1 code implementation • ACL 2020 • Daphne Ippolito, David Grangier, Douglas Eck, Chris Callison-Burch
We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives.
3 code implementations • 9 Jun 2022 • Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew Dai, Andrew La, Andrew Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakaş, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartłomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, César Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodola, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, Ethan Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, Francois Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Kocoń, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jörg Frohberg, Jos Rozen, Jose Hernandez-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, Kory Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Şenel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ramírez Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Orduna Baitemirova, Melody Arnaud, Melvin McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, Michael Strube, Michał Swędrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T, Nanyun Peng, Nathan A. Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha S. Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Miłkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan LeBras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima, Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T. Piantadosi, Stuart M. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsu Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, ZiRui Wang, Ziyi Wu
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
1 code implementation • EMNLP 2018 • Ajay Patel, Alexander Sands, Chris Callison-Burch, Marianna Apidianaki
Vector space embedding models like word2vec, GloVe, fastText, and ELMo are extremely popular representations in natural language processing (NLP) applications.
1 code implementation • ACL 2022 • Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini
As a result, over 1% of the unprompted output of language models trained on these datasets is copied verbatim from the training data.
1 code implementation • 16 Feb 2024 • Ajay Patel, Colin Raffel, Chris Callison-Burch
The rapid rise to prominence of these models and these unique challenges has had immediate adverse impacts on open science and on the reproducibility of work that uses them.
1 code implementation • 11 Sep 2023 • Andrew Zhu, Liam Dugan, Alyssa Hwang, Chris Callison-Burch
Language model applications are becoming increasingly popular and complex, often including features like tool usage and retrieval augmentation.
1 code implementation • 14 Dec 2023 • Yue Yang, Fan-Yun Sun, Luca Weihs, Eli VanderBilt, Alvaro Herrasti, Winson Han, Jiajun Wu, Nick Haber, Ranjay Krishna, Lingjie Liu, Chris Callison-Burch, Mark Yatskar, Aniruddha Kembhavi, Christopher Clark
3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope.
1 code implementation • 31 Jan 2023 • Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, Chris Callison-Burch
While Chain-of-Thought (CoT) prompting boosts Language Models' (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka.
1 code implementation • TACL 2016 • Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, Chris Callison-Burch
Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus.
Ranked #8 on Text Simplification on TurkCorpus
1 code implementation • Findings (EMNLP) 2021 • Daniel Khashabi, Amos Ng, Tushar Khot, Ashish Sabharwal, Hannaneh Hajishirzi, Chris Callison-Burch
GooAQ answers are mined from Google's responses to our collected questions, specifically from the answer boxes in the search results.
1 code implementation • CVPR 2023 • Yue Yang, Artemis Panagopoulou, Shenghao Zhou, Daniel Jin, Chris Callison-Burch, Mark Yatskar
Overall, LaBo demonstrates that inherently interpretable models can be widely applied at similar, or better, performance than black box approaches.
1 code implementation • NAACL 2021 • Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, D{\'\i}dac Sur{\'\i}s, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video).
1 code implementation • 3 Jan 2023 • Li Zhang, Chris Callison-Burch
Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments.
1 code implementation • 2 May 2023 • Andrew Zhu, Karmanya Aggarwal, Alexander Feng, Lara J. Martin, Chris Callison-Burch
Dungeons & Dragons (D&D) is a tabletop roleplaying game with complex natural language interactions between players and hidden state information.
1 code implementation • EMNLP 2020 • Li Zhang, Qing Lyu, Chris Callison-Burch
We propose a suite of reasoning tasks on two types of relations between procedural events: goal-step relations ("learn poses" is a step in the larger goal of "doing yoga") and step-step temporal relations ("buy a yoga mat" typically precedes "learn poses").
1 code implementation • NAACL 2019 • Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris Callison-Burch, Dan Roth
Inherently, this is a natural language understanding task, and we propose to address it as such.
1 code implementation • 8 Jun 2019 • Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris Callison-Burch, Dan Roth
Inherently, this is a natural language understanding task, and we propose to address it as such.
