant training samples. (6/🧵) #EMNLP2023

We build symptom-classifiers following the BDI-II 📋 using a semantic retrieval pipeline to predict every symptom decision.

Our pipeline searches for semantic similarities over an index of representative sentences for each symptom. (5/🧵) #EMNLP2023

Our goal❗: Estimate depression severity levels for social media users based on their posts. We automatically relate their responses to the BDI-II and calculate their BDI-score related to four depression levels. (4/🧵) #EMNLP2023

We propose an approach which incorporates clinical questionnaires to detect the presence of symptom markers. We adhere to the BDI-II questionnaire 📋, which includes 21 clinically validated symptoms with four alternative responses. (3/🧵) #EMNLP2023

🧠 Discover how using a method that associates users’ writing semantics with fine-grained depression symptoms can be helpful in the diagnosis of the disease! 

Our data-efficient method is compatible with any kind of SBERT model! (2/🧵) #EMNLP2023

Can we estimate depression severity from social media texts without much training data?

✅ Yes! Semantic Similarity Models come to the rescue!

Check out our latest #EMNLP2023 paper by Anxo Pérez, Neha Warikoo, Kexin Wang, Javier Parapar and Iryna Gurevych – (1/🧵)

📝 arxiv.org/abs/2211.07624

Happening now! Come to the Law Law Land in Aquarius 2 for 🐤BoF session of 🪶 NLP on legal text ⚖️ #EMNLP2023

Presenting this at the 2pm poster session today! #EMNLP #EMNLP2023

I’m in Singapore for #EMNLP2023! ✈️

Say hi if you’d like to chat about cultural analytics, healthcare applications, ethics and social biases, or anything else.

I’m headed to Cambodia afterwards so if you have travel tips for me, I’d also love to chat about that! 🌴