Neural Speed Reading Audited
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Dokumenter
- Neural Speed Reading Audited
Forlagets udgivne version, 324 KB, PDF-dokument
Several approaches to neural speed reading have been presented at major NLP and machine learning conferences in 2017–20; i.e., “human-inspired” recurrent network architectures that learn to “read” text faster by skipping irrelevant words, typically optimizing the joint objective of minimizing classification error rate and FLOPs used at inference time. This paper reflects on the meaningfulness of the speed reading task, showing that (a) better and faster approaches to, say, document classification, already exist, which also learn to ignore part of the input (I give an example with 7% error reduction and a 136x speed-up over the state of the art in neural speed reading); and that (b) any claims that neural speed reading is “human-inspired”, are ill-founded.
Originalsprog | Engelsk |
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Titel | Findings of the Association for Computational Linguistics: EMNLP 2020 |
Forlag | Association for Computational Linguistics |
Publikationsdato | 2020 |
Sider | 148–153 |
DOI | |
Status | Udgivet - 2020 |
Begivenhed | The 2020 Conference on Empirical Methods in Natural Language Processing - online Varighed: 16 nov. 2020 → 20 nov. 2020 http://2020.emnlp.org |
Konference
Konference | The 2020 Conference on Empirical Methods in Natural Language Processing |
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Lokation | online |
Periode | 16/11/2020 → 20/11/2020 |
Internetadresse |
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