1 code implementation • NAACL (NUSE) 2021 • Michael Yoder, Sopan Khosla, Qinlan Shen, Aakanksha Naik, Huiming Jin, Hariharan Muralidharan, Carolyn Rosé
The pipeline includes modules for character identification and coreference, as well as the attribution of quotes and narration to those characters.
2 code implementations • 1 Feb 2024 • Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, Hannaneh Hajishirzi
Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs.
1 code implementation • 31 Jan 2024 • Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk, David Atkinson, Russell Authur, Ben Bogin, Khyathi Chandu, Jennifer Dumas, Yanai Elazar, Valentin Hofmann, Ananya Harsh Jha, Sachin Kumar, Li Lucy, Xinxi Lyu, Nathan Lambert, Ian Magnusson, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell, Nishant Subramani, Oyvind Tafjord, Pete Walsh, Luke Zettlemoyer, Noah A. Smith, Hannaneh Hajishirzi, Iz Beltagy, Dirk Groeneveld, Jesse Dodge, Kyle Lo
Language models have become a critical technology to tackling a wide range of natural language processing tasks, yet many details about how the best-performing language models were developed are not reported.
1 code implementation • 19 Dec 2023 • Maria Antoniak, Aakanksha Naik, Carla S. Alvarado, Lucy Lu Wang, Irene Y. Chen
Ethical frameworks for the use of natural language processing (NLP) are urgently needed to shape how large language models (LLMs) and similar tools are used for healthcare applications.
1 code implementation • 16 Nov 2023 • Mihir Parmar, Aakanksha Naik, Himanshu Gupta, Disha Agrawal, Chitta Baral
Assessing these models on long sequences is crucial since prior work in the general domain has demonstrated performance degradation of LLMs on longer texts.
no code implementations • 16 Nov 2023 • Aakanksha Naik, Bailey Kuehl, Erin Bransom, Doug Downey, Tom Hope
Focusing on biomedicine, this work presents CARE (Clinical Aggregation-oriented Result Extraction) -- a new IE dataset for the task of extracting clinical findings.
no code implementations • 19 Jun 2023 • Carl Edwards, Aakanksha Naik, Tushar Khot, Martin Burke, Heng Ji, Tom Hope
We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets.
1 code implementation • 30 Apr 2023 • Yuze Lou, Bailey Kuehl, Erin Bransom, Sergey Feldman, Aakanksha Naik, Doug Downey
Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications.
no code implementations • 25 Mar 2023 • Kyle Lo, Joseph Chee Chang, Andrew Head, Jonathan Bragg, Amy X. Zhang, Cassidy Trier, Chloe Anastasiades, Tal August, Russell Authur, Danielle Bragg, Erin Bransom, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Yen-Sung Chen, Evie Yu-Yen Cheng, Yvonne Chou, Doug Downey, Rob Evans, Raymond Fok, Fangzhou Hu, Regan Huff, Dongyeop Kang, Tae Soo Kim, Rodney Kinney, Aniket Kittur, Hyeonsu Kang, Egor Klevak, Bailey Kuehl, Michael Langan, Matt Latzke, Jaron Lochner, Kelsey MacMillan, Eric Marsh, Tyler Murray, Aakanksha Naik, Ngoc-Uyen Nguyen, Srishti Palani, Soya Park, Caroline Paulic, Napol Rachatasumrit, Smita Rao, Paul Sayre, Zejiang Shen, Pao Siangliulue, Luca Soldaini, Huy Tran, Madeleine van Zuylen, Lucy Lu Wang, Christopher Wilhelm, Caroline Wu, Jiangjiang Yang, Angele Zamarron, Marti A. Hearst, Daniel S. Weld
Scholarly publications are key to the transfer of knowledge from scholars to others.
no code implementations • 13 Feb 2023 • Srishti Palani, Aakanksha Naik, Doug Downey, Amy X. Zhang, Jonathan Bragg, Joseph Chee Chang
Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers.
no code implementations • 1 Sep 2022 • Hao-Ren Yao, Luke Breitfeller, Aakanksha Naik, Chunxiao Zhou, Carolyn Rose
Event Temporal Relation Extraction (ETRE) is a crucial yet challenging problem.
1 code implementation • Findings (NAACL) 2022 • Aakanksha Naik, Sravanthi Parasa, Sergey Feldman, Lucy Lu Wang, Tom Hope
We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models.
1 code implementation • 2 Nov 2021 • Aakanksha Naik, Jill Lehman, Carolyn Rose
We reflect on the question: have transfer learning methods sufficiently addressed the poor performance of benchmark-trained models on the long tail?
no code implementations • 15 May 2021 • Luke Breitfeller, Aakanksha Naik, Carolyn Rose
We demonstrate the utility of extracted cues by integrating them with an event ordering model using a joint BiLSTM and ILP constraint architecture.
no code implementations • EACL 2021 • Aakanksha Naik, Jill Lehman, Carolyn Rose
Our best-performing models reach F1 scores of 70. 0 and 72. 9 on notes and conversations respectively, using no labeled data from target domains.
1 code implementation • ACL 2020 • Aakanksha Naik, Carolyn Rosé
We tackle the task of building supervised event trigger identification models which can generalize better across domains.
no code implementations • WS 2019 • Aakanksha Naik, Luke Breitfeller, Carolyn Rose
Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs.
1 code implementation • WS 2019 • Xinru Yan, Aakanksha Naik, Yohan Jo, Carolyn Rose
We propose a novel take on understanding narratives in social media, focusing on learning {''}functional story schemas{''}, which consist of sets of stereotypical functional structures.
no code implementations • ACL 2019 • Aakanksha Naik, Ravich, Abhilasha er, Carolyn Rose, Eduard Hovy
In this work, we show that existing embedding models are inadequate at constructing representations that capture salient aspects of mathematical meaning for numbers, which is important for language understanding.
1 code implementation • CONLL 2019 • Abhilasha Ravichander, Aakanksha Naik, Carolyn Rose, Eduard Hovy
Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language understanding system can reasonably be expected to handle.
1 code implementation • COLING 2018 • Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, Graham Neubig
Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner.
Natural Language Inference Natural Language Understanding +1
no code implementations • WS 2017 • Aakanksha Naik, Chris Bogart, Carolyn Rose
In this paper, we describe a system for automatic construction of user disease progression timelines from their posts in online support groups using minimal supervision.
no code implementations • WS 2017 • Khyathi u, Aakanksha Naik, Ch, Aditya rasekar, Zi Yang, Niloy Gupta, Eric Nyberg
In this paper, we describe our participation in phase B of task 5b of the fifth edition of the annual BioASQ challenge, which includes answering factoid, list, yes-no and summary questions from biomedical data.