K o RC : Knowledge Oriented Reading Comprehension Benchmark for Deep Text Understanding (2023)

Abstract: Deep text understanding, which requires the connections between a given document and prior knowledge beyond its text, has been highlighted by many benchmarks in recent years. However, these benchmarks have encountered two major limitations. On the one hand, most of them require human annotation of knowledge, which leads to limited knowledge coverage. On the other hand, they usually use choices or spans in the texts as the answers, which results in narrow answer space. To overcome these limitations, we build a new challenging benchmark named KoRC in this paper. Compared with previous benchmarks, KoRC has two advantages, i.e., broad knowledge coverage and flexible answer format. Specifically, we utilize massive knowledge bases to guide annotators or large language models (LLMs) to construct knowledgable questions. Moreover, we use labels in knowledge bases rather than spans or choices as the final answers. We test state-of-the-art models on KoRC and the experimental results show that the strongest baseline only achieves 68.3% and 30.0% F1 measure in the IID and OOD test set, respectively. These results indicate that deep text understanding is still an unsolved challenge. We will release our dataset and baseline methods upon acceptance.

## introduction deep text understanding requires the integration of text information with its relevant background (prior) knowledge @xcite @xcite . it has been a long-pursued goal in natural language understanding @xcite @xcite 2019) for decades, and plays a key role in many real-world applications. many benchmarks have been proposed to guide the development of deep text understanding skills. early attempts formalize text understanding into machine reading comprehension (mrc) framework, such as squad @xcite and race @xcite . readers are required to answer questions about the given document in mrc tasks. recently proposed benchmarks further highlight the requirement of deep text understanding. to answer their questions, benchmarks such as cos-mosqa @xcite , dream @xcite , and c 3 @xcite have tapped into knowledge beyond the text. moreover, it is necessary for deep text understanding to reason over a combination of different knowledge sources, as required by @xcite and wikihop @xcite , etc. however, these benchmarks have encountered two limitations. ## dataset analysis we perform a detailed analysis of korc. we first design two evaluation metrics where the number of answers are in-determinant. then, we investigate sophisticated data splitting strategy. finally, we conduct comprehensive analysis with regard to the data distribution in korc. ## experiments we establish the initial baselines for korc and use korc to analyze the deep text understanding ability of these baseline models. more experiments, analysis, and benchmark results are included in the project repository. ## conclusion in this paper, we propose a new benchmark-korc for deep text understanding with broad knowledge coverage and flexible answer format. our contributions are not only the dataset itself, but also we demonstrate the feasibility to guide llms to generate deep text understanding questions with the help of large-scale background kb. our baseline experiments demonstrates to which extent existing powerful models can leverage background knowledge to understand passages by trying to solve korc. in the future, we plan to extend korc to more complicated knowledge, such as literal knowledge and qualifier knowledge in common knowledge bases. it is intriguing to design more skillful reader models via connecting the document with background knowledge.