Search Results for author: Y-Lan Boureau

Found 34 papers, 11 papers with code

Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design

no code implementations SIGDIAL (ACL) 2022 A. Stevie Bergman, Gavin Abercrombie, Shannon Spruit, Dirk Hovy, Emily Dinan, Y-Lan Boureau, Verena Rieser

Over the last several years, end-to-end neural conversational agents have vastly improved their ability to carry unrestricted, open-domain conversations with humans.

Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts

no code implementations6 Jul 2023 Mounica Maddela, Megan Ung, Jing Xu, Andrea Madotto, Heather Foran, Y-Lan Boureau

Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack truly widespread adoption in self-help format.

Improving Open Language Models by Learning from Organic Interactions

no code implementations7 Jun 2023 Jing Xu, Da Ju, Joshua Lane, Mojtaba Komeili, Eric Michael Smith, Megan Ung, Morteza Behrooz, William Ngan, Rashel Moritz, Sainbayar Sukhbaatar, Y-Lan Boureau, Jason Weston, Kurt Shuster

We present BlenderBot 3x, an update on the conversational model BlenderBot 3, which is now trained using organic conversation and feedback data from participating users of the system in order to improve both its skills and safety.

Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring the trolls

no code implementations5 Aug 2022 Da Ju, Jing Xu, Y-Lan Boureau, Jason Weston

The promise of interaction between intelligent conversational agents and humans is that models can learn from such feedback in order to improve.

Test

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

1 code implementation5 Aug 2022 Kurt Shuster, Jing Xu, Mojtaba Komeili, Da Ju, Eric Michael Smith, Stephen Roller, Megan Ung, Moya Chen, Kushal Arora, Joshua Lane, Morteza Behrooz, William Ngan, Spencer Poff, Naman Goyal, Arthur Szlam, Y-Lan Boureau, Melanie Kambadur, Jason Weston

We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large number of user defined tasks.

Continual Learning

Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback

no code implementations5 Aug 2022 Jing Xu, Megan Ung, Mojtaba Komeili, Kushal Arora, Y-Lan Boureau, Jason Weston

We then study various algorithms for improving from such feedback, including standard supervised learning, rejection sampling, model-guiding and reward-based learning, in order to make recommendations on which type of feedback and algorithms work best.

Retrieval

Detecting Inspiring Content on Social Media

1 code implementation6 Sep 2021 Oana Ignat, Y-Lan Boureau, Jane A. Yu, Alon Halevy

We release a dataset of 5, 800 inspiring and 5, 800 non-inspiring English-language public post unique ids collected from a dump of Reddit public posts made available by a third party and use linguistic heuristics to automatically detect which social media English-language posts are inspiring.

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling

no code implementations7 Jul 2021 Emily Dinan, Gavin Abercrombie, A. Stevie Bergman, Shannon Spruit, Dirk Hovy, Y-Lan Boureau, Verena Rieser

Over the last several years, end-to-end neural conversational agents have vastly improved in their ability to carry a chit-chat conversation with humans.

Bot-Adversarial Dialogue for Safe Conversational Agents

no code implementations NAACL 2021 Jing Xu, Da Ju, Margaret Li, Y-Lan Boureau, Jason Weston, Emily Dinan

Conversational agents trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior.

Reducing conversational agents' overconfidence through linguistic calibration

no code implementations30 Dec 2020 Sabrina J. Mielke, Arthur Szlam, Emily Dinan, Y-Lan Boureau

While improving neural dialogue agents' factual accuracy is the object of much research, another important aspect of communication, less studied in the setting of neural dialogue, is transparency about ignorance.

Recipes for Safety in Open-domain Chatbots

no code implementations14 Oct 2020 Jing Xu, Da Ju, Margaret Li, Y-Lan Boureau, Jason Weston, Emily Dinan

Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted biases.

Controlling Style in Generated Dialogue

1 code implementation22 Sep 2020 Eric Michael Smith, Diana Gonzalez-Rico, Emily Dinan, Y-Lan Boureau

Open-domain conversation models have become good at generating natural-sounding dialogue, using very large architectures with billions of trainable parameters.

Dialogue Generation

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

Multi-scale Transformer Language Models

no code implementations1 May 2020 Sandeep Subramanian, Ronan Collobert, Marc'Aurelio Ranzato, Y-Lan Boureau

We investigate multi-scale transformer language models that learn representations of text at multiple scales, and present three different architectures that have an inductive bias to handle the hierarchical nature of language.

Inductive Bias Language Modelling

All-in-One Image-Grounded Conversational Agents

no code implementations28 Dec 2019 Da Ju, Kurt Shuster, Y-Lan Boureau, Jason Weston

As single-task accuracy on individual language and image tasks has improved substantially in the last few years, the long-term goal of a generally skilled agent that can both see and talk becomes more feasible to explore.

Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training

1 code implementation ACL 2020 Margaret Li, Stephen Roller, Ilia Kulikov, Sean Welleck, Y-Lan Boureau, Kyunghyun Cho, Jason Weston

Generative dialogue models currently suffer from a number of problems which standard maximum likelihood training does not address.

The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents

no code implementations ACL 2020 Kurt Shuster, Da Ju, Stephen Roller, Emily Dinan, Y-Lan Boureau, Jason Weston

We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, discuss topics and situations, and perceive and converse about images.

Revisiting the Evaluation of Theory of Mind through Question Answering

no code implementations IJCNLP 2019 Matthew Le, Y-Lan Boureau, Maximilian Nickel

Theory of mind, i. e., the ability to reason about intents and beliefs of agents is an important task in artificial intelligence and central to resolving ambiguous references in natural language dialogue.

Question Answering

Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue

1 code implementation IJCNLP 2019 Dongyeop Kang, Anusha Balakrishnan, Pararth Shah, Paul Crook, Y-Lan Boureau, Jason Weston

These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone's preferences, react to their requests, and recommend more appropriate items.

Recommendation Systems

Multiple-Attribute Text Rewriting

no code implementations ICLR 2019 Guillaume Lample, Sandeep Subramanian, Eric Smith, Ludovic Denoyer, Marc'Aurelio Ranzato, Y-Lan Boureau

The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style".

Attribute Disentanglement +2

I Know the Feeling: Learning to Converse with Empathy

no code implementations ICLR 2019 Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau

Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling.

Dialogue Generation

Multiple-Attribute Text Style Transfer

3 code implementations1 Nov 2018 Sandeep Subramanian, Guillaume Lample, Eric Michael Smith, Ludovic Denoyer, Marc'Aurelio Ranzato, Y-Lan Boureau

The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style".

Attribute Disentanglement +3

Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset

9 code implementations ACL 2019 Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau

One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill.

Dialogue Generation

Learning End-to-End Goal-Oriented Dialog

6 code implementations24 May 2016 Antoine Bordes, Y-Lan Boureau, Jason Weston

We show similar result patterns on data extracted from an online concierge service.

dialog state tracking Goal-Oriented Dialog +1

Sparse Feature Learning for Deep Belief Networks

no code implementations NeurIPS 2007 Marc'Aurelio Ranzato, Y-Lan Boureau, Yann L. Cun

Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the raw input.

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