1 code implementation • NAACL (ACL) 2022 • Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
1 code implementation • 1 Jun 2023 • Liam Dugan, Anshul Wadhawan, Kyle Spence, Chris Callison-Burch, Morgan McGuire, Victor Zordan
Recent work in speech-to-speech translation (S2ST) has focused primarily on offline settings, where the full input utterance is available before any output is given.
1 code implementation • 16 Mar 2022 • Liam Dugan, Eleni Miltsakaki, Shriyash Upadhyay, Etan Ginsberg, Hannah Gonzalez, Dayheon Choi, Chuning Yuan, Chris Callison-Burch
We conduct a feasibility study into the applicability of answer-agnostic question generation models to textbook passages.
1 code implementation • Findings (ACL) 2022 • Liam Dugan, Eleni Miltsakaki, Shriyash Upadhyay, Etan Ginsberg, Hannah Gonzalez, DaHyeon Choi, Chuning Yuan, Chris Callison-Burch
We conduct a feasibility study into the applicability of answer-unaware question generation models to textbook passages.
1 code implementation • 24 May 2023 • Li Zhang, Hainiu Xu, Abhinav Kommula, Chris Callison-Burch, Niket Tandon
An earlier dataset, OpenPI, provided crowdsourced annotations of entity state changes in text.
1 code implementation • ACL 2022 • Shuyan Zhou, Li Zhang, Yue Yang, Qing Lyu, Pengcheng Yin, Chris Callison-Burch, Graham Neubig
To this end, we develop a simple and efficient method that links steps (e. g., "purchase a camera") in an article to other articles with similar goals (e. g., "how to choose a camera"), recursively constructing the KB.
1 code implementation • 24 Dec 2022 • Liam Dugan, Daphne Ippolito, Arun Kirubarajan, Sherry Shi, Chris Callison-Burch
As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer.
1 code implementation • 5 Jul 2023 • Sha Li, Ruining Zhao, Manling Li, Heng Ji, Chris Callison-Burch, Jiawei Han
Event schemas are a form of world knowledge about the typical progression of events.
1 code implementation • 26 Jan 2023 • Li Zhang, Hainiu Xu, Yue Yang, Shuyan Zhou, Weiqiu You, Manni Arora, Chris Callison-Burch
By injecting the causal relations between entities and events as intermediate reasoning steps in our representation, we further boost the performance to . 67 F1.
1 code implementation • WS 2017 • Courtney Napoles, Chris Callison-Burch
Our model rivals the current state of the art using a fraction of the training data.
2 code implementations • NAACL 2019 • Reno Kriz, João Sedoc, Marianna Apidianaki, Carolina Zheng, Gaurav Kumar, Eleni Miltsakaki, Chris Callison-Burch
Sentence simplification is the task of rewriting texts so they are easier to understand.
Ranked #4 on Text Simplification on Newsela
1 code implementation • ACL 2019 • Daphne Ippolito, Reno Kriz, Maria Kustikova, João Sedoc, Chris Callison-Burch
While conditional language models have greatly improved in their ability to output high-quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences.
2 code implementations • ACL 2020 • Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, Douglas Eck
Recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text.
2 code implementations • EMNLP 2020 • Liam Dugan, Daphne Ippolito, Arun Kirubarajan, Chris Callison-Burch
In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text.
1 code implementation • EMNLP 2021 • Yue Yang, Artemis Panagopoulou, Qing Lyu, Li Zhang, Mark Yatskar, Chris Callison-Burch
Understanding what sequence of steps are needed to complete a goal can help artificial intelligence systems reason about human activities.
Ranked #1 on VGSI on wikiHow-image
1 code implementation • Asian Chapter of the Association for Computational Linguistics 2020 • Li Zhang, Qing Lyu, Chris Callison-Burch
Modern task-oriented dialog systems need to reliably understand users' intents.
1 code implementation • TACL 2014 • Wei Xu, Alan Ritter, Chris Callison-Burch, William B. Dolan, Yangfeng Ji
We present MultiP (Multi-instance Learning Paraphrase Model), a new model suited to identify paraphrases within the short messages on Twitter.
