no code implementations • 14 Oct 2021 • Prafulla Kumar Choubey, Alexander R. Fabbri, Jesse Vig, Chien-Sheng Wu, Wenhao Liu, Nazneen Fatema Rajani
Then, we fine-tune a base summarization model, which is trained on all training samples, on the clean (noisy) subset to obtain an \textit{expert} (\textit{anti-expert}) model.
1 code implementation • 8 Oct 2021 • Tanya Goyal, Nazneen Fatema Rajani, Wenhao Liu, Wojciech Kryściński
Summarization systems make numerous "decisions" about summary properties during inference, e. g. degree of copying, specificity and length of outputs, etc.
no code implementations • NAACL 2021 • Karan Goel, Laurel Orr, Nazneen Fatema Rajani, Jesse Vig, Christopher R{\'e}
If not, how easily can such a system be repurposed for their use case?
2 code implementations • ACL 2021 • Jesse Vig, Wojciech Kryściński, Karan Goel, Nazneen Fatema Rajani
Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization.
1 code implementation • EMNLP 2021 • Han Guo, Nazneen Fatema Rajani, Peter Hase, Mohit Bansal, Caiming Xiong
With the availability of the fast influence functions, we demonstrate their usefulness in four applications.
no code implementations • 1 Dec 2020 • Pascal Sturmfels, Jesse Vig, Ali Madani, Nazneen Fatema Rajani
Recent deep-learning approaches to protein prediction have shown that pre-training on unlabeled data can yield useful representations for downstream tasks.
no code implementations • 18 Oct 2020 • Nazneen Fatema Rajani, Ben Krause, Wengpeng Yin, Tong Niu, Richard Socher, Caiming Xiong
Interpretability techniques in NLP have mainly focused on understanding individual predictions using attention visualization or gradient-based saliency maps over tokens.
no code implementations • 14 Oct 2020 • Lav R. Varshney, Nazneen Fatema Rajani, Richard Socher
Human creativity is often described as the mental process of combining associative elements into a new form, but emerging computational creativity algorithms may not operate in this manner.
1 code implementation • INLG (ACL) 2020 • Qingyun Wang, Qi Zeng, Lifu Huang, Kevin Knight, Heng Ji, Nazneen Fatema Rajani
To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison.
1 code implementation • EMNLP 2020 • Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev, Richard Socher, Caiming Xiong
We demonstrate that this framework enables a pretrained entailment model to work well on new entailment domains in a few-shot setting, and show its effectiveness as a unified solver for several downstream NLP tasks such as question answering and coreference resolution when the end-task annotations are limited.
3 code implementations • Findings (EMNLP) 2021 • Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, Nazneen Fatema Rajani
While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of the distribution they generate.
2 code implementations • NAACL 2021 • Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures.
2 code implementations • ICLR 2021 • Jesse Vig, Ali Madani, Lav R. Varshney, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
Transformer architectures have proven to learn useful representations for protein classification and generation tasks.
1 code implementation • ACL 2020 • Tianlu Wang, Xi Victoria Lin, Nazneen Fatema Rajani, Bryan McCann, Vicente Ordonez, Caiming Xiong
Word embeddings derived from human-generated corpora inherit strong gender bias which can be further amplified by downstream models.
2 code implementations • ACL 2020 • Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, Dragomir Radev
Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions.
2 code implementations • ACL 2020 • Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, Byron C. Wallace
We propose several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how faithful these rationales are (i. e., the degree to which provided rationales influenced the corresponding predictions).
1 code implementation • ACL 2019 • Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, Richard Socher
Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input.
Ranked #22 on Common Sense Reasoning on CommonsenseQA
no code implementations • NAACL 2018 • Nazneen Fatema Rajani, Raymond Mooney
We propose four categories of auxiliary features for ensembling for VQA.
no code implementations • WS 2017 • Nazneen Fatema Rajani, Mihaela Bornea, Ken Barker
In the medical domain, it is common to link text spans to medical concepts in large, curated knowledge repositories such as the Unified Medical Language System.
no code implementations • 27 May 2016 • Nazneen Fatema Rajani, Raymond J. Mooney
Ensembling methods are well known for improving prediction accuracy.
no code implementations • 16 Apr 2016 • Nazneen Fatema Rajani, Raymond J. Mooney
We present results on combining supervised and unsupervised methods to ensemble multiple systems for two popular Knowledge Base Population (KBP) tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity Discovery and Linking (TEDL).