Search Results for author: Yacine Jernite

Found 21 papers, 9 papers with code

Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards

no code implementations16 Aug 2021 Angelina McMillan-Major, Salomey Osei, Juan Diego Rodriguez, Pawan Sasanka Ammanamanchi, Sebastian Gehrmann, Yacine Jernite

Developing documentation guidelines and easy-to-use templates for datasets and models is a challenging task, especially given the variety of backgrounds, skills, and incentives of the people involved in the building of natural language processing (NLP) tools.

Text Generation

Improving Conditioning in Context-Aware Sequence to Sequence Models

no code implementations21 Nov 2019 Xinyi Wang, Jason Weston, Michael Auli, Yacine Jernite

Neural sequence to sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence.

Data Augmentation Document-level +1

Unsupervised Text Summarization via Mixed Model Back-Translation

no code implementations22 Aug 2019 Yacine Jernite

Back-translation based approaches have recently lead to significant progress in unsupervised sequence-to-sequence tasks such as machine translation or style transfer.

Machine Translation Style Transfer +2

ELI5: Long Form Question Answering

1 code implementation ACL 2019 Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, Michael Auli

We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to open-ended questions.

Language Modelling Question Answering

Why Build an Assistant in Minecraft?

1 code implementation22 Jul 2019 Arthur Szlam, Jonathan Gray, Kavya Srinet, Yacine Jernite, Armand Joulin, Gabriel Synnaeve, Douwe Kiela, Haonan Yu, Zhuoyuan Chen, Siddharth Goyal, Demi Guo, Danielle Rothermel, C. Lawrence Zitnick, Jason Weston

In this document we describe a rationale for a research program aimed at building an open "assistant" in the game Minecraft, in order to make progress on the problems of natural language understanding and learning from dialogue.

Minecraft Natural Language Understanding

CraftAssist: A Framework for Dialogue-enabled Interactive Agents

3 code implementations19 Jul 2019 Jonathan Gray, Kavya Srinet, Yacine Jernite, Haonan Yu, Zhuoyuan Chen, Demi Guo, Siddharth Goyal, C. Lawrence Zitnick, Arthur Szlam

This paper describes an implementation of a bot assistant in Minecraft, and the tools and platform allowing players to interact with the bot and to record those interactions.


CraftAssist Instruction Parsing: Semantic Parsing for a Minecraft Assistant

no code implementations17 Apr 2019 Yacine Jernite, Kavya Srinet, Jonathan Gray, Arthur Szlam

We propose a large scale semantic parsing dataset focused on instruction-driven communication with an agent in Minecraft.

Minecraft Semantic Parsing

Grounded Recurrent Neural Networks

no code implementations23 May 2017 Ankit Vani, Yacine Jernite, David Sontag

In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process "grounding").

Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning

no code implementations23 Apr 2017 Yacine Jernite, Samuel R. Bowman, David Sontag

This work presents a novel objective function for the unsupervised training of neural network sentence encoders.

Representation Learning

Variable Computation in Recurrent Neural Networks

no code implementations18 Nov 2016 Yacine Jernite, Edouard Grave, Armand Joulin, Tomas Mikolov

Recurrent neural networks (RNNs) have been used extensively and with increasing success to model various types of sequential data.

Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation

no code implementations ICML 2017 Yacine Jernite, Anna Choromanska, David Sontag

We consider multi-class classification where the predictor has a hierarchical structure that allows for a very large number of labels both at train and test time.

Classification Density Estimation +5

Character-Aware Neural Language Models

15 code implementations26 Aug 2015 Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush

We describe a simple neural language model that relies only on character-level inputs.

Language Modelling

Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests

no code implementations NeurIPS 2013 Yacine Jernite, Yonatan Halpern, David Sontag

We show that the existence of such a quartet allows us to uniquely identify each latent variable and to learn all parameters involving that latent variable.

Cannot find the paper you are looking for? You can Submit a new open access paper.