Abstract
This paper discusses the current critique against neural network-based Natural Language Understanding solutions known as language models. We argue that much of the current debate revolves around an argumentation error that we refer to as the singleton fallacy: the assumption that a concept (in this case, language, meaning, and understanding) refers to a single and uniform phenomenon, which in the current debate is assumed to be unobtainable by (current) language models. By contrast, we argue that positing some form of (mental) “unobtanium” as definiens for understanding inevitably leads to a dualistic position, and that such a position is precisely the original motivation for developing distributional methods in computational linguistics. As such, we argue that language models present a theoretically (and practically) sound approach that is our current best bet for computers to achieve language understanding. This understanding must however be understood as a computational means to an end.
Highlights
We are at an inspiring stage in research on Natural Language Understanding (NLU), with the development of models that are capable of unprecedented progress across a wide range of tasks (Wang et al, 2019)
It is always precarious to build arguments on inherently vague and general concepts such as “language,” “understanding,” and “meaning,” since the resulting theoretical constructs become so overly general that they almost become vacuous. We think that this is precisely what cumbers the current debate, and our aim in this paper is to shed light on some of the inherent challenges with using these concepts to problematize the current development in NLU. We discuss how these terms are used in the current debate, and we argue that most of the current critique of the semantic capabilities of language models rest on a misunderstanding, or misrepresentation, that we refer to as the singleton fallacy
This paper has argued that much of the current debate on language models rests on what we have referred to as the singleton fallacy: the assumption that language, meaning, and understanding are single and uniform phenomena that are unobtainable by language models
Summary
We are at an inspiring stage in research on Natural Language Understanding (NLU), with the development of models that are capable of unprecedented progress across a wide range of tasks (Wang et al, 2019). It is always precarious to build arguments on inherently vague and general concepts such as “language,” “understanding,” and “meaning,” since the resulting theoretical constructs become so overly general that they almost become vacuous We think that this is precisely what cumbers the current debate, and our aim in this paper is to shed light on some of the inherent challenges with using these concepts to problematize the current development in NLU. In this first section, we discuss how these terms are used in the current debate, and we argue that most of the current critique of the semantic capabilities of language models rest on a misunderstanding, or misrepresentation, that we refer to as the singleton fallacy.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
