Artificial learners often behave differently from human learners in the context of neural agent-based simulations of language emergence and change. The lack of appropriate cognitive biases in these learners is one of the prevailing explanations. However, it has also been proposed that more naturalistic settings of language learning and use could lead to more human-like results. In this work, we investigate the latter account focusing on the word-order/case-marking trade-off, a widely attested language universal which has proven particularly difficult to simulate. We propose a new Neural-agent Language Learning and Communication framework (NeLLCom) where pairs of speaking and listening agents first learn a given miniature language through supervised learning, and then optimize it for communication via reinforcement learning. Following closely the setup of earlier human experiments, we succeed in replicating the trade-off with the new framework without hard-coding any learning bias in the agents. We see this as an essential step towards the investigation of language universals with neural learners.
翻译:人工学习者在以神经剂为基础的语言出现和变化模拟中的行为往往不同于人类学习者。这些学习者缺乏适当的认知偏见是主要的解释之一。然而,也有人提议,语言学习和使用更自然的环境可能导致更人性化的结果。在这项工作中,我们调查后一种以单词顺序/案件标记取舍为主的会计核算,这是一个得到广泛证实的普遍语言,已经证明特别难以模拟。我们提议一个新的神经剂语言学习和交流框架(NELLCom),在这个框架中,一对讲和听的代理人首先通过监督的学习学习学习学习学习某种特定的微型语言,然后通过强化的学习优化其交流。在紧随早期人类实验的设置之后,我们成功地复制了与新框架的权衡,而没有硬地将代理人的任何学习偏差编码。我们认为这是与神经学学习者一起调查通用语言的一个重要步骤。