Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction (2023)

Abstract: Document-level relation extraction (DocRE) attracts more research interest recently. While models achieve consistent performance gains in DocRE, their underlying decision rules are still understudied: Do they make the right predictions according to rationales? In this paper, we take the first step toward answering this question and then introduce a new perspective on comprehensively evaluating a model. Specifically, we first conduct annotations to provide the rationales considered by humans in DocRE. Then, we conduct investigations and discover the fact that: In contrast to humans, the representative state-of-the-art (SOTA) models in DocRE exhibit different reasoning processes. Through our proposed RE-specific attacks, we next demonstrate that the significant discrepancy in decision rules between models and humans severely damages the robustness of models. After that, we introduce mean average precision (MAP) to evaluate the understanding and reasoning capabilities of models. According to the extensive experimental results, we finally appeal to future work to consider evaluating the understanding ability of models because the improved ability renders models more trustworthy and robust to be deployed in real-world scenarios. We make our annotations and code publicly available.

## introduction relation extraction (re), aiming to extract relations between entities from texts, plays an important role in constructing a large-scale knowledge graph @xcite . most previous work extract relations from a single sentence @xcite @xcite , while recent studies adopt multiple sentences as a whole to harvest more relations including inter-sentence relations @xcite , i.e., document-level relation extraction (docre). docre is more challenging because models are required to synthesize all information of a given document and then predict relations by reasoning and language understanding @xcite @xcite . previous work obtains consistent performance gains on docred @xcite , the proposal of which has benefited the rapid development of docre @xcite . however, the extent to which their proposed methods possess language understanding and reasoning capabilities is still understudied. a common evaluation method is to measure average error across a test set, which neglects the situations where models can make right predictions according to wrong features. as shown in figure 1 , the model accurately predicts the relation between věra čáslavská and czech as humans do. however, the evidence words considered by models are incomprehensible to humans. similar situations, where models improve their performance by recognizing the spurious patterns, are identified by parts of the ai community, including annotation artifacts in natural language inference (nli) @xcite @xcite and shallow template matches in named entity recognition (ner) @xcite . these learned spurious patterns can severely damage their robustness and generalization abilities in the corresponding tasks @xcite . to the best of our knowledge, this is the first work to diagnose the decision rules of models in docre. in this paper, we analyze and characterize the understanding ability of sota models in docre, expose the bottleneck of the models, and then introduce a new evaluation metric to select trustworthy and robust models from those well-performed ones. our contributions are summarized as follows: ## data collection our ultimate goal is to provide all of the evidence words (decision rules) that humans rely on during the reasoning process in docre. since it is not feasible for annotators to label relations and evidence from scratch in docre @xcite , we select docred to further annotate our fine-grained decision rule (word-level evidence). our proposed dataset is named docred hwe , where hwe denotes humanannotated word-level evidence. in the following two sections, we first elaborate on the underlying reasons why we conduct word-level evidence annotation and why on docred, and then introduce the details of our annotation. ## limitation in this paper, we propose docred hwe and introduce a new metric to select the most robust and trustworthy model from those well-performed ones in docre. however, all data in docred are sampled from wikipedia and wikidata, which indicates that training and test data in docred can be identically and independently distributed (i.i.d. assumption). the i.i.d. assumption impedes our demonstration of the intuition: a model with a higher map will obtain a higher f1 score on the test set. due to the i.i.d. assumption, models can succeed in obtaining a higher f1 score by greedily absorbing all correlations (including spurious correlations) in the training data. to strictly demonstrate the intuition, we need a test set that exhibits different and unknown testing distributions. in addition, expanding the research scope to a cleaner re-docred and analyzing the role of unobservable wrong labels are also crucial and interesting ideas. we leave them as our future work. ## conclusion based on our analysis of the decision rules of existing models on docre and our annotated word-level evidence, we expose the bottleneck of the existing models by our introduced map and our proposed re-specific attacks. we also extract some instructive suggestions by exploring the connections between the experimental results. we appeal to future research to take understanding and reasoning capabilities into consideration when evaluating a model and then guide models to learn evidence from humans. based on proper evaluation and guidance, significant development can be brought to the document-level re, where the performance, generalization ability, and robustness of models are more likely to be improved. p577 should be p580.