Ankur P. Parikh

Simple Recurrence Improves Masked Language Models

2022 · arXiv (Cornell University)

In this work, we explore whether modeling recurrence into the Transformer architecture can both be beneficial and efficient, by building an extremely simple recurrent module into the Transformer. We compare our model to baselines following …

A Decomposable Attention Model for Natural Language Inference

2016 · arXiv (Cornell University)

We propose a simple neural architecture for natural language inference.Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable.On the Stanford Natural Language Inference (SNLI) …

Learning Recurrent Span Representations for Extractive Question Answering

2016 · arXiv (Cornell University)

The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of …

Natural Questions: A Benchmark for Question Answering Research

2019 · Transactions of the Association for Computational Linguistics

We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia …

Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index

2019

Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query, which is computationally prohibitive. In this paper, we introduce query-agnostic …

BLEURT: Learning Robust Metrics for Text Generation

2020

Text generation has made significant advances in the last few years. Yet, evaluation metrics have lagged behind, as the most popular choices (e.g., BLEU and ROUGE) may correlate poorly with human judgments. We propose BLEURT, …

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

2021

Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Anuoluwapo Aremu, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna-Adriana Clinciu, Dipanjan Das, Kaustubh Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Chinenye Emezue, Varun Gangal, Cristina Garbacea, …