Powerful transformer models based on neural networks such as GPT-4 have enabled huge progress in natural language processing. This paper identifies three challenges for computer programs dealing with metaphors. First, the phenomenon of Twice-Apt-Metaphors shows that metaphorical interpretations do not have to be triggered by syntactical, semantic or pragmatic tensions. The detection of these metaphors seems to involve a sense of aesthetic pleasure or a higher-order theory of mind, both of which are difficult to implement into computer programs. Second, the contexts relative to which metaphors are interpreted are not simply given but must be reconstructed based on pragmatic considerations that can involve presuppositional pretence. If computer programs cannot produce or understand such a form of pretence, they will have problems dealing with certain metaphors. Finally, adequately interpreting and reacting to some metaphors seems to require the ability to have internal, first-personal experiential and affective states. Since it is questionable whether computer programs have such mental states, it can be assumed that they will have problems with these kinds of metaphors.

中文翻译：

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机器和隐喻：计算机程序检测、解释和产生隐喻的挑战

基于 GPT-4 等神经网络的强大 Transformer 模型使自然语言处理取得了巨大进步。本文指出了处理隐喻的计算机程序面临的三个挑战。首先，双重隐喻现象表明，隐喻解释不一定是由句法、语义或语用张力引发的。对这些隐喻的检测似乎涉及审美愉悦感或高阶心理理论，这两者都很难在计算机程序中实现。其次，解释隐喻所涉及的上下文不是简单给出的，而是必须基于可能涉及预设假装的实用考虑来重建。如果计算机程序无法产生或理解这种形式的伪装，它们在处理某些隐喻时就会遇到问题。最后，对某些隐喻的充分解释和反应似乎需要具有内部的、第一人称的体验和情感状态的能力。由于计算机程序是否具有这种心理状态值得怀疑，因此可以假设它们在处理此类隐喻时会遇到问题。