The Validity of Coreference-based Evaluations of Natural Language Understanding
arXiv:2602.16200v1 Announce Type: new Abstract: In this thesis, I refine our understanding as to what conclusions we can reach from coreference-based evaluations by expanding existing evaluation practices and considering the extent to which evaluation results are either converging or conflicting. First, I analyze standard coreference evaluations and show that their design often leads to non-generalizable conclusions due to issues of measurement validity - including contestedness (multiple, competing definitions of coreference) and convergent validity (evaluation results that rank models differently across benchmarks). Second, I propose and implement a novel evaluation focused on testing systems' ability to infer the relative plausibility of events, a key aspect of resolving coreference. Through this extended evaluation, I find that contemporary language models demonstrate strong performance on standard benchmarks - improving over earlier baseline systems within certain domains and typ
arXiv:2602.16200v1 Announce Type: new Abstract: In this thesis, I refine our understanding as to what conclusions we can reach from coreference-based evaluations by expanding existing evaluation practices and considering the extent to which evaluation results are either converging or conflicting. First, I analyze standard coreference evaluations and show that their design often leads to non-generalizable conclusions due to issues of measurement validity - including contestedness (multiple, competing definitions of coreference) and convergent validity (evaluation results that rank models differently across benchmarks). Second, I propose and implement a novel evaluation focused on testing systems' ability to infer the relative plausibility of events, a key aspect of resolving coreference. Through this extended evaluation, I find that contemporary language models demonstrate strong performance on standard benchmarks - improving over earlier baseline systems within certain domains and types of coreference - but remain sensitive to the evaluation conditions: they often fail to generalize in ways one would expect a human to be capable of when evaluation contexts are slightly modified. Taken together, these findings clarify both the strengths, such as improved accuracy over baselines on widely used evaluations, and the limitations of the current NLP paradigm, including weaknesses in measurement validity, and suggest directions for future work in developing better evaluation methods and more genuinely generalizable systems.
Executive Summary
The article 'The Validity of Coreference-based Evaluations of Natural Language Understanding' critically examines the current practices and limitations of coreference-based evaluations in natural language processing (NLP). The author argues that standard evaluations often lead to non-generalizable conclusions due to issues of measurement validity, including contested definitions of coreference and conflicting results across different benchmarks. The study introduces a novel evaluation method focused on inferring the relative plausibility of events, revealing that while contemporary language models perform well on standard benchmarks, they struggle with generalization under modified evaluation conditions. The findings highlight both the strengths and limitations of current NLP paradigms and suggest avenues for developing more robust evaluation methods and generalizable systems.
Key Points
- ▸ Standard coreference evaluations often lead to non-generalizable conclusions due to measurement validity issues.
- ▸ Contemporary language models show strong performance on standard benchmarks but fail to generalize under modified conditions.
- ▸ A novel evaluation method is proposed to test systems' ability to infer the relative plausibility of events.
- ▸ The study identifies strengths and limitations in current NLP paradigms and suggests directions for future research.
Merits
Comprehensive Analysis
The article provides a thorough analysis of the current state of coreference-based evaluations, highlighting both their strengths and weaknesses.
Novel Evaluation Method
The introduction of a new evaluation method focused on event plausibility inference adds significant value to the field.
Balanced Perspective
The study offers a balanced view of contemporary language models, acknowledging their strengths while also pointing out their limitations.
Demerits
Limited Scope
The study primarily focuses on coreference-based evaluations, which may not cover all aspects of natural language understanding.
Generalizability Concerns
The findings suggest that while models perform well on standard benchmarks, their performance may not generalize to real-world scenarios.
Measurement Validity Issues
The article highlights issues with measurement validity, which could affect the reliability of the evaluation results.
Expert Commentary
The article 'The Validity of Coreference-based Evaluations of Natural Language Understanding' presents a rigorous and well-reasoned critique of current evaluation practices in NLP. The author's analysis of measurement validity issues, including contested definitions of coreference and conflicting benchmark results, is particularly insightful. The introduction of a novel evaluation method focused on event plausibility inference is a significant contribution to the field, as it addresses a critical aspect of coreference resolution that has been overlooked in standard evaluations. The findings highlight the strengths of contemporary language models, such as their improved accuracy on standard benchmarks, while also exposing their limitations, particularly in terms of generalization under modified conditions. This balanced perspective is crucial for advancing the field, as it acknowledges both the progress made and the challenges that remain. The study's implications for practical applications and policy are also noteworthy, as they underscore the need for more robust evaluation methods and generalizable systems. Overall, the article provides a valuable contribution to the ongoing discourse on the validity and reliability of coreference-based evaluations in NLP.
Recommendations
- ✓ Future research should focus on developing more comprehensive evaluation methods that address the issues of measurement validity and generalization.
- ✓ Policymakers and funding agencies should prioritize research aimed at improving the robustness and generalizability of language models.
