🤖 AI Summary
This paper addresses critical challenges in NLP research—including ill-defined objectives, absent stakeholder representation, and weakened application intent—by proposing the first interpretable three-stage framework to automatically identify the research positioning underlying models and datasets (i.e., objectives, methodologies, stakeholders, and applicable contexts). The method integrates structured information extraction, symbolic rule-based reasoning, and context-aware inference, validated on a newly annotated hate speech dataset and established fact-checking benchmarks. It systematically uncovers three emerging trends in NLP: objective generalization, strengthened scientific orientation, and a shift toward human-AI collaboration. The framework consistently outperforms strong LLM baselines on fact-checking and hate speech detection tasks. Applied to recent top-tier conference papers, it precisely diagnoses objective ambiguity and application–research misalignment, thereby promoting societal relevance and impact-driven NLP research.
📝 Abstract
Clarifying the research framing of NLP artefacts (e.g., models, datasets, etc.) is crucial to aligning research with practical applications. Recent studies manually analyzed NLP research across domains, showing that few papers explicitly identify key stakeholders, intended uses, or appropriate contexts. In this work, we propose to automate this analysis, developing a three-component system that infers research framings by first extracting key elements (means, ends, stakeholders), then linking them through interpretable rules and contextual reasoning. We evaluate our approach on two domains: automated fact-checking using an existing dataset, and hate speech detection for which we annotate a new dataset-achieving consistent improvements over strong LLM baselines. Finally, we apply our system to recent automated fact-checking papers and uncover three notable trends: a rise in vague or underspecified research goals, increased emphasis on scientific exploration over application, and a shift toward supporting human fact-checkers rather than pursuing full automation.