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.
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.
Ranked #1 on NetHack Score on NetHack Learning Environment
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.
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).
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.
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.
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.
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.
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.
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.
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.