1 code implementation • EMNLP 2021 • Joongwon Kim, Mounica Maddela, Reno Kriz, Wei Xu, Chris Callison-Burch
We categorize examples in our corpus, and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited.
1 code implementation • INLG (ACL) 2021 • Qing Lyu, Li Zhang, Chris Callison-Burch
The knowledge of scripts, common chains of events in stereotypical scenarios, is a valuable asset for task-oriented natural language understanding systems.
1 code implementation • 21 Feb 2024 • Andrew Zhu, Alyssa Hwang, Liam Dugan, Chris Callison-Burch
One type of question that is commonly found in day-to-day scenarios is ``fan-out'' questions, complex multi-hop, multi-document reasoning questions that require finding information about a large number of entities.
1 code implementation • ACL 2019 • Sihao Chen, Daniel Khashabi, Chris Callison-Burch, Dan Roth
This work presents PerspectroScope, a web-based system which lets users query a discussion-worthy natural language claim, and extract and visualize various perspectives in support or against the claim, along with evidence supporting each perspective.
Natural Language Inference Natural Language Understanding +1
1 code implementation • 15 Dec 2021 • Qing Lyu, Hua Zheng, Daoxin Li, Li Zhang, Marianna Apidianaki, Chris Callison-Burch
We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.
1 code implementation • 8 May 2023 • Josh Magnus Ludan, Yixuan Meng, Tai Nguyen, Saurabh Shah, Qing Lyu, Marianna Apidianaki, Chris Callison-Burch
Large Language Models (LLMs) are so powerful that they sometimes learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on out-of-distribution data.
1 code implementation • 29 Aug 2023 • Zachary Horvitz, Ajay Patel, Chris Callison-Burch, Zhou Yu, Kathleen McKeown
Our parameter-efficient approach, ParaGuide, leverages paraphrase-conditioned diffusion models alongside gradient-based guidance from both off-the-shelf classifiers and strong existing style embedders to transform the style of text while preserving semantic information.
1 code implementation • 16 Apr 2021 • Mohammad Sadegh Rasooli, Chris Callison-Burch, Derry Tanti Wijaya
Our captioning results on Arabic are slightly better than that of its supervised model.
1 code implementation • NAACL (DaSH) 2021 • Rebecca Iglesias-Flores, Megha Mishra, Ajay Patel, Akanksha Malhotra, Reno Kriz, Martha Palmer, Chris Callison-Burch
Acquiring training data for natural language processing systems can be expensive and time-consuming.
1 code implementation • 21 Dec 2022 • Yijiang River Dong, Lara J. Martin, Chris Callison-Burch
In this work, we capitalize on state-of-the-art Code-LLMs, such as Codex, to bootstrap the use of symbolic methods for tracking the state of stories and aiding in story understanding.
1 code implementation • 3 Nov 2023 • Alyssa Hwang, Andrew Head, Chris Callison-Burch
GPT-Vision has impressed us on a range of vision-language tasks, but it comes with the familiar new challenge: we have little idea of its capabilities and limitations.
1 code implementation • 24 Oct 2022 • Yue Yang, Artemis Panagopoulou, Marianna Apidianaki, Mark Yatskar, Chris Callison-Burch
We propose to extract these properties from images and use them in an ensemble model, in order to complement the information that is extracted from language models.
1 code implementation • 30 Oct 2023 • Josh Magnus Ludan, Qing Lyu, Yue Yang, Liam Dugan, Mark Yatskar, Chris Callison-Burch
Black-box deep neural networks excel in text classification, yet their application in high-stakes domains is hindered by their lack of interpretability.
1 code implementation • 30 Aug 2019 • Jeffrey Cheng, Chris Callison-Burch
State-of-the-art machine translation (MT) models do not use knowledge of any single language's structure; this is the equivalent of asking someone to translate from English to German while knowing neither language.
