b'Arthur Szlam'

research

∙ 09/14/2023

A Data Source for Reasoning Embodied Agents

Recent progress in using machine learning models for reasoning tasks has...

0 Jack Lanchantin, et al. ∙

research

∙ 05/18/2023

Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructions

Human intelligence's adaptability is remarkable, allowing us to adjust t...

0 Shrestha Mohanty, et al. ∙

research

∙ 05/01/2023

Learning to Reason and Memorize with Self-Notes

Large language models have been shown to struggle with limited context m...

4 Jack Lanchantin, et al. ∙

research

∙ 04/26/2023

Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models

Current dialogue research primarily studies pairwise (two-party) convers...

0 Jimmy Wei, et al. ∙

research

∙ 01/13/2023

Infusing Commonsense World Models with Graph Knowledge

While language models have become more capable of producing compelling l...

0 Alexander Gurung, et al. ∙

research

∙ 11/12/2022

Collecting Interactive Multi-modal Datasets for Grounded Language Understanding

Human intelligence can remarkably adapt quickly to new tasks and environ...

0 Shrestha Mohanty, et al. ∙

research

∙ 10/11/2022

CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory

We propose CLIP-Fields, an implicit scene model that can be trained with...

0 Nur Muhammad Mahi Shafiullah, et al. ∙

research

∙ 08/05/2022

BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage

We present BlenderBot 3, a 175B parameter dialogue model capable of open...

16 Kurt Shuster, et al. ∙

research

∙ 05/27/2022

IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022

Human intelligence has the remarkable ability to adapt to new tasks and ...

0 Julia Kiseleva, et al. ∙

research

∙ 05/05/2022

Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021

Human intelligence has the remarkable ability to quickly adapt to new ta...

0 Julia Kiseleva, et al. ∙

research

∙ 04/19/2022

Many Episode Learning in a Modular Embodied Agent via End-to-End Interaction

In this work we give a case study of an embodied machine-learning (ML) p...

0 Yuxuan Sun, et al. ∙

research

∙ 03/24/2022

Language Models that Seek for Knowledge: Modular Search Generation for Dialogue and Prompt Completion

Language models (LMs) have recently been shown to generate more factual ...

0 Kurt Shuster, et al. ∙

research

∙ 03/11/2022

Can I see an Example? Active Learning the Long Tail of Attributes and Relations

There has been significant progress in creating machine learning models ...

0 Tyler L. Hayes, et al. ∙

research

∙ 12/10/2021

Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity

State-of-the-art dialogue models still often stumble with regards to fac...

0 Kurt Shuster, et al. ∙

research

∙ 11/09/2021

Reason first, then respond: Modular Generation for Knowledge-infused Dialogue

Large language models can produce fluent dialogue but often hallucinate ...

0 Leonard Adolphs, et al. ∙

research

∙ 10/13/2021

NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment

Human intelligence has the remarkable ability to adapt to new tasks and ...

0 Julia Kiseleva, et al. ∙

research

∙ 07/15/2021

Beyond Goldfish Memory: Long-Term Open-Domain Conversation

Despite recent improvements in open-domain dialogue models, state of the...

13 Jing Xu, et al. ∙

research

∙ 06/08/2021

Hash Layers For Large Sparse Models

We investigate the training of sparse layers that use different paramete...

0 Stephen Roller, et al. ∙

research

∙ 05/13/2021

Not All Memories are Created Equal: Learning to Forget by Expiring

Attention mechanisms have shown promising results in sequence modeling t...

0 Sainbayar Sukhbaatar, et al. ∙

research

∙ 12/30/2020

Linguistic calibration through metacognition: aligning dialogue agent responses with expected correctness

Open-domain dialogue agents have vastly improved, but still confidently ...

0 Sabrina J. Mielke, et al. ∙

research

∙ 12/17/2020

Few-shot Sequence Learning with Transformers

Few-shot algorithms aim at learning new tasks provided only a handful of...

9 Lajanugen Logeswaran, et al. ∙

research

∙ 10/06/2020

CURI: A Benchmark for Productive Concept Learning Under Uncertainty

Humans can learn and reason under substantial uncertainty in a space of ...

4 Ramakrishna Vedantam, et al. ∙

research

∙ 10/01/2020

How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds

We seek to create agents that both act and communicate with other agents...

21 Prithviraj Ammanabrolu, et al. ∙

research

∙ 08/18/2020

Deploying Lifelong Open-Domain Dialogue Learning

Much of NLP research has focused on crowdsourced static datasets and the...

0 Kurt Shuster, et al. ∙

research

∙ 07/06/2020

Fast Adaptation via Policy-Dynamics Value Functions

Standard RL algorithms assume fixed environment dynamics and require a s...

