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Michael Hahn

Saarland University

10 papers hereh-index 151.1k citations30 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author7
  • middle author1

Across the 9 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.FL1
affiliations
  • Saarland University
Homepage
same name
  • Michael Hahn — 12 papers, h 4
  • Michael Hahn — 8 papers, h 9
  • Michael Hahn — 8 papers, h 3
  • Michael Hahn — 7 papers, h 3
  • Michael Hahn — 6 papers, h 4
  • Michael Hahn — 5 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162023
most citedTheoretical Limitations of Self-Attention in Neural Sequence Models

146 citations · 155 across the 7 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.FL2019★ 1 cited

Wreath Products of Distributive Forest Algebras

Michael Hahn, Andreas Krebs, Howard Straubing

It is an open problem whether definability in Propositional Dynamic Logic (PDL) on forests is decidable. Based on an algebraic characterization by Bojańczyk, et. al.,(2012) in term…

cs.CL2019

Tabula nearly rasa: Probing the Linguistic Knowledge of Character-Level Neural Language Models Trained on Unsegmented Text

Michael Hahn, Marco Baroni

Recurrent neural networks (RNNs) have reached striking performance in many natural language processing tasks. This has renewed interest in whether these generic sequence processing…

cs.CL2019★ 146 cited

Theoretical Limitations of Self-Attention in Neural Sequence Models

Michael Hahn

Transformers are emerging as the new workhorse of NLP, showing great success across tasks. Unlike LSTMs, transformers process input sequences entirely through self-attention. Previ…

cs.CL2019★ 1 cited

Character-based Surprisal as a Model of Reading Difficulty in the Presence of Error

Michael Hahn, Frank Keller, Yonatan Bisk +1

Intuitively, human readers cope easily with errors in text; typos, misspelling, word substitutions, etc. do not unduly disrupt natural reading. Previous work indicates that letter…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.