1 code implementation • 10 Dec 2020 • Mohammad Sadegh Rasooli, Farzane Bakhtyari, Fatemeh Shafiei, Mahsa Ravanbakhsh, Chris Callison-Burch
We also show that our model improves English-to-Persian machine translation in scenarios for which the training data is from colloquial Persian with 1. 4 absolute BLEU score difference in the development data, and 0. 8 in the test data.
1 code implementation • NAACL 2021 • Nikzad Khani, Isidora Tourni, Mohammad Sadegh Rasooli, Chris Callison-Burch, Derry Tanti Wijaya
We find that images of words are not always invariant across languages, and that language pairs with shared culture, meaning having either a common language family, ethnicity or religion, have improved image translatability (i. e., have more similar images for similar words) compared to its converse, regardless of their geographic proximity.
Cultural Vocal Bursts Intensity Prediction Multilingual NLP +3
2 code implementations • 16 Feb 2022 • Bryan Li, Lara J. Martin, Chris Callison-Burch
Transformers have been showing near-human performance on a variety of tasks, but they are not without their limitations.
1 code implementation • 26 Apr 2023 • Li Zhang, Liam Dugan, Hainiu Xu, Chris Callison-Burch
Furthermore, we show that the style of code prompt has a large effect on performance for some but not all tasks and that fine-tuning on text instructions leads to better relative performance of code prompts.
1 code implementation • 24 May 2023 • Bryan Li, Samar Haider, Chris Callison-Burch
We then evaluate various multilingual LLMs on our dataset and metrics to probe their internal knowledge and use the proposed metrics to discover numerous inconsistencies in how these models respond in different languages.
no code implementations • 5 Feb 2015 • Kathryn Baker, Michael Bloodgood, Bonnie J. Dorr, Chris Callison-Burch, Nathaniel W. Filardo, Christine Piatko, Lori Levin, Scott Miller
We apply our MN annotation scheme to statistical machine translation using a syntactic framework that supports the inclusion of semantic annotations.
no code implementations • 21 Oct 2014 • Michael Bloodgood, Chris Callison-Burch
We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources.
no code implementations • 20 Oct 2014 • Michael Bloodgood, Chris Callison-Burch
Building machine translation (MT) test sets is a relatively expensive task.
no code implementations • 24 Sep 2014 • Kathryn Baker, Michael Bloodgood, Chris Callison-Burch, Bonnie J. Dorr, Nathaniel W. Filardo, Lori Levin, Scott Miller, Christine Piatko
We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation.
no code implementations • EMNLP 2018 • Anne Cocos, Skyler Wharton, Ellie Pavlick, Marianna Apidianaki, Chris Callison-Burch
Adjectives like {``}warm{''}, {``}hot{''}, and {``}scalding{''} all describe temperature but differ in intensity.
no code implementations • CL 2017 • Ann Irvine, Chris Callison-Burch
We present experiments on a wide range of languages and data sizes.
no code implementations • ACL 2018 • John Hewitt, Daphne Ippolito, Brendan Callahan, Reno Kriz, Derry Tanti Wijaya, Chris Callison-Burch
To facilitate research on the task, we introduce a large-scale multilingual corpus of images, each labeled with the word it represents.
no code implementations • EACL 2017 • Anne Cocos, Chris Callison-Burch
There is a relationship between what we say and where we say it.
no code implementations • NAACL 2018 • Reno Kriz, Eleni Miltsakaki, Marianna Apidianaki, Chris Callison-Burch
Lexical simplification involves identifying complex words or phrases that need to be simplified, and recommending simpler meaning-preserving substitutes that can be more easily understood.
no code implementations • NAACL 2018 • Anne Cocos, Marianna Apidianaki, Chris Callison-Burch
In this paper, we present a head-to-head comparison of six taxonomic organization algorithms that vary with respect to their structural and transitivity constraints, and treatment of synonymy.
no code implementations • NAACL 2018 • Marianna Apidianaki, Guillaume Wisniewski, Anne Cocos, Chris Callison-Burch
We propose a variant of a well-known machine translation (MT) evaluation metric, HyTER (Dreyer and Marcu, 2012), which exploits reference translations enriched with meaning equivalent expressions.
