Mirror: A Universal Framework for Various Information Extraction Tasks (2023)

Abstract: Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations. Meanwhile, this divergence leads to information waste and increases difficulties in building complex applications in real scenarios. Recent studies often formulate IE tasks as a triplet extraction problem. However, such a paradigm does not support multi-span and n-ary extraction, leading to weak versatility. To this end, we reorganize IE problems into unified multi-slot tuples and propose a universal framework for various IE tasks, namely Mirror. Specifically, we recast existing IE tasks as a multi-span cyclic graph extraction problem and devise a non-autoregressive graph decoding algorithm to extract all spans in a single step. It is worth noting that this graph structure is incredibly versatile, and it supports not only complex IE tasks, but also machine reading comprehension and classification tasks. We manually construct a corpus containing 57 datasets for model pretraining, and conduct experiments on 30 datasets across 8 downstream tasks. The experimental results demonstrate that our model has decent compatibility and outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings. The code, model weights, and pretraining corpus are available at https://github.com/Spico197/Mirror .

## introduction information extraction (ie) is a fundamental field in natural language processing (nlp), which aims to extract structured information from unstructured text @xcite , such as named entity recognition (ner) @xcite @xcite , relation extraction (re) @xcite , event extraction (ee). however, each ie task is usually isolated from specific data structures and delicate models, which makes it figure 1 : multi-span cyclic graph for discontinuous ner and re tasks (best viewed in color). the spans are connected by three types of edges, including consecutive connections, dotted jump connections and tail-tohead connections. adr in discontinuous ner refers to the entity label of adverse drug reaction. difficult to share knowledge across tasks @xcite . in order to unify the data formats and take advantage of common features between different tasks, there are two main routes in recent studies. the first one is to utilize generative pretrained language models (plms) to generate the structured information directly. @xcite and @xcite structure the ie tasks as a sequence-tosequence generation problem and use generative models to predict the structured information autoregressively. however, such methods cannot provide the exact positions of the structured information, which is essential to the ner task and fair evaluations @xcite . besides, the generationbased methods are usually slow and consume huge resources to train on large-scale datasets @xcite . the second way is to apply the extractive plms, which are faster to train and inference. single-span ner: flat and nested ner tasks with consecutive spans. multi-span: multi-span extraction, e.g., the discontinuous ner. n-ary tuple: the ability of n-ary tuple extraction, e.g., quadruple extraction. classification: the classification tasks. mrc: extractive machine reading comprehension tasks. it is worth noting that generative models (tanl, uie, deepstruct, and instructuie) may be capable of all the tasks if their current paradigms or patterns are changed. however, since the original papers do not contain relevant experiments, we mark them as ✗ or • here. usm @xcite regards the ie tasks as a triplet prediction problem via semantic matching. however, this method is limited to a small range of triplet-based tasks, and it is unable to address multi-span and n-ary extraction problems. to overcome the above challenges, we propose mirror, a novel framework that can handle complex multi-span extraction, n-ary extraction, machine reading comprehension (mrc), and even classification tasks, which are not supported by the previous universal ie systems. as exemplified in figure 1 , we formulate ie tasks as a unified multislot tuple extraction problem and transform those tuples into multi-span cyclic graphs. this graph structure is rather flexible and scalable. it can be applied to not only complex ie tasks but also mrc and classification tasks. mirror takes schemas as part of the model inputs, and this benefits few-shot and zero-shot tasks naturally. compared with other models in our contributions are summarized as follows: and tail-to-head connections. maximal cliques also have been used to flat & discontinuous ner tasks @xcite and trigger-available & trigger-free event extractions @xcite . dygie++ takes ner, re, and ee tasks as span graphs and applies iterative propagation to enhance spans' contextual representations @xcite . oneie uses a similar graph structure with global constraint features @xcite . in addition to explicit graph-based multi-task ie systems, generative language models are widely used. @xcite and @xcite add special index tokens into bart @xcite vocabulary to help perform various ner and absa tasks and obtain explicit span positions. tanl @xcite apply t5 @xcite to generate texts with special enclosures as the predicted information. genie @xcite and deepstruct @xcite share a similar idea to generate subject-relation-object triplets, and deepstruct extends the model size to 10b with glm @xcite . ## mirror framework in this section, we introduce the mirror framework. we first address the unified data input format to the model, then introduce the unified task formulation and the model structure. ## conclusion we propose mirror, a schema-guided framework for universal information extraction. mirror transforms ie tasks into a unified multi-slot tuple extraction problem and introduces the multi-span cyclic graph to represent such structures. due to the flexible design, mirror is capable of multi-span and n-ary extraction tasks. compared to previous systems, mirror supports not only complex information extraction but also mrc and classification tasks. we manually collect 57 datasets for pretraining and conduct experiments on 30 datasets across 8 tasks. the experimental results show good compatibility, and mirror achieves competitive performances with state-of-the-art systems.