12 Roberta Raileanu, et al. ∙

research

∙ 06/22/2020

Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions

We present our view of what is necessary to build an engaging open-domai...

19 Stephen Roller, et al. ∙

research

∙ 04/22/2020

Residual Energy-Based Models for Text Generation

Text generation is ubiquitous in many NLP tasks, from summarization, to ...

0 Yuntian Deng, et al. ∙

research

∙ 04/10/2020

Learning to Visually Navigate in Photorealistic Environments Without any Supervision

Learning to navigate in a realistic setting where an agent must rely sol...

3 Lina Mezghani, et al. ∙

research

∙ 02/07/2020

I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents

Dialogue research tends to distinguish between chit-chat and goal-orient...

8 Shrimai Prabhumoye, et al. ∙

research

∙ 02/07/2020

I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-orientated dialogue agents

Dialogue research tends to distinguish between chit-chat and goal-orient...

6 Shrimai Prabhumoye, et al. ∙

research

∙ 11/20/2019

Generating Interactive Worlds with Text

Procedurally generating cohesive and interesting game environments is ch...

29 Angela Fan, et al. ∙

research

∙ 07/22/2019

Why Build an Assistant in Minecraft?

In this document we describe a rationale for a research program aimed at...

2 Arthur Szlam, et al. ∙

research

∙ 07/19/2019

CraftAssist: A Framework for Dialogue-enabled Interactive Agents

This paper describes an implementation of a bot assistant in Minecraft, ...

0 Jonathan Gray, et al. ∙

research

∙ 06/07/2019

Real or Fake? Learning to Discriminate Machine from Human Generated Text

Recent advances in generative modeling of text have demonstrated remarka...

0 Anton Bakhtin, et al. ∙

research

∙ 04/17/2019

CraftAssist Instruction Parsing: Semantic Parsing for a Minecraft Assistant

We propose a large scale semantic parsing dataset focused on instruction...

0 Yacine Jernite, et al. ∙

research

∙ 03/07/2019

Learning to Speak and Act in a Fantasy Text Adventure Game

We introduce a large scale crowdsourced text adventure game as a researc...

0 Jack Urbanek, et al. ∙

research

∙ 01/31/2019

The Second Conversational Intelligence Challenge (ConvAI2)

We describe the setting and results of the ConvAI2 NeurIPS competition t...

2 Emily Dinan, et al. ∙

research

∙ 11/22/2018

Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

In hierarchical reinforcement learning a major challenge is determining ...

0 Sainbayar Sukhbaatar, et al. ∙

research

∙ 11/01/2018

Dialogue Natural Language Inference

Consistency is a long standing issue faced by dialogue models. In this p...

6 Sean Welleck, et al. ∙

research

∙ 09/06/2018

Planning with Arithmetic and Geometric Attributes

A desirable property of an intelligent agent is its ability to understan...

10 David Folqué, et al. ∙

research

∙ 04/20/2018

Lightweight Adaptive Mixture of Neural and N-gram Language Models

It is often the case that the best performing language model is an ensem...

0 Anton Bakhtin, et al. ∙

research

∙ 03/01/2018

Composable Planning with Attributes

The tasks that an agent will need to solve often are not known during tr...

0 Amy Zhang, et al. ∙

research

∙ 02/26/2018

Modeling Others using Oneself in Multi-Agent Reinforcement Learning

We consider the multi-agent reinforcement learning setting with imperfec...

0 Roberta Raileanu, et al. ∙

research

∙ 01/22/2018

Personalizing Dialogue Agents: I have a dog, do you have pets too?

Chit-chat models are known to have several problems: they lack specifici...

0 Saizheng Zhang, et al. ∙

research

∙ 11/21/2017

Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent

Contrary to most natural language processing research, which makes use o...

0 Zhilin Yang, et al. ∙

research

∙ 07/18/2017

Optimizing the Latent Space of Generative Networks

Generative Adversarial Networks (GANs) have been shown to be able to sam...

0 Piotr Bojanowski, et al. ∙

research

∙ 06/07/2017

Low-shot learning with large-scale diffusion

This paper considers the problem of inferring image labels for which onl...

0 Matthijs Douze, et al. ∙

research

∙ 04/20/2017

Hard Mixtures of Experts for Large Scale Weakly Supervised Vision

Training convolutional networks (CNN's) that fit on a single GPU with mi...

0 Sam Gross, et al. ∙

research

∙ 02/15/2017

Training Language Models Using Target-Propagation

While Truncated Back-Propagation through Time (BPTT) is the most popular...

0 Sam Wiseman, et al. ∙

research

∙ 02/08/2017

Automatic Rule Extraction from Long Short Term Memory Networks

Although deep learning models have proven effective at solving problems ...

0 W. James Murdoch, et al. ∙

Arthur Szlam

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