no code implementations • SEMEVAL 2017 • Sneha Rajana, Chris Callison-Burch, Marianna Apidianaki, Vered Shwartz
Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems.
no code implementations • SEMEVAL 2017 • Anne Cocos, Marianna Apidianaki, Chris Callison-Burch
WordNet has facilitated important research in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage.
no code implementations • EMNLP 2017 • Derry Tanti Wijaya, Brendan Callahan, John Hewitt, Jie Gao, Xiao Ling, Marianna Apidianaki, Chris Callison-Burch
Bilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora.
no code implementations • EMNLP 2017 • Ross Mechanic, Dean Fulgoni, Hannah Cutler, Sneha Rajana, Zheyuan Liu, Bradley Jackson, Anne Cocos, Chris Callison-Burch, Marianna Apidianaki
Semantic relation knowledge is crucial for natural language understanding.
no code implementations • WS 2017 • Anne Cocos, Marianna Apidianaki, Chris Callison-Burch
The role of word sense disambiguation in lexical substitution has been questioned due to the high performance of vector space models which propose good substitutes without explicitly accounting for sense.
no code implementations • WS 2017 • Anietie Andy, Mark Dredze, Mugizi Rwebangira, Chris Callison-Burch
EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event.
no code implementations • TACL 2015 • Wei Xu, Chris Callison-Burch, Courtney Napoles
Simple Wikipedia has dominated simplification research in the past 5 years.
no code implementations • TACL 2014 • Ellie Pavlick, Matt Post, Ann Irvine, Dmitry Kachaev, Chris Callison-Burch
We present a large scale study of the languages spoken by bilingual workers on Mechanical Turk (MTurk).
no code implementations • TACL 2013 • Adam Lopez, Matt Post, Chris Callison-Burch, Jonathan Weese, Juri Ganitkevitch, Narges Ahmidi, Olivia Buzek, Leah Hanson, Beenish Jamil, Matthias Lee, Ya-Ting Lin, Henry Pao, Fatima Rivera, Leili Shahriyari, Debu Sinha, Adam Teichert, Stephen Wampler, Michael Weinberger, Daguang Xu, Lin Yang, Shang Zhao
Machine translation (MT) draws from several different disciplines, making it a complex subject to teach.
no code implementations • LREC 2014 • Ryan Cotterell, Chris Callison-Burch
To the best of the authors knowledge, this work is the most diverse corpus of dialectal Arabic in both the source of the content and the number of dialects.
no code implementations • LREC 2014 • Juri Ganitkevitch, Chris Callison-Burch
We release a massive expansion of the paraphrase database (PPDB) that now includes a collection of paraphrases in 23 different languages.
no code implementations • LREC 2014 • Ann Irvine, Joshua Langfus, Chris Callison-Burch
We present the American Local News Corpus (ALNC), containing over 4 billion words of text from 2, 652 online newspapers in the United States.
no code implementations • NAACL 2019 • Jo{\~a}o Sedoc, Daphne Ippolito, Arun Kirubarajan, Jai Thirani, Lyle Ungar, Chris Callison-Burch
We introduce a unified framework for human evaluation of chatbots that augments existing tools and provides a web-based hub for researchers to share and compare their dialog systems.
no code implementations • WS 2019 • Daphne Ippolito, David Grangier, Chris Callison-Burch, Douglas Eck
Story infilling involves predicting words to go into a missing span from a story.
no code implementations • WS 2019 • Anietie Andy, Derry Tanti Wijaya, Chris Callison-Burch
Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public.
no code implementations • WS 2019 • Aina Gar{\'\i} Soler, Anne Cocos, Marianna Apidianaki, Chris Callison-Burch
Word embedding representations provide good estimates of word meaning and give state-of-the art performance in semantic tasks.
no code implementations • TACL 2019 • Anne Cocos, Chris Callison-Burch
Many natural language processing tasks require discriminating the particular meaning of a word in context, but building corpora for developing sense-aware models can be a challenge.
