Should i tell all?: an experiment on conciseness in spoken dialogue
Bibliographic record
Abstract
Spoken dialogue systems have a strong requirement to produce concise and informative utterances. While interacting over a phone, users must both understand the system’s utterances, and remember important facts that the system is providing. Thus most dialogue systems implement some combination of different techniques for (1) option selection: pruning the set of options; (2) information selection: selecting a subset of information to present about each option; (3) aggregation: combining multiple items of information succinctly. We first describe how user models based on multi-attribute decision theory support domain-independent algorithms for both option selection and information selection. We then describe experiments to determine an optimal level of conciseness in information selection, i.e. how much information to include for an option. Our results show that (a) users are highly oriented to utterance conciseness; (b) the information selection algorithm is highly consistent with user’s judgments of conciseness; and (c) the appropriate level of conciseness is both user and dialogue strategy dependent. 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".