Analysing Human Strategies of Information Transmission as a Function of Discourse Context
| Authors | |
|---|---|
| Publication date | 2021 |
| Host editors |
|
| Book title | The 25th Conference on Computational Natural Language Learning |
| Book subtitle | CoNLL 2021 : proceedings of the conference : November 10-11, 2021, online |
| ISBN (electronic) |
|
| Event | 25th Conference on Computational Natural Language Learning |
| Pages (from-to) | 647–660 |
| Publisher | Stroudsburg, PA: The Association for Computational Linguistics |
| Organisations |
|
| Abstract |
Speakers are thought to use rational information transmission strategies for efficient communication (Genzel and Charniak, 2002; Aylett and Turk, 2004; Jaeger and Levy, 2007). Previous work analysing these strategies in sentence production has failed to take into account how the information content of sentences varies as a function of the available discourse context. In this study, we estimate sentence information content within discourse context. We find that speakers transmit information at a stable rate—i.e., rationally—in English newspaper articles but that this rate decreases in spoken open domain and written task-oriented dialogues. We also observe that speakers’ choices are not oriented towards local uniformity of information, which is another hypothesised rational strategy. We suggest that a more faithful model of communication should explicitly include production costs and goal-oriented rewards.
|
| Document type | Conference contribution |
| Note | With supplementary video and software |
| Language | English |
| Published at | https://doi.org/10.18653/v1/2021.conll-1.50 |
| Downloads |
2021.conll-1.50
(Final published version)
|
| Supplementary materials | |
| Permalink to this page | |