We present a recurrent neural network (RNN) that performs thematic role assignment and can be used for human–robot interaction (HRI). The RNN is trained to map sentence structures to meanings (e.g., predicates). Previously, we have shown that the model is able to generalize on English and French corpora. In this article, we investigate its ability to adapt to various languages originating from Asia or Europe. We show that it can successfully learn to parse sentences related to home scenarios in 15 languages, namely English, German, French, Spanish, Catalan, Basque, Portuguese, Italian, Bulgarian, Turkish, Persian, Hindi, Marathi, Malay, and Mandarin Chinese. Moreover, in the corpora, we have deliberately included variable complex sentences in order to explore the flexibility of the predicate-like output representations. This demonstrates that: 1) the learning principle of our model is not limited to a particular language (or particular sentence structures), but more generic in nature and 2) it can deal with various kind of representations (not only predicates), which enables users to adapt it to their own needs. As the model is inspired from neuroscience and language acquisition theories, this generic and language-independent aspect makes it a good candidate for modeling human sentence processing. Finally, we discuss the potential implementation of the model in a grounded robotic architecture.

中文翻译：

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教你的机器人你的语言！用于模拟人类句子处理的可训练神经解析器：15 种语言的示例

我们提出了一个循环神经网络（RNN），它执行主题角色分配，可用于人机交互（HRI）。RNN 被训练来将句子结构映射到意义（例如，谓词）。之前，我们已经证明该模型能够泛化英语和法语语料库。在本文中，我们调查了它适应源自亚洲或欧洲的各种语言的能力。我们表明它可以成功地学习解析 15 种语言中与家庭场景相关的句子，即英语、德语、法语、西班牙语、加泰罗尼亚语、巴斯克语、葡萄牙语、意大利语、保加利亚语、土耳其语、波斯语、印地语、马拉地语、马来语和普通话. 此外，在语料库中，我们特意包含了可变复杂句子，以探索类谓词输出表示的灵活性。这表明：1）我们模型的学习原则不限于特定的语言（或特定的句子结构），而是在本质上更通用 2）它可以处理各种表示（不仅是谓词），这使用户能够适应它到他们自己的需要。由于该模型的灵感来自神经科学和语言习得理论，这种通用且独立于语言的方面使其成为模拟人类句子处理的理想选择。最后，我们讨论了该模型在接地机器人架构中的潜在实现。由于该模型的灵感来自神经科学和语言习得理论，这种通用且独立于语言的方面使其成为模拟人类句子处理的理想选择。最后，我们讨论了该模型在接地机器人架构中的潜在实现。由于该模型的灵感来自神经科学和语言习得理论，这种通用且独立于语言的方面使其成为模拟人类句子处理的理想选择。最后，我们讨论了该模型在接地机器人架构中的潜在实现。