Small Models Are (Still) Effective Cross-Domain Argument Extractors
Authors: William Gantt, Aaron Steven White


Reviewer paper summaries: This paper studies the problem of cross-domain event argument extraction. Two approaches---question answering and template infilling---are investigated using six event argument extraction datasets, in total 30 source-target pairs. Results show that small fine-tuned models (i.e., Flan-T5) outperform the zero-shot prompt-based approach using GPT models.
Summary of strengths: The paper is well structured and easy to follow; the contribution is clear; the takeaway message is simple and has practical implications (`training a smaller model in-house on recasted existing resources can be a cheaper, more effective first-line approach to extraction in a new domain`)
Summary of weaknesses: * Comparing models fine-tuned on similar datasets (cross-domain transfer) against prompt-based GPT models (zero-shot) may not be fair because the former approach uses labelled data; I guess prompt-based GPT models can also easily use these resources, e.g., in-context learning.
* Additional qualitative analysis may help readers understand the strengths and weaknesses of explored models better (my suggestion is moving Section 3.1 to the appendix to leave space for error analysis)
Comments, suggestions and typos: N/A
Overall assessment scores: 3
Metareview score: 3
Metareview: This paper focuses on cross-domain event argument extraction. The authors show that small models trained with annotated examples outperform large language models in zero-shot transfer settings. They support their claim with experiments conducted across six different datasets.
Summary of reasons to publish: They study a challenging problem and provide comprehensive experimental results across a wide range of datasets, which can be valuable for future research.