Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction (2023)

Abstract: The robustness to distribution changes ensures that NLP models can be successfully applied in the realistic world, especially for information extraction tasks. However, most prior evaluation benchmarks have been devoted to validating pairwise matching correctness, ignoring the crucial validation of robustness. In this paper, we present the first benchmark that simulates the evaluation of open information extraction models in the real world, where the syntactic and expressive distributions under the same knowledge meaning may drift variously. We design and annotate a large-scale testbed in which each example is a knowledge-invariant clique that consists of sentences with structured knowledge of the same meaning but with different syntactic and expressive forms. By further elaborating the robustness metric, a model is judged to be robust if its performance is consistently accurate on the overall cliques. We perform experiments on typical models published in the last decade as well as a representative large language model, and the results show that the existing successful models exhibit a frustrating degradation, with a maximum drop of 23.43 F_1 score. Our resources and code will be publicly available.

## introduction open information extraction (openie) aims to extract n-ary knowledge tuples {(a 1 , p, a 2 , ..., a n )} consisting of n arguments and one predicate from the natural text in a domain-independent manner, which has been served as the backbone to benefit nlp applications for many years @xcite @xcite . due to its structural flexibility, the evaluation of openie is a nontrivial problem, which in turn drives the advancement of the task. early studies @xcite measure the performance of extractions a t e x i @xmath0: extraction results of openie6 for three semantically equivalent sentences from carb and robust. the proposed benchmark robust computes the robustness score on a clique of sentences. nevertheless, it is not trivial to construct a benchmark that satisfies the aforementioned conditions of encompassing both knowledge invariance and distributional shift. first, manual annotation of parallel texts to maintain the same knowledge meaning with different syntactic and expressive forms may result in either too trivial or artificial. second, it is difficult to build a metric that measures the robustness as well as be compatible with existing benchmarks (e.g., @xcite ) to ensure comparability. on the other hand, natural language paraphrasing is defined as producing sentences with different surface forms (syntactic and lexical) by conveying the same semantic meaning @xcite . going beyond the pairwise correctness comparison, can we evaluate the robustness of models based on reliable paraphrases equipped with syntactic and expressive transformations? in this paper, we introduce robust, a robust openie benchmark with ubiquitous syntactic transformations, aiming to evaluate the robustness of openie models. robust is a large-scale human-annotated benchmark consisting of 1, 272 robustness testing cliques, where each clique contains sentences with different syntactic and expressive variations while conveying the same underlying knowledge meaning, for a total of 4,971 sentences and 16,191 knowledge extractions. to obtain each clique, we first adopt a syntactically controllable paraphraser with diversified syntactic sampling and expressive filtering strategies to generate paraphrases for each sentence in carb. we then design a two-stage annotation pipeline to perform sentence correction and knowledge extraction for each individual paraphrase in cliques based on human experts. this data paradigm enables evaluation to go beyond pairwise matching to clique-wise comparisons. upon the testbed structure, we calculate the robustness scores with respect to the worst performance within a clique and further analyze the performance variances on all cliques. this metric fairly reflects the robustness of models to distributional drifts and is also compatible with existing benchmarks calculated at one sentence magnitude. to explore the robustness of existing models, we implement typical openie systems published in the past decade. the experimental results show a dramatic degradation in model performance on robust, with an average drop of 18 percentage points in f 1 scores, indicating that the robustness of existing successful models is far from satisfactory. we then further analyze the correlation between the variances of the model performance and the divergences of the syntactic distances on the cliques. the results find that the variance grows as the syntactic distance increases, and models behaved with similar variance on most of the cliques also demonstrate the inner consistency of our benchmark. in addition, we also evaluate the a representative large language model chatgpt foot_0 for openie. experimental results demonstrate that chatgpt achieves a remarkable performance that is compatible with the state-of-the-art model on carb (f 1 score of 0.516 under the 10-shot setting), yet it still exhibits the robustness issue on robust (f 1 score of 0.275 under the 10-shot setting). ## the robust benchmark in this section, we describe the details of the benchmark construction. the benchmark consists of cliques based on syntactically diverse paraphrase generation and human annotation to unsure the knowledge invariance and distributional shift, where both the syntactic transformations sampled from real world and the human experience guarantee the naturalness. we also provide details of annotations and strategies in the appendix a.1 and a.2. ## data analysis to understand the general characteristics of ro-bust, we provide quantitative statistics at different granularities in comparison to previous benchmarks. in contrast to the traditional analysis on words and sentences, we further investigate the syntactic phenomena on cliques to explain the robustness evaluation. ## experiments in this section, we explore the robustness of existing successful openie systems and further analyze the impact of different model architectures on robustness. we first introduce the proposed robust metric, which calculates the robustness performance on a clique, and then extensively evaluate six typical models from three major categories and a large language model chatgpt. furthermore, based on the clique structure, we analyze the correlation between the variances of the model performance and the syntactic divergences in cliques. ## conclusion and future work in this work, we propose robust, a large-scale human-annotated openie benchmark consisting of 1272 robustness testing cliques, where each clique contains sentences with different syntactic and expressive variations while conveying the same underlying knowledge meaning. we introduce our methodology for constructing the benchmark, including a syntactically and expressively diverse paraphrase generation, and a two-stage manual annotation. a comprehensive analysis is then performed to demonstrate the consistency of the proposed data with the real world. we finally perform extensive experiments on existing successive models as well as a representative large language model, and the results show that the robustness of existing methods is far from satisfied. the further detailed analysis demonstrates the substantial internal coherence of our benchmark, providing inspiration for the future development of robustness benchmarks. we have presented a dataset with metrics to evaluate the robustness of openie models in this paper. however, there are still several limitations that need to be improved in further study. first, there are a few studies exploring the pre-trained language models to perform zero-shot information extraction with advantages. to the lack of open source code, we have not explored the robustness performance of these zero-shot models. second, we think the robustness problem generally exists in the nlp community, we remain the extensive study of robustness examination for more domains and models in future works.