RT by @wikiresearch: The video of the presentation w/ @rnav_arora of our #EMNLP2023 paper on Transparent Stance Detection in Multilingual Wikipedia Editor Discussions predicting Wikipedia policies for content moderation is now online at
https://youtu.be/UUuC6Q1SIoM?t=2190 https://twitter.com/frimelle/status/1747919662405353701
#emnlp2023
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Some highlights from EMNLP 2023
RT by @wikiresearch: Excited to start the new year by presenting our #EMNLP2023 paper on Transparent Stance Detection in Multilingual Wikipedia Editor Discussions w/ @rnav_arora @IAugenstein at the @Wikimedia Research Showcase!
Online, 17.01., 17:30 UTC
https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase#January_2024 @wikiresearch https://twitter.com/frimelle/status/1746569501284368467
MediaWikiWikimedia Research/Showcase - MediaWiki
RT by @wikiresearch: Thanks @wikiresearch for sharing!
Our #emnlp2023 paper is also available on ACL Anthology now: https://aclanthology.org/2023.emnlp-main.100/ https://twitter.com/ConiaSimone/status/1740084544881963053
ACL AnthologyIncreasing Coverage and Precision of Textual Information in Multilingual Knowledge GraphsSimone Conia, Min Li, Daniel Lee, Umar Minhas, Ihab Ilyas, Yunyao Li. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
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A paper on the topic by Max Glockner (UKP Lab), @ievaraminta Staliūnaitė (University of Cambridge), James Thorne (KAIST AI), Gisela Vallejo (University of Melbourne), Andreas Vlachos (University of Cambridge) and Iryna Gurevych was accepted to TACL and has just been presented at #EMNLP2023.
arXiv.orgAmbiFC: Fact-Checking Ambiguous Claims with EvidenceAutomated fact-checking systems verify claims against evidence to predict their veracity. In real-world scenarios, the retrieved evidence may not unambiguously support or refute the claim and yield conflicting but valid interpretations. Existing fact-checking datasets assume that the models developed with them predict a single veracity label for each claim, thus discouraging the handling of such ambiguity. To address this issue we present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. It contains fine-grained evidence annotations of 50k passages from 5k Wikipedia pages. We analyze the disagreements arising from ambiguity when comparing claims against evidence in AmbiFC, observing a strong correlation of annotator disagreement with linguistic phenomena such as underspecification and probabilistic reasoning. We develop models for predicting veracity handling this ambiguity via soft labels and find that a pipeline that learns the label distribution for sentence-level evidence selection and veracity prediction yields the best performance. We compare models trained on different subsets of AmbiFC and show that models trained on the ambiguous instances perform better when faced with the identified linguistic phenomena.
At #EMNLP2023, our colleague Jonathan Tonglet 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 this thread 
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A group photo from the poster presentation of »AmbiFC: Fact-Checking Ambiguous Claims with Evidence«, co-authored by our colleague Max Glockner, @ievaraminta, James Thorne, Gisela Vallejo, Andreas Vlachos and Iryna Gurevych. #EMNLP2023
A successful #EMNLPMeeting has come to an end! A group photo of our colleagues Yongxin Huang, Jonathan Tonglet, Aniket Pramanick, Sukannya Purkayastha, Dominic Petrak and Max Glockner, who represented the UKP Lab in Singapore! #EMNLP2023
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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! (6/) #EMNLP2023 #AdaSent #NLProc
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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. If the base PLM is domain-adapted with another loss, the adapter won’t be compatible any more, reflected in a performance drop. (5/) #EMNLP2023
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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
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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
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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: 
https://arxiv.org/abs/2305.12920
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.
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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 
https://arxiv.org/abs/2211.07624
Code 
https://gitlab.irlab.org/anxo.pvila/semantic-4-depression
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We also illustrate how our semantic retrieval pipeline provides interpretability of the symptom estimation, highlighting the most relevant sentences. (8/) #EMNLP2023 #NLProc
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Our approaches achieve good performance in two Reddit benchmark collections (DCHR metric). (7/) #EMNLP2023 #NLProc
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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