Internal and external pressures on language emergence: least effort, object constancy and frequency (2020)

Abstract: In previous work, artificial agents were shown to achieve almost perfect accuracy in referential games where they have to communicate to identify images. Nevertheless, the resulting communication protocols rarely display salient features of natural languages, such as compositionality. In this paper, we propose some realistic sources of pressure on communication that avert this outcome. More specifically, we formalise the principle of least effort through an auxiliary objective. Moreover, we explore several game variants, inspired by the principle of object constancy, in which we alter the frequency, position, and luminosity of the objects in the images. We perform an extensive analysis on their effect through compositionality metrics, diagnostic classifiers, and zero-shot evaluation. Our findings reveal that the proposed sources of pressure result in emerging languages with less redundancy, more focus on high-level conceptual information, and better abilities of generalisation. Overall, our contributions reduce the gap between emergent and natural languages.

## introduction one of the key requirements for a machine to be intelligent is its ability to communicate in natural language @xcite . while supervised approaches with labelled texts have recently achieved unprecedented performances in several applications @xcite , they still neglect fundamental components of natural communication, such as the speakers' intention and the function of their utterances @xcite . this functional aspect of language instead is captured by multi-agent games @xcite , in which agents have to communicate about some shared input space (e.g. images). agents usually * shared senior authorship manage to communicate with success, measured in terms of task accuracy @xcite , if the setting is fully cooperative @xcite . however, several studies have shown that the emerged languages rarely display features inherent to natural languages, such as compositionality of meaning foot_0 and generalisation to novel objects (e.g. @xcite . for instance, agents might develop protocols to refer to specific pixel values, rather than concept-level information @xcite . referential games are a perfect controlled environment to study how sources of pressure on the agents affect the 'naturalness' of emergent languages. previous work has proposed to limit the memory of neural agents across turns of dialogue @xcite or to soft-constrain the active vocabulary size @xcite . however, these constraints seem at odds with the capacity of human memory. in this work, we propose a set of yet unexplored but more realistic sources of pressure, either internal to the agents or external, pertaining to the input space. an internal source of pressure, inspired by the principle of least effort @xcite , see § 2.1), compels the agents to keep the length of sentences to the bare minimum. we implement this pressure through an auxiliary loss that incentivises the generation of the end-of-sentence token as early as possible. several external pressures instead are implemented as game variants, where we control for the frequency, the position, and the illumination of objects in images. these game variants are again motivated by principles governing human perception, such as object constancy @xcite , see § 2.2). our results demonstrate that the internal pressure efficiently compresses the sentence lengths and the vocabulary size without loss of accuracy. moreover, based on established metrics of compositionality @xcite @xcite and zeroshot evaluation, we show that agents with pressure towards object constancy achieve the highest scores. finally, diagnostic classification reveals how the external pressures make agents sensitive to higher-level object properties. in general, we offer a series of contributions. in addition to a novel model objective and game variants, we establish a methodology to adapt the communication hyper-parameters automatically. moreover, we draw connections to principles of human cognition, thus aligning the multi-agent game to hypotheses on natural language evolution @xcite . we elaborate on such principles in § 2. in § 3, we outline the basic setup for the referential game and the dataset. the auxiliary loss and game variants that operationalise the cognitive principles are described in § 4. we discuss the metrics for evaluation in § 5 and provide the results in § 6. the main conclusions to our work are summarised in § 7. ## motivation the proposed sources of pressure on emergent languages are motivated on the basis of general principles of human communication and perception. in this section, we outline the principles of least effort, object constancy, and object frequency. ## setup we study language emergence in the context of task-oriented multi-agent games. in the current section, we present our baseline setup ( § 3.1) and the dataset that we use ( § 3.2). ## formalisation of pressures as the core contribution of this paper, we propose a series of changes to the baseline setup in order to incorporate model internal and external pressures related to concept learning. in the current section, we describe these alternative setups. ## experiments we now describe the procedures that we use for training and evaluation, and provide details on the (hyper)parameter settings. ## results in this section, we provide the experimental results and discuss them critically. in § 6.1, we establish the effect of the vocabulary penalty on the sequence lengths and vocabulary sizes in the emerging languages. in § 6.2, we compare the impact of different external pressures on the model's ability to generalise in zero-shot evaluation. in § 6.3, we analyse the protocols evolved in each game variant in the light of the metrics described in § 5.3. finally, we investigate which image features can be decoded from the emitted messages in § 6.4. ## conclusions while most artificial agents achieve communication success in referential games, the emerging protocols are far from natural. therefore, we coax the agent languages into developing desirable properties through sources of pressure that are both effective and realistic in terms of human cognition. in particular, we encourage the agents to make the least effort (in terms of sentence length and active vocabulary) through an auxiliary loss. moreover, inspired by principles of perceptual constancy and frequency, we introduce external pressure by manipulating the appearance and frequency distribution of objects within images. firstly, we found that least effort reduces message redundancy without loss of communication accuracy. secondly, according to a series of well established metrics, external pressures facilitate the emergence of communicative protocols with a higher degree of compositionality. thirdly, some sources of pressure such as colour constancy increase the accuracy in zero-shot communication, hence leading to a better ability to generalise. finally, we reveal through diagnostic classifiers that agents under external pressures retain high-level information (such as shape or color of objects) rather than local pixel features. in general, the sources of pressure we propose bring forth a series of advantages: 1) they encourage more natural communication protocols; 2) they mitigate the arbitrariness of hyper-parameter setting; 3) they are realistic and justified by general principles of human cognition. in the future, this could help shedding light on the evolution of natural languages.