Towards Best Experiment Design for Evaluating Dialogue System Output
| Autor: | Samira Shaikh, Sashank Santhanam |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language business.industry Computer science Design of experiments media_common.quotation_subject 02 engineering and technology computer.software_genre Task (project management) Likert scale Consistency (negotiation) Extant taxon Ranking 020204 information systems 0202 electrical engineering electronic engineering information engineering Continuous scale 020201 artificial intelligence & image processing Quality (business) Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing media_common |
| Zdroj: | INLG |
| DOI: | 10.48550/arxiv.1909.10122 |
| Popis: | To overcome the limitations of automated metrics (e.g. BLEU, METEOR) for evaluating dialogue systems, researchers typically use human judgments to provide convergent evidence. While it has been demonstrated that human judgments can suffer from the inconsistency of ratings, extant research has also found that the design of the evaluation task affects the consistency and quality of human judgments. We conduct a between-subjects study to understand the impact of four experiment conditions on human ratings of dialogue system output. In addition to discrete and continuous scale ratings, we also experiment with a novel application of Best-Worst scaling to dialogue evaluation. Through our systematic study with 40 crowdsourced workers in each task, we find that using continuous scales achieves more consistent ratings than Likert scale or ranking-based experiment design. Additionally, we find that factors such as time taken to complete the task and no prior experience of participating in similar studies of rating dialogue system output positively impact consistency and agreement amongst raters Comment: Accepted at INLG 2019 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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