193 citations · 286 across the 15 of their papers we have counts for
17 papers
Improving Text-to-Image Consistency via Automatic Prompt Optimization
Oscar Mañas, Pietro Astolfi, Melissa Hall +6
Impressive advances in text-to-image (T2I) generative models have yielded a plethora of high performing models which are able to generate aesthetically appealing, photorealistic im…
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Jack Urbanek, Florian Bordes, Pietro Astolfi +3
Curation methods for massive vision-language datasets trade off between dataset size and quality. However, even the highest quality of available curated captions are far too short…
Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models
Jimmy Wei, Kurt Shuster, Arthur Szlam +3
Current dialogue research primarily studies pairwise (two-party) conversations, and does not address the everyday setting where more than two speakers converse together. In this wo…
Infusing Commonsense World Models with Graph Knowledge
Alexander Gurung, Mojtaba Komeili, Arthur Szlam +2
While language models have become more capable of producing compelling language, we find there are still gaps in maintaining consistency, especially when describing events in a dyn…
Mephisto: A Framework for Portable, Reproducible, and Iterative Crowdsourcing
Jack Urbanek, Pratik Ringshia
We introduce Mephisto, a framework to make crowdsourcing for research more reproducible, transparent, and collaborative. Mephisto provides abstractions that cover a broad set of ta…
Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity
Kurt Shuster, Jack Urbanek, Arthur Szlam +1
State-of-the-art dialogue models still often stumble with regards to factual accuracy and self-contradiction. Anecdotally, they have been observed to fail to maintain character ide…
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue
Leonard Adolphs, Kurt Shuster, Jack Urbanek +2
Large language models can produce fluent dialogue but often hallucinate factual inaccuracies. While retrieval-augmented models help alleviate this issue, they still face a difficul…
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
Prithviraj Ammanabrolu, Jack Urbanek, Margaret Li +3
We seek to create agents that both act and communicate with other agents in pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019) -- a large-scale crowd-source…
Deploying Lifelong Open-Domain Dialogue Learning
Kurt Shuster, Jack Urbanek, Emily Dinan +2
Much of NLP research has focused on crowdsourced static datasets and the supervised learning paradigm of training once and then evaluating test performance. As argued in de Vries e…
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
Stephen Roller, Y-Lan Boureau, Jason Weston +13
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 bui…
I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents
Shrimai Prabhumoye, Margaret Li, Jack Urbanek +4
Dialogue research tends to distinguish between chit-chat and goal-oriented tasks. While the former is arguably more naturalistic and has a wider use of language, the latter has cle…
Generating Interactive Worlds with Text
Angela Fan, Jack Urbanek, Pratik Ringshia +8
Procedurally generating cohesive and interesting game environments is challenging and time-consuming. In order for the relationships between the game elements to be natural, common…
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Emily Dinan, Angela Fan, Adina Williams +3
Models often easily learn biases present in the training data, and their predictions directly reflect this bias. We analyze gender bias in dialogue data, and examine how this bias…
Learning to Speak and Act in a Fantasy Text Adventure Game
Jack Urbanek, Angela Fan, Siddharth Karamcheti +7
We introduce a large scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dial…
The Second Conversational Intelligence Challenge (ConvAI2)
Emily Dinan, Varvara Logacheva, Valentin Malykh +14
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots. Some key takeaways from the competition ar…
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Saizheng Zhang, Emily Dinan, Jack Urbanek +3
Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the…
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
Zhilin Yang, Saizheng Zhang, Jack Urbanek +5
Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment. In this work we propose…