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 dynamically changing world.
In this whitepaper we discuss the current state of data collection and annotation in ML research, establish the motivation for building a shared framework to enable researchers to create and open-source data collection and annotation tools as part of their publication, and outline a set of suggested requirements for a system to facilitate these goals.
Large language models can produce fluent dialogue but often hallucinate factual inaccuracies.
We seek to create agents that both act and communicate with other agents in pursuit of a goal.
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).
no code implementations • 22 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.
Dialogue research tends to distinguish between chit-chat and goal-oriented tasks.
no code implementations • 20 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.
Models often easily learn biases present in the training data, and their predictions directly reflect this bias.
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.
3 code implementations • 31 Jan 2019 • Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander Miller, Kurt Shuster, Jack Urbanek, Douwe Kiela, Arthur Szlam, Iulian Serban, Ryan Lowe, Shrimai Prabhumoye, Alan W. black, Alexander Rudnicky, Jason Williams, Joelle Pineau, Mikhail Burtsev, Jason Weston
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots.
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 #4 on Dialogue Generation on Persona-Chat
Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment.