Emergent Language in a Multi-Modal, Multi-Step Referential Game

By Katrina Evtimova, Andrew Drozdov, Douwe Kiela, Kyunghyun Cho. 2017

31 citations recorded by the source corpus.

Inspired by previous work on emergent language in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration. The multi-modal multi-step setting allows agents to develop an internal language significantly closer to natural language, in that they share a single set of messages, and that the length of the conversation may vary according to the difficulty of the task. We examine these properties empirically using a dataset consisting of images and textual descriptions of mammals, where the agents are tasked with identifying the correct object. Our experiments indicate that a robust and efficient communication protocol emerges, where gradual information exchange informs better predictions and higher communication bandwidth improves generalization.

Subfields: Artificial Intelligence, Cultural Studies, Sociology and Political Science

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Citations in the corpus

Challenges for Transparency (2017, background): “Exciting work has begun to explore this direction, looking for ways to enable multiple agents to cooperate effectively (Evtimova et al., 2017; Mordatch and Abbeel, 2017).”
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning (2017, background): “The more recent resurgence has focused on deep RL [1, 8, 10, 13, 18, 19, 23, 26].”
Emergent Communication through Negotiation (2018, background): “…has mainly studied the emergence of communication in cooperative games like referential games, variants of the Lewis signaling game (Lewis, 1969), where messages are used to disambiguate between different possible referents (Goldman et al., 2007; Lazaridou et al., 2016; Evtimova et al., 2017).”
Transparency: Motivations and Challenges (2017, background): “ve that a fruitful line of work will be to help machines understand each other. Exciting work has begun to explore this direction, looking for ways to enable multiple agents to cooperate effectively (Evtimova et al., 2017; Mordatch and Abbeel, 2017). Different paradigms of multi-agent organization s”
Prosocial learning agents solve generalized Stag Hunts better than selfish ones (2017, background): “…can interact with others (Silver et al. 2016; Lazaridou, Peysakhovich, and Baroni 2017; Das et al. 2017; Lowe et al. 2017; Foer-ster et al. 2017; Evtimova et al. 2017; Foerster et al. 2016; Havrylov and Titov 2017; Lewis et al. 2017; Heinrich and Silver 2016; Peng et al. 2017; Leibo et al.…”
Emergent Translation in Multi-Agent Communication (2017, background): “, 2016), adaptive communication bandwidth (Havrylov & Titov, 2017) and multi-turn communication with a variable-length conversation (Evtimova et al., 2017), and simple compositionality (Kottur et al.”
Pommerman: A Multi-Agent Playground (2018, background): “The latter is a recently burgeoning subﬁeld of multi-agent learning (Foerster et al. 2016; Resnick et al. 2018d; Evtimova et al. 2017; Foerster et al. 2017; Lewis et al. 2017; Mordatch and Abbeel 2017; Lazaridou et al. 2018) with established prior work as well (Steels 1999; Steels 2003; Levy and…”
Maintaining cooperation in complex social dilemmas using deep reinforcement learning (2017, background): “, 2009) as well as the growing RL-based literature on language emergence in games (Lazaridou et al., 2017; Das et al., 2017; Evtimova et al., 2017; Havrylov & Titov, 2017; Jorge et al., 2016).”
Consequentialist conditional cooperation in social dilemmas with imperfect information (2017, background): “, 2017; Peysakhovich & Lerer, 2017), and the emergence of language (Lazaridou et al., 2017; Das et al., 2017; Evtimova et al., 2017; Havrylov & Titov, 2017; Jorge et al., 2016).”
Talk the Walk: Navigating New York City through Grounded Dialogue (2018, background): “…communication, where, agents communicate (in natural language or otherwise) in order to solve a task, with respect to their shared environment (Lazaridou et al., 2016; Das et al., 2017b; Mordatch & Abbeel, 2017; Evtimova et al., 2017; Lewis et al., 2017; Strub et al., 2017; Kottur et al., 2017).”