no code implementations • 22 Dec 2020 • Reno Kriz, Marianna Apidianaki, Chris Callison-Burch
Text simplification systems generate versions of texts that are easier to understand for a broader audience.
no code implementations • ACL 2022 • Emily Reif, Daphne Ippolito, Ann Yuan, Andy Coenen, Chris Callison-Burch, Jason Wei
In this paper, we leverage large language models (LMs) to perform zero-shot text style transfer.
no code implementations • EMNLP 2021 • Mohammad Sadegh Rasooli, Chris Callison-Burch, Derry Tanti Wijaya
Our captioning results on Arabic are slightly better than that of its supervised model.
no code implementations • COLING (CRAC) 2020 • Anietie Andy, Chris Callison-Burch, Derry Tanti Wijaya
Many people live-tweet televised events like Presidential debates and popular TV-shows and discuss people or characters in the event.
no code implementations • 11 Nov 2021 • Ann Yuan, Daphne Ippolito, Vitaly Nikolaev, Chris Callison-Burch, Andy Coenen, Sebastian Gehrmann
We use our method to curate SynthBio - a new evaluation set for WikiBio - composed of structured attribute lists describing fictional individuals, mapped to natural language biographies.
no code implementations • 17 Nov 2021 • Yue Yang, Joongwon Kim, Artemis Panagopoulou, Mark Yatskar, Chris Callison-Burch
Schemata are structured representations of complex tasks that can aid artificial intelligence by allowing models to break down complex tasks into intermediate steps.
no code implementations • 16 Mar 2022 • Anietie Andy, Siyi Liu, Daphne Ippolito, Reno Kriz, Chris Callison-Burch, Derry Wijaya
While popular televised events such as presidential debates or TV shows are airing, people provide commentary on them in real-time.
no code implementations • 25 May 2022 • Damilola Omitaomu, Shabnam Tafreshi, Tingting Liu, Sven Buechel, Chris Callison-Burch, Johannes Eichstaedt, Lyle Ungar, João Sedoc
Hence, we collected detailed characterization of the participants' traits, their self-reported empathetic response to news articles, their conversational partner other-report, and turn-by-turn third-party assessments of the level of self-disclosure, emotion, and empathy expressed.
no code implementations • 9 Jun 2022 • Daphne Ippolito, Liam Dugan, Emily Reif, Ann Yuan, Andy Coenen, Chris Callison-Burch
The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural language generation (NLG) system to craft text.
no code implementations • Findings (NAACL) 2022 • Daphne Ippolito, Liam Dugan, Emily Reif, Ann Yuan, Andy Coenen, Chris Callison-Burch
While previous work has tackled this problem with models trained specifically to do fill in the blank, a more useful model is one that can effectively perform _both_ FitB and continuation tasks.
no code implementations • NAACL 2022 • Qing Lyu, Zheng Hua, Daoxin Li, Li Zhang, Marianna Apidianaki, Chris Callison-Burch
We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.
1 code implementation • 6 Sep 2022 • Bryan Li, Mohammad Sadegh Rasooli, Ajay Patel, Chris Callison-Burch
We propose a two-stage approach for training a single NMT model to translate unseen languages both to and from English.
no code implementations • 22 Sep 2022 • Qing Lyu, Marianna Apidianaki, Chris Callison-Burch
In this survey, we review over 110 model explanation methods in NLP through the lens of faithfulness.
no code implementations • 29 Sep 2022 • Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli, Noah Constant, Colin Raffel, Chris Callison-Burch
An arbitrary task can be reformulated as a natural language prompt, and a language model can be asked to generate the completion, indirectly performing the task in a paradigm known as prompt-based learning.
no code implementations • LREC 2022 • Anietie Andy, Reno Kriz, Sharath Chandra Guntuku, Derry Tanti Wijaya, Chris Callison-Burch
While popular Television (TV) shows are airing, some users interested in these shows publish social media posts about the show.
