Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input (1804.03984v1)

Abstract: The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks. Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neural network agents on referential communication games. We extend previous work, in which agents were trained in symbolic environments, by developing agents which are able to learn from raw pixel data, a more challenging and realistic input representation. We find that the degree of structure found in the input data affects the nature of the emerged protocols, and thereby corroborate the hypothesis that structured compositional language is most likely to emerge when agents perceive the world as being structured.

Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input

This paper explores the emergence of communication protocols, particularly in referential games with both symbolic and raw pixel inputs. The researchers investigate the capabilities of reinforcement learning agents to develop compositional communication protocols in environments that vary in input complexity and structure.

Introduction and Context

The paper of emergent communication has been pivotal in understanding both language evolution and acquisition. The focus here is the influence of environmental and pre-linguistic conditions on the resultant communication protocols. The authors employ referential games as the platform for examining these effects, exploring both symbolic and raw perceptual inputs. This distinction is crucial, as symbolic inputs typically provide disentangled and structured data, while raw pixel inputs are more entangled and closely mirror the sensory inputs humans receive.

Methodology

The researchers' methodology utilizes state-of-the-art reinforcement learning techniques. By employing neural network agents in referential games, they aim to elucidate the conditions under which structured communication protocols emerge. The experiments are systematically divided into two studies. The first paper involves symbolic data, where objects are represented by attribute vectors highly aligned with conventional language learning tasks. The second paper, more challenging by design, involves raw pixel data input, providing a closer approximation to natural perception.

Study 1: Symbolic Data

In the symbolic environment, agents successfully develop communication protocols that exhibit compositional characteristics. The key finding is that the structure inherent in the input data supports the emergence of similar structures in the communication protocol. Performance metrics such as communicative success and topographic similarity indicate that agents can generalize to novel objects, pointing to productive capacities of these learned protocols. The results underscore the suitability of structured representations for fostering compositional language.

Study 2: Pixel Data

The second paper shifts focus to raw pixel inputs. Despite the increased difficulty due to the entanglement of input features, agents again demonstrate emergent communication capabilities. Notably, however, the complexity and subtlety of these input signals reduce the clarity of structured and compositional protocol emergence compared to symbolic input. This indicates a tangible link between input separability and linguistic structure, implicating disentanglement as a core facilitator for the formation of compositional language.

Numerical Results and Bold Claims

The researchers present a series of compelling quantitative results illustrating how training agents with varied input complexity affects the emergence of communication protocols. In structured environments, agents reach a communicative success rate of up to 98.5%, with substantial topographic similarity to the input space. However, with pixel inputs, success rates are maintained around 93-94%, albeit with reduced emergent protocol structure, hinting at the complexity and challenges inherent in disentangled input-driven language emergence.

Implications and Future Developments

The implications of this research are multi-faceted. Practically, these insights could guide the development of more robust communication protocols in AI systems, particularly for environments where perceptual inputs are inherently complex. Theoretically, these findings advance our understanding of the necessary conditions for language structure to arise and persist in natural and artificial systems. Future work may explore more sophisticated simulacra of environmental conditions and incorporate a broader spectrum of semantic inputs to further examine the scalability and limitations of emergent communication protocols in AI.

In conclusion, this work makes a significant contribution to emergent communication research by advancing methods to scale traditional language evolution studies to contemporary AI frameworks, highlighting the influential role of input structure on communication protocol emergence.