Pith. sign in

REVIEW 1 cited by

An Analysis of User Behaviors for Objectively Evaluating Spoken Dialogue Systems

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2401.04867 v2 pith:Y6NTFYCG submitted 2024-01-10 cs.CL cs.AIcs.HC

classification cs.CLcs.AIcs.HC
keywords dialoguebehaviorsuserevaluationtasksinterviewsystemsattentive
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Establishing evaluation schemes for spoken dialogue systems is important, but it can also be challenging. While subjective evaluations are commonly used in user experiments, objective evaluations are necessary for research comparison and reproducibility. To address this issue, we propose a framework for indirectly but objectively evaluating systems based on users' behaviors. In this paper, to this end, we investigate the relationship between user behaviors and subjective evaluation scores in social dialogue tasks: attentive listening, job interview, and first-meeting conversation. The results reveal that in dialogue tasks where user utterances are primary, such as attentive listening and job interview, indicators like the number of utterances and words play a significant role in evaluation. Observing disfluency also can indicate the effectiveness of formal tasks, such as job interview. On the other hand, in dialogue tasks with high interactivity, such as first-meeting conversation, behaviors related to turn-taking, like average switch pause length, become more important. These findings suggest that selecting appropriate user behaviors can provide valuable insights for objective evaluation in each social dialogue task.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Symbolic or Numerical? Understanding Physics Problem Solving in Reasoning LLMs

    cs.CL 2025-07 conditional novelty 5.0 of 10

    DeepSeek-R1 achieves 75.9% zero-shot and 81.3% few-shot accuracy on filtered SciBench physics problems, far above general-purpose chat models, and correct answers tend to use symbolic derivation.

Pith tools