A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers (2105.03011v1)

Abstract: Readers of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the content much more efficient. However, building such tools requires data that reflect the difficulty of the task arising from complex reasoning about claims made in multiple parts of a paper. In contrast, existing information-seeking question answering datasets usually contain questions about generic factoid-type information. We therefore present QASPER, a dataset of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers. We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers, motivating further research in document-grounded, information-seeking QA, which our dataset is designed to facilitate.

A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

This paper introduces Qasper, a specialized dataset designed to advance research in NLP by facilitating the development of question answering (QA) systems focused on academic research papers. The presented dataset comprises 5,049 questions derived from 1,585 NLP research papers, emphasizing the need for complex document-level reasoning.

Dataset Characteristics and Construction

Qasper targets information-seeking questions that require answers embedded in the full text of academic papers. Distinct from other existing datasets, which predominantly focus on factoid-style questions, Qasper questions are crafted by NLP practitioners who initially only skim the title and abstract of the paper in question. Answers are provided by separate annotators who validate and supply supporting evidence found within the paper.

This decoupling of question writing and answering ensures genuine information-seeking behavior rather than retrospective fact extraction. Qasper's emphasis is on questions necessitating multi-paragraph reasoning and interpretation of diverse formats such as tables and figures. Consequently, the dataset highlights typical hurdles in scientific discourse comprehension, such as synthesizing disparate pieces of evidence spread across sections like methods, results, and discussion.

Model Evaluation and Benchmarking

The authors assess current QA models' efficacy on the Qasper dataset, demonstrating a significant performance gap between machine and human capabilities. Specifically, state-of-the-art document-level Transformer models, while proficient on other datasets, underperformed by a margin of at least 27 F1 points in answering Qasper questions compared to human annotators. This performance indicates the pressing need for enhancing document-grounded reasoning in QA systems.

The paper also presents a comprehensive analysis of annotator agreement to reinforce the dataset's robustness and validity. An estimated lower bound of human performance was established via inter-annotator comparisons, which the current models still struggle to meet.

Theoretical and Practical Implications

Qasper signifies a pivotal move toward developing machine learning models that more accurately reflect the nuanced human approach to exploring scientific texts. The intricate nature of the questions and the requirement for integrated understanding across diverse document sections make it a valuable resource for researchers aiming to develop more sophisticated, context-aware QA systems.

The insights gleaned from Qasper could have profound implications for the design of next-generation reading comprehension tools, enhancing automated systems' ability to interact meaningfully with scientific literature. By focusing on information-seeking scenarios anchored in real user interactions, Qasper encourages the creation of systems that genuinely assist in digesting complex academic content rather than displaying superficial textual patterns.

Future Directions

Qasper opens avenues for future research in various domains. A natural extension includes compiling similar datasets in other scientific disciplines or languages, given the currently English-centric scope. Additionally, leveraging the dataset for multimodal QA tasks involving figures and tables could further enrich its applicability. Cross-disciplinary collaboration between NLP experts and domain researchers could also refine methodologies for interpreting nuanced scientific discourse and constructing cross-domain QA systems.

In sum, Qasper serves as a substantial contribution to NLP, advocating for QA systems refined enough for rigorous, document-level challenges common in scientific inquiry, thereby pushing the boundaries of natural language understanding in specialized content areas.