New Frontiers of Information Extraction (2022)

Abstract: This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources. Particularly, this tutorial will provide audience with a systematic introduction to recent advances of IE, by answering several important research questions. These questions include (i) how to develop an robust IE system from noisy, insufficient training data, while ensuring the reliability of its prediction? (ii) how to foster the generalizability of IE through enhancing the system’s cross-lingual, cross-domain, cross-task and cross-modal transferability? (iii) how to precisely support extracting structural information with extremely fine-grained, diverse and boundless labels? (iv) how to further improve IE by leveraging indirect supervision from other NLP tasks, such as NLI, QA or summarization, and pre-trained language models? (v) how to acquire knowledge to guide the inference of IE systems? We will discuss several lines of frontier research that tackle those challenges, and will conclude the tutorial by outlining directions for further investigation.

## introduction information extraction (ie) is the process of automatically extracting structural information from unstructured or semi-structured data. it provides the essential support for natural language understanding by recognizing and resolving the concepts, entities, events described in text, and inferring the relations among them. in various application domains, ie automates the costly acquisition process of domain-specific knowledge representations that have been the backbone of any knowledge-driven ai systems. for example, automated knowledge base construction has relied on technologies for entity-centric ie @xcite . extraction of events and event chains assists machines with narrative prediction @xcite and summarization tasks @xcite . medical ie also benefits important but expensive clinical tasks such as drug discovery and repurposing @xcite . despite the importance, frontier research in ie still faces several key challenges. the first challenge is that existing dominant methods using language modeling representation cannot sufficiently capture the essential knowledge and structures required for ie tasks. the second challenge is on the development of extraction models for fine-grained information with less supervision, considering that obtaining structural annotation on unlabeled data has been very costly. the third challenge is to extend the reliability and generalizability of ie systems in real-world scenarios, where data sources often contain incorrect, invalid or unrecognizable inputs, as well as inputs containing unseen labels and mixture of modalities. by tackling those critical challenges, recent literature is leading to transformative advancement in principles and methodologies of ie system development. we believe it is necessary to present a timely tutorial to comprehensively summarize the new frontiers in ie research and point out the emerging challenges that deserve further investigation. in this tutorial, we will systematically review several lines of frontier research on developing robust, reliable and adaptive learning systems for extracting rich structured information. beyond introducing robust learning and inference methods for unsupervised denoising, constraint capture and novelty detection, we will discuss recent approaches for leveraging indirect supervision from natural language inference and generation tasks to improve ie. we will also review recent minimally supervised methods for training ie models with distant supervision from linguistic patterns, corpus statistics or language modeling objectives. in addition, we will minimally supervised ie indirect supervision weak supervision modeling label semantics qa as ie nli as ie nlg as ie linguistic pattern mining language model probing robust ie noise-robustness faithfulness and fairness logical consistency corpus statistics noise-robust training ood detection debiased training counterfactual inference constraint learning constrained inference transferable ie cross-domain transfer cross-lingual transfer cross-type transfer adversarial domain adaptation divergence-based adaptation language-invariant representation data projection prototype learning reading definitions cross-modal & knowledge-guided ie vision-language ie semi-structured ie schema-guided ie statistics-guided ie commonsense ie ie with less supervision robust and reliable ie portable and generalizable ie new frontiers of ie figure 1: a roadmap for new frontiers of information extraction and a graphical abstract of this tutorial. illustrate how a model trained on a close domain can be reliably adapted to produce extraction from data sources in different domains, languages and modalities, or acquiring global knowledge (e.g., event schemas) to guide the extraction on a highly diverse open label space. participants will learn about recent trends and emerging challenges in this topic, representative tools and learning resources to obtain ready-to-use models, and how related technologies benefit end-user nlp applications. a graphical abstract of this tutorial is provided as fig. 1, which serves as our roadmap for new frontiers of information extraction. ## outline of tutorial content this half-day tutorial presents a systematic overview of recent advancement in ie technologies. we will begin motivating this topic with a selection of real-world applications and emerging challenges of ie. then, we will introduce robust learning methods and inference methods to tackle noisy supervision, prediction inconsistency and outof-distribution (ood) inputs. we will also discuss about indirect supervision and minimal supervision methods that further improves ie model development under limited learning resources. based on the robust ie systems developed in close-domain settings, we will explain how transfer learning technologies can adaptively extend the utility of the sys-tems across domains, languages and tasks, and how complementary information can be extracted from data modalities other than human language. moreover, we will exemplify the use of aforementioned technologies in various end-user nlp applications such as misinformation detection and scientific discovery, and will outline emerging research challenges that may catalyze further investigation on developing reliable and adaptive learning systems for ie. the detailed contents are outlined below. ## specification of the tutorial the proposed tutorial is considered a cutting-edge tutorial that introduces new frontiers in ie research. the presented topic has not been covered by acl/emnlp/naacl/eacl/coling tutorials in the past 4 years. one exception is the acl 2020 tutorial "multi-modal information extraction from text, semi-structured, and tabular data on the web" that is partly relevant to one of our technical sections ( §2.5). that particular section of our talk will focus on ie from visual and multi-media data in addition to semi-structured data, being different from the aforementioned acl 2020 tutorial that has mainly covered topics on semi-structured data. ## tutorial instructors the following are biographies of the speaker. past tutorials given by us are listed in appx. §a.1.