The success of deep learning in natural language processing raises intriguing
questions about the nature of linguistic meaning and ways in which it can be
processed by natural and artificial systems. One such question has to do with
subword segmentation algorithms widely employed in language modeling, machine
translation, and other tasks since 2016. These algorithms often cut words into
semantically opaque pieces, such as 'period', 'on', 't', and 'ist' in
'period|on|t|ist'. The system then represents the resulting segments in a dense
vector space, which is expected to model grammatical relations among them. This
representation may in turn be used to map 'period|on|t|ist' (English) to
'par|od|ont|iste' (French). Thus, instead of being modeled at the lexical
level, translation is reformulated more generally as the task of learning the
best bilingual mapping between the sequences of subword segments of two
languages; and sometimes even between pure character sequences:
'p|e|r|i|o|d|o|n|t|i|s|t' $\rightarrow$ 'p|a|r|o|d|o|n|t|i|s|t|e'. Such subword
segmentations and alignments are at work in highly efficient end-to-end machine
translation systems, despite their allegedly opaque nature. The computational
value of such processes is unquestionable. But do they have any linguistic or
philosophical plausibility? I attempt to cast light on this question by
reviewing the relevant details of the subword segmentation algorithms and by
relating them to important philosophical and linguistic debates, in the spirit
of making artificial intelligence more transparent and explainable.

本文讨论词的子词分割算法在自然语言处理和机器翻译等任务中的应用。虽然这些算法将单词分为形式上相对含义不明确的子词，然而它们在高效的机器翻译系统中发挥着作用。本文探讨这种算法的语言学和哲学学理基础，并试图使人工智能更透明和可解释。