ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1808.09178
  4. Cited By
Analysing the potential of seq-to-seq models for incremental
  interpretation in task-oriented dialogue

Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue

28 August 2018
Dieuwke Hupkes
S. Bouwmeester
Raquel Fernández
ArXiv (abs)PDFHTML

Papers citing "Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue"

4 / 4 papers shown
Can Visual Dialogue Models Do Scorekeeping? Exploring How Dialogue Representations Incrementally Encode Shared Knowledge
Can Visual Dialogue Models Do Scorekeeping? Exploring How Dialogue Representations Incrementally Encode Shared KnowledgeAnnual Meeting of the Association for Computational Linguistics (ACL), 2022
Brielen Madureira
David Schlangen
163
4
0
14 Apr 2022
Inducing Causal Structure for Interpretable Neural Networks
Inducing Causal Structure for Interpretable Neural Networks
Atticus Geiger
Zhengxuan Wu
Hanson Lu
J. Rozner
Elisa Kreiss
Thomas Icard
Noah D. Goodman
Christopher Potts
CMLOOD
384
94
0
01 Dec 2021
Causal Abstractions of Neural Networks
Causal Abstractions of Neural NetworksNeural Information Processing Systems (NeurIPS), 2021
Atticus Geiger
Hanson Lu
Thomas Icard
Christopher Potts
NAICML
353
308
0
06 Jun 2021
Incremental Processing in the Age of Non-Incremental Encoders: An
  Empirical Assessment of Bidirectional Models for Incremental NLU
Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLUConference on Empirical Methods in Natural Language Processing (EMNLP), 2020
Brielen Madureira
David Schlangen
132
21
0
11 Oct 2020
1
Page 1 of 1