Search Results for author: Emily Dinan

Found 30 papers, 10 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.

Effective Theory of Transformers at Initialization

no code implementations4 Apr 2023 Emily Dinan, Sho Yaida, Susan Zhang

We perform an effective-theory analysis of forward-backward signal propagation in wide and deep Transformers, i. e., residual neural networks with multi-head self-attention blocks and multilayer perceptron blocks.

Improving Chess Commentaries by Combining Language Models with Symbolic Reasoning Engines

no code implementations15 Dec 2022 Andrew Lee, David Wu, Emily Dinan, Mike Lewis

Despite many recent advancements in language modeling, state-of-the-art language models lack grounding in the real world and struggle with tasks involving complex reasoning.

Language Modelling

When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels

no code implementations28 Oct 2022 Weiyan Shi, Emily Dinan, Kurt Shuster, Jason Weston, Jing Xu

Deployed dialogue agents have the potential to integrate human feedback to continuously improve themselves.


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

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

Multi-Dimensional Gender Bias Classification

no code implementations EMNLP 2020 Emily Dinan, Angela Fan, Ledell Wu, Jason Weston, Douwe Kiela, Adina Williams

We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.

Classification General Classification

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

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.

Adversarial NLI: A New Benchmark for Natural Language Understanding

2 code implementations ACL 2020 Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, Douwe Kiela

We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure.

Natural Language Understanding

Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack

no code implementations IJCNLP 2019 Emily Dinan, Samuel Humeau, Bharath Chintagunta, Jason Weston

The detection of offensive language in the context of a dialogue has become an increasingly important application of natural language processing.


Neural Text Generation with Unlikelihood Training

5 code implementations ICLR 2020 Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, Jason Weston

Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core.

Blocking Text Generation

Learning to Speak and Act in a Fantasy Text Adventure Game

1 code implementation 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.


Wizard of Wikipedia: Knowledge-Powered Conversational agents

2 code implementations ICLR 2019 Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, Jason Weston

In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date.

Dialogue Generation

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.


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

15 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.

Ranked #5 on Dialogue Generation on Persona-Chat (using extra training data)

Conversational Response Selection Dialogue Generation +1

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