Search Results for author: Bhargavi Paranjape

Found 16 papers, 8 papers with code

Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals

no code implementations16 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.

counterfactual Natural Language Inference +1

PuMer: Pruning and Merging Tokens for Efficient Vision Language Models

1 code implementation27 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.

ART: Automatic multi-step reasoning and tool-use for large language models

2 code implementations16 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.

AGRO: Adversarial Discovery of Error-prone groups for Robust Optimization

1 code implementation2 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.


CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation

1 code implementation10 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.

counterfactual Data Augmentation +6

Retrieval-guided Counterfactual Generation for QA

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.

counterfactual Data Augmentation +6

Prompting Contrastive Explanations for Commonsense Reasoning Tasks

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.

EASE: Extractive-Abstractive Summarization with Explanations

no code implementations14 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.

Abstractive Text Summarization Document Summarization +1

FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation

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.

Fact Verification Question Answering

An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction

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.

Contextualized Representations for Low-resource Utterance Tagging

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.

Emotion Classification

Entity Projection via Machine Translation for Cross-Lingual NER

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.

Cross-Lingual NER Machine Translation +4

Weighted Global Normalization for Multiple Choice Reading Comprehension over Long Documents

no code implementations5 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.

Answer Selection Multiple-choice +1

SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations

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.

Clustering Information Retrieval +3

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