no code implementations • NIDCP (LREC) 2022 • James Fiumara, Christopher Cieri, Mark Liberman, Chris Callison-Burch, Jonathan Wright, Robert Parker
NIEUW leverages the power of novel incentives to elicit linguistic data and annotations from a wide variety of contributors including citizen scientists, game players, and language students and professionals.
no code implementations • 13 Oct 2022 • Chris Callison-Burch, Gaurav Singh Tomar, Lara J. Martin, Daphne Ippolito, Suma Bailis, David Reitter
In this paper, we frame D&D specifically as a dialogue system challenge, where the tasks are to both generate the next conversational turn in the game and predict the state of the game given the dialogue history.
no code implementations • 18 Dec 2022 • Ajay Patel, Nicholas Andrews, Chris Callison-Burch
Existing unsupervised approaches like STRAP have largely focused on style transfer to target authors with many examples of their writing style in books, speeches, or other published works.
no code implementations • 20 Dec 2022 • Pei Zhou, Andrew Zhu, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, Prithviraj Ammanabrolu
We propose a novel task, G4C, to study teacher-student natural language interactions in a goal-driven and grounded environment.
no code implementations • 25 Feb 2023 • Tianyi Zhang, Isaac Tham, Zhaoyi Hou, Jiaxuan Ren, Liyang Zhou, Hainiu Xu, Li Zhang, Lara J. Martin, Rotem Dror, Sha Li, Heng Ji, Martha Palmer, Susan Brown, Reece Suchocki, Chris Callison-Burch
Schema induction builds a graph representation explaining how events unfold in a scenario.
1 code implementation • 24 Apr 2023 • Bryan Li, Chris Callison-Burch
This work proposes a synthetic data generation method for cross-lingual QA which leverages indirect supervision from existing parallel corpora.
no code implementations • 22 May 2023 • Ajay Patel, Delip Rao, Ansh Kothary, Kathleen McKeown, Chris Callison-Burch
Style representation learning builds content-independent representations of author style in text.
no code implementations • 29 May 2023 • Qing Lyu, Marianna Apidianaki, Chris Callison-Burch
The representation space of pretrained Language Models (LMs) encodes rich information about words and their relationships (e. g., similarity, hypernymy, polysemy) as well as abstract semantic notions (e. g., intensity).
no code implementations • 15 Aug 2023 • Andrew Zhu, Lara J. Martin, Andrew Head, Chris Callison-Burch
The role of a Dungeon Master, or DM, in the game Dungeons & Dragons is to perform multiple tasks simultaneously.
no code implementations • 21 Sep 2023 • Zhaoyi Joey Hou, Li Zhang, Chris Callison-Burch
Script learning studies how stereotypical events unfold, enabling machines to reason about narratives with implicit information.
no code implementations • 16 Oct 2023 • Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen, Oyvind Tafjord, Niket Tandon, Li Zhang, Chris Callison-Burch, Peter Clark
Language agents have shown some ability to interact with an external environment, e. g., a virtual world such as ScienceWorld, to perform complex tasks, e. g., growing a plant, without the startup costs of reinforcement learning.
no code implementations • 21 Feb 2024 • Qing Lyu, Kumar Shridhar, Chaitanya Malaviya, Li Zhang, Yanai Elazar, Niket Tandon, Marianna Apidianaki, Mrinmaya Sachan, Chris Callison-Burch
Accurately gauging the confidence level of Large Language Models' (LLMs) predictions is pivotal for their reliable application.
no code implementations • 29 Feb 2024 • Tianyi Zhang, Li Zhang, Zhaoyi Hou, Ziyu Wang, Yuling Gu, Peter Clark, Chris Callison-Burch, Niket Tandon
Planning in a text-based environment continues to be a major challenge for AI systems.
no code implementations • 20 Mar 2024 • Yiming Huang, Weilin Wan, Yue Yang, Chris Callison-Burch, Mark Yatskar, Lingjie Liu
Text-to-motion models excel at efficient human motion generation, but existing approaches lack fine-grained controllability over the generation process.