Human-level play in the game of Diplomacy by combining language models with strategic reasoning

1 code implementation • Science 2022 • Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyan Hu, Athul Paul Jacob, Mojtaba Komeili, Karthik Konath, Minae Kwon, Adam Lerer, Mike Lewis, Alexander H. Miller, Sash Mitts, Aditya Renduchintala, Stephen Roller, Dirk Rowe, Weiyan Shi, Joe Spisak, Alexander Wei, David Wu, Hugh Zhang, Markus Zijlstra

Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.

The NetHack Learning Environment

3 code implementations • NeurIPS 2020 • Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel

Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminal-based roguelike game, NetHack.

NetHack Score Reinforcement Learning (RL) +1

How Context Affects Language Models' Factual Predictions

no code implementations • AKBC 2020 • Fabio Petroni, Patrick Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H. Miller, Sebastian Riedel

When pre-trained on large unsupervised textual corpora, language models are able to store and retrieve factual knowledge to some extent, making it possible to use them directly for zero-shot cloze-style question answering.

Information Retrieval Language Modelling +4

MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions

1 code implementation • 9 Oct 2019 • Viswanath Sivakumar, Olivier Delalleau, Tim Rocktäschel, Alexander H. Miller, Heinrich Küttler, Nantas Nardelli, Mike Rabbat, Joelle Pineau, Sebastian Riedel

This is largely an artifact of building on top of frameworks designed for RL in games (e. g. OpenAI Gym).

Network Congestion Control OpenAI Gym

Language Models as Knowledge Bases?

1 code implementation • IJCNLP 2019 • Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H. Miller, Sebastian Riedel

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks.

Language Modelling Open-Domain Question Answering

Importance of Search and Evaluation Strategies in Neural Dialogue Modeling

1 code implementation • WS 2019 • Ilia Kulikov, Alexander H. Miller, Kyunghyun Cho, Jason Weston

We investigate the impact of search strategies in neural dialogue modeling.

Dialogue Generation Open-Ended Question Answering

Retrieve and Refine: Improved Sequence Generation Models For Dialogue

1 code implementation • WS 2018 • Jason Weston, Emily Dinan, Alexander H. Miller

Sequence generation models for dialogue are known to have several problems: they tend to produce short, generic sentences that are uninformative and unengaging.

Retrieval

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

ParlAI: A Dialog Research Software Platform

22 code implementations • EMNLP 2017 • Alexander H. Miller, Will Feng, Adam Fisch, Jiasen Lu, Dhruv Batra, Antoine Bordes, Devi Parikh, Jason Weston

We introduce ParlAI (pronounced "par-lay"), an open-source software platform for dialog research implemented in Python, available at http://parl. ai.

reinforcement-learning Reinforcement Learning (RL) +1

Learning through Dialogue Interactions by Asking Questions

2 code implementations • 15 Dec 2016 • Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston

A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction.

reinforcement-learning Reinforcement Learning (RL)

Dialogue Learning With Human-In-The-Loop

2 code implementations • 29 Nov 2016 • Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston

An important aspect of developing conversational agents is to give a bot the ability to improve through communicating with humans and to learn from the mistakes that it makes.

Question Answering reinforcement-learning +1