Search Results for author: Jack Urbanek

Found 15 papers, 3 papers with code

Infusing Commonsense World Models with Graph Knowledge

no code implementations13 Jan 2023 Alexander Gurung, Mojtaba Komeili, Arthur Szlam, Jason Weston, Jack Urbanek

While language models have become more capable of producing compelling language, we find there are still gaps in maintaining consistency, especially when describing events in a dynamically changing world.

Mephisto: A Framework for Portable, Reproducible, and Iterative Crowdsourcing

1 code implementation12 Jan 2023 Jack Urbanek, Pratik Ringshia

In this whitepaper we discuss the current state of data collection and annotation in ML research, establish the motivation for building a shared framework to enable researchers to create and open-source data collection and annotation tools as part of their publication, and outline a set of suggested requirements for a system to facilitate these goals.

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

no code implementations Findings (NAACL) 2022 Kurt Shuster, Jack Urbanek, Arthur Szlam, Jason Weston

State-of-the-art dialogue models still often stumble with regards to factual accuracy and self-contradiction.

Deploying Lifelong Open-Domain Dialogue Learning

no code implementations18 Aug 2020 Kurt Shuster, Jack Urbanek, Emily Dinan, Arthur Szlam, Jason Weston

As argued in de Vries et al. (2020), crowdsourced data has the issues of lack of naturalness and relevance to real-world use cases, while the static dataset paradigm does not allow for a model to learn from its experiences of using language (Silver et al., 2013).

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

no code implementations22 Jun 2020 Stephen Roller, Y-Lan Boureau, Jason Weston, Antoine Bordes, Emily Dinan, Angela Fan, David Gunning, Da Ju, Margaret Li, Spencer Poff, Pratik Ringshia, Kurt Shuster, Eric Michael Smith, Arthur Szlam, Jack Urbanek, Mary Williamson

We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet.

Continual Learning

Generating Interactive Worlds with Text

no code implementations20 Nov 2019 Angela Fan, Jack Urbanek, Pratik Ringshia, Emily Dinan, Emma Qian, Siddharth Karamcheti, Shrimai Prabhumoye, Douwe Kiela, Tim Rocktaschel, Arthur Szlam, Jason Weston

We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.

BIG-bench Machine Learning Common Sense Reasoning

Learning to Speak and Act in a Fantasy Text Adventure Game

no code implementations IJCNLP 2019 Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston

We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.


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

13 code implementations ACL 2018 Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, Jason Weston

Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating.

Dialogue Generation Specificity

Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent

no code implementations ICLR 2018 Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston

Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment.

Grounded language learning

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