Search Results for author: Bhargavi Paranjape

Found 11 papers, 4 papers with code

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.

Data Augmentation Question Answering +2

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.

Pretrained Language Models

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 +3

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.

Information Retrieval Multi-Label Classification +1

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