Search Results for author: Jason Naradowsky

Found 21 papers, 8 papers with code

Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation

1 code implementation24 Oct 2023 Qiang Zhang, Jason Naradowsky, Yusuke Miyao

Knowing how to end and resume conversations over time is a natural part of communication, allowing for discussions to span weeks, months, or years.

Dialogue Generation

Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models

1 code implementation29 May 2023 Qiang Zhang, Jason Naradowsky, Yusuke Miyao

We propose the "Ask an Expert" framework in which the model is trained with access to an "expert" which it can consult at each turn.

Emergent Communication with Attention

no code implementations18 May 2023 Ryokan Ri, Ryo Ueda, Jason Naradowsky

To develop computational agents that better communicate using their own emergent language, we endow the agents with an ability to focus their attention on particular concepts in the environment.

Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem

1 code implementation Findings (ACL) 2022 Qiang Zhang, Jason Naradowsky, Yusuke Miyao

We introduce the task of implicit offensive text detection in dialogues, where a statement may have either an offensive or non-offensive interpretation, depending on the listener and context.

Text Detection

Pow-Wow: A Dataset and Study on Collaborative Communication in Pommerman

no code implementations ICML Workshop LaReL 2020 Takuma Yoneda, Matthew R. Walter, Jason Naradowsky

In this work we perform a controlled study of human language use in a competitive team-based game, and search for useful lessons for structuring communication protocol between autonomous agents.

Emergent Communication with World Models

no code implementations22 Feb 2020 Alexander I. Cowen-Rivers, Jason Naradowsky

This provides a visual grounding of the message, similar to an enhanced observation of the world, which may include objects outside of the listening agent's field-of-view.

Visual Grounding

Meta-learning Extractors for Music Source Separation

1 code implementation17 Feb 2020 David Samuel, Aditya Ganeshan, Jason Naradowsky

We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models.

Meta-Learning Music Source Separation

Represent, Aggregate, and Constrain: A Novel Architecture for Machine Reading from Noisy Sources

no code implementations30 Oct 2016 Jason Naradowsky, Sebastian Riedel

In order to extract event information from text, a machine reading model must learn to accurately read and interpret the ways in which that information is expressed.

Reading Comprehension

Programming with a Differentiable Forth Interpreter

1 code implementation ICML 2017 Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel

Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model.

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