Span-Based Semantic Role Labeling with Contrastive Learning
Authors: Callum Kodai Tulloch, Koichi Takeuchi


Reviewer paper summaries: This paper produce a contrastive learning method for Span-Based Semantic Role Labeling. The contrastive learning is applied in two directions as in the CLIP paper (Radford et al., 2021). Experiments are done in a Japanese data set, where the baselines are each of the one direction of contrastive learning.
Summary of strengths: 1. Paper is well written with clear picture and equations highlighted.
Summary of weaknesses: 1. The experiments are done in a not so popular benchmark.
2. The baselines are simple ablation study.
3. Method is not particularly novel.
Comments, suggestions and typos: Right now, this is a prelimary technical report. To show that the contrastive learning actually works, you need to work on popular benchmarks and built on top of SOTA models.

Meanwhile, in general, SRL tasks alone might be losing its' importance.
Overall assessment scores: 2