Compositionality has traditionally been understood as a major factor in productivity of language and, more broadly, human cognition. Yet, recently, some research started to question its status, showing that artificial neural networks are good at generalization even without noticeable compositional behavior. We argue that some of these conclusions are too strong and/or incomplete. In the context of a two-agent communication game, we show that compositionality indeed seems essential for successful generalization when the evaluation is done on a proper dataset.

翻译：语言的构成传统上被认为是语言生产力和更广泛的人类认知的一个主要因素。 然而,最近,一些研究开始质疑语言的状态,表明人工神经网络即使没有明显的构成行为,也擅长一般化。 我们争论说,其中一些结论太强和(或)不完整。 在双剂交流游戏中,我们表明,在对正确的数据集进行评估时,合成对于成功普遍化似乎确实至关重要。