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Maintaining Common Ground in Dynamic Environments

Summary

Most grounded dialogue benchmarks test whether agents can establish shared understanding of a fixed scene. They largely overlook keeping that understanding up to date as the world changes. Udagawa and Aizawa (TACL, 2021) address this gap with a sequential collaborative reference task that extends the OneCommon setup over time. Two agents each see part of a 2-D plane with dots whose color, size and position are continuous values. The dots move, and the agents' views shift between turns. At every turn the partners must agree on one shared dot. The game ends at the first mismatch, so the number of consecutive successful turns measures how well common ground is maintained. Using crowdsourcing, the authors built Dynamic-OneCommon Corpus (D-OCC). Their analysis shows people rely on rich spatio-temporal descriptions and refer back to earlier agreements. A neural baseline handles spatial cues and keeps a previously shared referent, but makes poor use of temporal information and performs worse with real humans than in selfplay. The resource offers a controlled testbed for dialogue systems in changing environments.

Key points

  • D-OCC contains 5,617 dialogues with more than 65K utterances, collected via Amazon Mechanical Turk. Code and data are at github.com/Alab-NII/dynamic-onecommon.
  • Evaluation separates two skills. Success at the first selection reflects creating common ground. The length of successful timesteps (LST) reflects maintaining it.
  • Entity movements are synthetic and fully controllable. This is meant to reduce biases, increase diversity and allow causal probing of model behavior.
  • The baseline builds on Udagawa and Aizawa (2020). It retains common ground best when given an explicit meta feature. It struggles with temporal reasoning and drops notably when paired with human partners.
  • Proposed future work includes:
    • reference and spatio-temporal expression annotation;
    • reinforcement learning through selfplay;
    • pragmatic reasoning (RSA);
    • video and vision-language pretraining.

Our summary of the paper, in our own words β€” not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

11 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 10 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00409 Β· arXiv 2105.14207

@article{udagawa2021maintaining,
  title = {{Maintaining Common Ground in Dynamic Environments}},
  author = {Udagawa, Takuma and Aizawa, Akiko},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2021},
  doi = {10.1162/tacl\_a\_00409},
  eprint = {2105.14207},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1162/tacl_a_00409}
}

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