no code implementations • ACL (CASE) 2021 • Salvatore Giorgi, Vanni Zavarella, Hristo Tanev, Nicolas Stefanovitch, Sy Hwang, Hansi Hettiarachchi, Tharindu Ranasinghe, Vivek Kalyan, Paul Tan, Shaun Tan, Martin Andrews, Tiancheng Hu, Niklas Stoehr, Francesco Ignazio Re, Daniel Vegh, Dennis Atzenhofer, Brenda Curtis, Ali Hürriyetoğlu
Evaluating the state-of-the-art event detection systems on determining spatio-temporal distribution of the events on the ground is performed unfrequently.
no code implementations • ACL (CASE) 2021 • Tiancheng Hu, Niklas Stoehr
An ever-increasing amount of text, in the form of social media posts and news articles, gives rise to new challenges and opportunities for the automatic extraction of socio-political events.
no code implementations • ACL (CASE) 2021 • Francesco Re, Daniel Vegh, Dennis Atzenhofer, Niklas Stoehr
This paper accompanies our top-performing submission to the CASE 2021 shared task, which is hosted at the workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text.
no code implementations • ACL (SIGMORPHON) 2021 • Tiago Pimentel, Maria Ryskina, Sabrina J. Mielke, Shijie Wu, Eleanor Chodroff, Brian Leonard, Garrett Nicolai, Yustinus Ghanggo Ate, Salam Khalifa, Nizar Habash, Charbel El-Khaissi, Omer Goldman, Michael Gasser, William Lane, Matt Coler, Arturo Oncevay, Jaime Rafael Montoya Samame, Gema Celeste Silva Villegas, Adam Ek, Jean-Philippe Bernardy, Andrey Shcherbakov, Aziyana Bayyr-ool, Karina Sheifer, Sofya Ganieva, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Andrew Krizhanovsky, Natalia Krizhanovsky, Clara Vania, Sardana Ivanova, Aelita Salchak, Christopher Straughn, Zoey Liu, Jonathan North Washington, Duygu Ataman, Witold Kieraś, Marcin Woliński, Totok Suhardijanto, Niklas Stoehr, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Richard J. Hatcher, Emily Prud'hommeaux, Ritesh Kumar, Mans Hulden, Botond Barta, Dorina Lakatos, Gábor Szolnok, Judit Ács, Mohit Raj, David Yarowsky, Ryan Cotterell, Ben Ambridge, Ekaterina Vylomova
This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features.
no code implementations • 6 Apr 2024 • Kevin Du, Vésteinn Snæbjarnarson, Niklas Stoehr, Jennifer C. White, Aaron Schein, Ryan Cotterell
To answer a question, language models often need to integrate prior knowledge learned during pretraining and new information presented in context.
1 code implementation • 28 Mar 2024 • Niklas Stoehr, Mitchell Gordon, Chiyuan Zhang, Owen Lewis
Can we localize the weights and mechanisms used by a language model to memorize and recite entire paragraphs of its training data?
1 code implementation • 13 Sep 2023 • Niklas Stoehr, Pengxiang Cheng, Jing Wang, Daniel Preotiuc-Pietro, Rajarshi Bhowmik
We compare pairwise, pointwise and listwise prompting techniques to elicit a language model's ranking knowledge.
no code implementations • 12 Jul 2023 • Giuseppe Russo, Niklas Stoehr, Manoel Horta Ribeiro
Conspiracy Theory Identication task is a new shared task proposed for the first time at the Evalita 2023.
1 code implementation • 6 Jul 2023 • Kevin Du, Lucas Torroba Hennigen, Niklas Stoehr, Alexander Warstadt, Ryan Cotterell
Many popular feature-attribution methods for interpreting deep neural networks rely on computing the gradients of a model's output with respect to its inputs.
1 code implementation • 7 Jun 2023 • Andreas Opedal, Niklas Stoehr, Abulhair Saparov, Mrinmaya Sachan
In this paper, we consolidate previous work on categorizing and representing math story problems and develop MathWorld, which is a graph-based semantic formalism specific for the domain of math story problems.
1 code implementation • 23 Feb 2023 • Mian Zhong, Shehzaad Dhuliawala, Niklas Stoehr
We cast victim count extraction as a question answering (QA) task with a regression or classification objective.
1 code implementation • 8 Dec 2022 • Niklas Stoehr, Benjamin J. Radford, Ryan Cotterell, Aaron Schein
For discrete data, SSMs commonly do so through a state-to-action emission matrix and a state-to-state transition matrix.
no code implementations • 21 Nov 2022 • Ali Hürriyetoğlu, Osman Mutlu, Fırat Duruşan, Onur Uca, Alaeddin Selçuk Gürel, Benjamin Radford, Yaoyao Dai, Hansi Hettiarachchi, Niklas Stoehr, Tadashi Nomoto, Milena Slavcheva, Francielle Vargas, Aaqib Javid, Fatih Beyhan, Erdem Yörük
The CASE 2022 extension consists of expanding the test data with more data in previously available languages, namely, English, Hindi, Portuguese, and Spanish, and adding new test data in Mandarin, Turkish, and Urdu for Sub-task 1, document classification.
no code implementations • 11 Nov 2022 • Tiago Pimentel, Josef Valvoda, Niklas Stoehr, Ryan Cotterell
This shift in perspective leads us to propose a new principle for probing, the architectural bottleneck principle: In order to estimate how much information a given component could extract, a probe should look exactly like the component.
1 code implementation • 11 Oct 2022 • Clément Lefebvre, Niklas Stoehr
In this work, we propose PR-ENT, a new event coding approach that is more flexible and resource-efficient, while maintaining competitive accuracy: first, we extend an event description such as "Military injured two civilians'' by a template, e. g. "People were [Z]" and prompt a pre-trained (cloze) language model to fill the slot Z.
1 code implementation • 8 Oct 2022 • Niklas Stoehr, Lucas Torroba Hennigen, Josef Valvoda, Robert West, Ryan Cotterell, Aaron Schein
It is based only on the action category ("what") and disregards the subject ("who") and object ("to whom") of an event, as well as contextual information, like associated casualty count, that should contribute to the perception of an event's "intensity".
no code implementations • LREC 2022 • Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Benoît Sagot, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova
The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.
1 code implementation • EMNLP 2021 • Niklas Stoehr, Lucas Torroba Hennigen, Samin Ahbab, Robert West, Ryan Cotterell
We do this by devising a set of textual and graph-based features which represent each of the causes.
1 code implementation • ICLR Workshop GTRL 2021 • Cristina Guzman, Daphna Keidar, Tristan Meynier, Andreas Opedal, Niklas Stoehr
We first learn the generative BA parameters in a supervised fashion using a Graph Neural Network (GNN) and a Random Forest Regressor, by minimizing the squared loss between the true generative parameters and the latent variables.
1 code implementation • 27 Mar 2020 • Fabian Stephany, Niklas Stoehr, Philipp Darius, Leonie Neuhäuser, Ole Teutloff, Fabian Braesemann
This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it allows for a real-time analysis of risk assessments.
1 code implementation • 12 Oct 2019 • Niklas Stoehr, Emine Yilmaz, Marc Brockschmidt, Jan Stuehmer
While a wide range of interpretable generative procedures for graphs exist, matching observed graph topologies with such procedures and choices for its parameters remains an open problem.