method
active
method:neutral-nli-diversityNeutral NLI Diversity
Variant weighting neutral predictions equally to contradictions to test if neutrals capture lexical diversity
Neighborhood — ranked by edge-count
Papers (1)
paper
- Semantic Diversity in Dialogue with Natural Language Inferenceintroducesmentions
Methods (2)
method
- Baseline NLI Diversityextendsrelated_toFirst variant: aggregates argmax NLI class predictions with contradiction=+1, entailment=-1, neutral=0
- NLI Diversityrelated_toNovel metric proposed in this paper using NLI predictions to score semantic diversity of a response set
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Best-performing variant: aggregates softmax probability mass rather than binary class counts
- Hypothesis proposed to explain Neutral NLI Diversity's high performance on decTest but low on conTest
- Motivates the creation of Neutral NLI Diversity as an ablation
- Limitation acknowledged in discussion section
- Confidence NLI Diversity achieves state-of-the-art performance on measuring semantic diversityclaim0.785Main performance claim of the paper
- Future work question arising from the finding that Combined model did not outperform MNLI model
- A diverse set of responses for a conversation captures contradictory ways one could respond, measurable by an NLI modelhypothesis0.738Core hypothesis motivating the NLI Diversity metric
- Indicates lack of statistically significant differences between top methods