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LastWords: Boring Problems Revisited
Huayang Li, Richard Sproat

LastWords: Boring Problems Revisited

Author / Organization:
Published 2026 Verification Pending

Overview & Synopsis

In 2022, the second author published a Last Words piece in Computational Linguistics in which he pointed out that, despite the advances in AI up to that point, there were still some problems in natural language processing that on the one hand seemed trivial and perhaps boring, but on the other were hard to solve using purely neural methods. One such problem discussed in that context was text normalization, and a particular subproblem of that, namely number name reading. The piece came out just a few months before the first release of ChatGPT, and needless to say, a lot has happened since then. Indeed, as we document here, the problem (of number name reading) has practically been solved, at least by one of the latest frontier models. However, we believe that there are still open issues for such limited domain, constrained and limited resource problems, and that in some future machine learning approach, the massive resources of the latest frontier models would not be needed to solve them.

Indexed from repository: arXiv (arxiv.org)
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Document Information

Document Title LastWords: Boring Problems Revisited
Author / Organization Huayang Li, Richard Sproat
Subject & Discipline cs.CL
Publication Year 2026
License / Rights arXiv Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website arXiv (arxiv.org)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-276664
Original Source Link Visit Source Page on arxiv.org »