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researcher

Mark Johnson

9 papers hereh-index 263.9k citations59 works total

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

author position
  • middle author5
  • last author4

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

fields
  • cs.CL6
  • cs.CV2
  • eess.AS1
same name
  • Mark Johnson — 17 papers, h 54
  • Mark Johnson — 2 papers, h 26
  • Mark Johnson — 2 papers
  • Mark Johnson — 2 papers, h 2
  • Mark Johnson — 1 paper, h 2
  • Mark Johnson — 1 paper

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
20182021
most citedNeural Rule-Execution Tracking Machine For Transformer-Based Text Generation

4 citations · 9 across the 4 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CL2021

Blindness to Modality Helps Entailment Graph Mining

Liane Guillou, Sander Bijl de Vroe, Mark Johnson +1

Understanding linguistic modality is widely seen as important for downstream tasks such as Question Answering and Knowledge Graph Population. Entailment Graph learning might also b…

cs.CL2021

Incorporating Temporal Information in Entailment Graph Mining

Liane Guillou, Sander Bijl de Vroe, Mohammad Javad Hosseini +2

We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct even…

cs.CL2021★ 4 cited

Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation

Yufei Wang, Can Xu, Huang Hu +5

Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e.g., BART and T5), have exhibited compelling performance on various natural language gen…

cs.CV2021★ 3 cited

ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning

Yufei Wang, Ian D. Wood, Stephen Wan +1

Novel Object Captioning is a zero-shot Image Captioning task requiring describing objects not seen in the training captions, but for which information is available from external ob…

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