David Grangier

Interactive Semantic Featuring for Text Classification

2016 · arXiv (Cornell University)

In text classification, dictionaries can be used to define human-comprehensible features. We propose an improvement to dictionary features called smoothed dictionary features. These features recognize document contexts instead of n-grams. We describe a principled methodology …

Neural Text Generation from Structured Data with Application to the Biography Domain

2016

This paper introduces a neural model for concept-to-text generation that scales to large, rich domains. It generates biographical sentences from fact tables on a new dataset of biographies from Wikipedia. This set is an order …

Minimum Bayes Risk Decoding with Neural Metrics of Translation Quality.

2021 · arXiv (Cornell University)

This work applies Minimum Bayes Risk (MBR) decoding to optimize diverse automated metrics of translation quality. Automatic metrics in machine translation have made tremendous progress recently. In particular, neural metrics, fine-tuned on human ratings (e.g. …

A Convolutional Encoder Model for Neural Machine Translation

2017

The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the …

Language Modeling with Gated Convolutional Networks

2016 · arXiv (Cornell University)

The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a …

Convolutional Sequence to Sequence Learning

2017 · arXiv (Cornell University)

The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. Compared to recurrent …

Understanding Back-Translation at Scale

2018 · arXiv (Cornell University)

An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a number …

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

2019

Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). 2019.

ELI5: Long Form Question Answering

2019

We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to openended questions. The dataset comprises 270K threads from the Reddit forum "Explain Like I'm Five" (ELI5) where …

Controllable Abstractive Summarization

2018

Current models for document summarization disregard user preferences such as the desired length, style, the entities that the user might be interested in, or how much of the document the user has already read. We …

Language modeling with gated convolutional networks

2017 · International Conference on Machine Learning

The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a …