no code implementations • EMNLP (newsum) 2021 • Haoran Li, Arash Einolghozati, Srinivasan Iyer, Bhargavi Paranjape, Yashar Mehdad, Sonal Gupta, Marjan Ghazvininejad
To achieve the best of both worlds, we propose EASE, an extractive-abstractive framework that generates concise abstractive summaries that can be traced back to an extractive summary.
no code implementations • 16 Nov 2023 • Yanai Elazar, Bhargavi Paranjape, Hao Peng, Sarah Wiegreffe, Khyathi Raghavi, Vivek Srikumar, Sameer Singh, Noah A. Smith
Previous work has found that datasets with paired inputs are prone to correlations between a specific part of the input (e. g., the hypothesis in NLI) and the label; consequently, models trained only on those outperform chance.
1 code implementation • 27 May 2023 • Qingqing Cao, Bhargavi Paranjape, Hannaneh Hajishirzi
Large-scale vision language (VL) models use Transformers to perform cross-modal interactions between the input text and image.
2 code implementations • 16 Mar 2023 • Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, Marco Tulio Ribeiro
We introduce Automatic Reasoning and Tool-use (ART), a framework that uses frozen LLMs to automatically generate intermediate reasoning steps as a program.
1 code implementation • 2 Dec 2022 • Bhargavi Paranjape, Pradeep Dasigi, Vivek Srikumar, Luke Zettlemoyer, Hannaneh Hajishirzi
We propose AGRO -- Adversarial Group discovery for Distributionally Robust Optimization -- an end-to-end approach that jointly identifies error-prone groups and improves accuracy on them.
1 code implementation • 10 Oct 2022 • Tanay Dixit, Bhargavi Paranjape, Hannaneh Hajishirzi, Luke Zettlemoyer
We present COunterfactual Generation via Retrieval and Editing (CORE), a retrieval-augmented generation framework for creating diverse counterfactual perturbations for CDA.
no code implementations • ACL 2022 • Bhargavi Paranjape, Matthew Lamm, Ian Tenney
To address these challenges, we develop a Retrieve-Generate-Filter(RGF) technique to create counterfactual evaluation and training data with minimal human supervision.
no code implementations • Findings (ACL) 2021 • Bhargavi Paranjape, Julian Michael, Marjan Ghazvininejad, Luke Zettlemoyer, Hannaneh Hajishirzi
Many commonsense reasoning NLP tasks involve choosing between one or more possible answers to a question or prompt based on knowledge that is often implicit.
no code implementations • 14 May 2021 • Haoran Li, Arash Einolghozati, Srinivasan Iyer, Bhargavi Paranjape, Yashar Mehdad, Sonal Gupta, Marjan Ghazvininejad
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability.
no code implementations • EMNLP 2021 • Kushal Lakhotia, Bhargavi Paranjape, Asish Ghoshal, Wen-tau Yih, Yashar Mehdad, Srinivasan Iyer
Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for NLP tasks such as Question Answering (QA) and Fact Verification.
2 code implementations • EMNLP 2020 • Bhargavi Paranjape, Mandar Joshi, John Thickstun, Hannaneh Hajishirzi, Luke Zettlemoyer
Decisions of complex language understanding models can be rationalized by limiting their inputs to a relevant subsequence of the original text.
no code implementations • WS 2019 • Bhargavi Paranjape, Graham Neubig
Utterance-level analysis of the speaker{'}s intentions and emotions is a core task in conversational understanding.
1 code implementation • IJCNLP 2019 • Alankar Jain, Bhargavi Paranjape, Zachary C. Lipton
Although over 100 languages are supported by strong off-the-shelf machine translation systems, only a subset of them possess large annotated corpora for named entity recognition.
Ranked #4 on Cross-Lingual NER on CoNLL 2003
no code implementations • 5 Dec 2018 • Aditi Chaudhary, Bhargavi Paranjape, Michiel de Jong
Motivated by recent evidence pointing out the fragility of high-performing span prediction models, we direct our attention to multiple choice reading comprehension.
1 code implementation • ICML 2017 • Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, Prateek Jain
Such applications demand prediction models with small storage and computational complexity that do not compromise significantly on accuracy.
4 code implementations • EMNLP 2017 • Dheeraj Mekala, Vivek Gupta, Bhargavi Paranjape, Harish Karnick
We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation.