The striking recent advances in eliciting seemingly meaningful language behaviour from language-only machine learning models have only made more apparent, through the surfacing of clear limitations, the need to go beyond the language-only mode and to ground these models "in the world". Proposals for doing so vary in the details, but what unites them is that the solution is sought in the addition of non-linguistic data types such as images or video streams, while largely keeping the mode of learning constant. I propose a different, and more wide-ranging conception of how grounding should be understood: What grounds language is its normative nature. There are standards for doing things right, these standards are public and authoritative, while at the same time acceptance of authority can and must be disputed and negotiated, in interactions in which only bearers of normative status can rightfully participate. What grounds language, then, is the determined use that language users make of it, and what it is grounded in is the community of language users. I sketch this idea, and draw some conclusions for work on computational modelling of meaningful language use.
翻译:最近,在从只使用语言的机器学习模式中吸引似乎有意义的语言行为方面取得了显著的进展,这通过显露出明显的局限性,更明显地表明有必要超越只使用语言的模式,并将这些模式置于“世界上”的基础之上。 这些建议在细节上各不相同,但两者的结合是,在寻求解决办法的同时,还增加了非语言数据类型,如图像或视频流,同时基本上保持学习模式的不变。 我提出了一个不同的、范围更广的概念,说明应如何理解基础语言:什么是其规范性质的基础语言。有正确行事的标准,这些标准是公开和权威性的,同时接受权威是公开和权威性的,同时,接受权威是必须争论和谈判的,在互动中,只有规范性地位的人才能正确参与。那么,语言的根据是语言使用者的确定使用,其基础是语言使用者群体。我勾画了这个概念,并为有意义语言使用的计算模型的工作得出了一些结论。