Unsupervised Domain Adaptation for Joint Information Extraction (2022)

Abstract: Joint Information Extraction (JIE) aims to jointly solve multiple tasks in the Information Extraction pipeline (e.g., entity mention, event trigger, relation, and event argument extraction). Due to their ability to leverage task dependencies and avoid error propagation, JIE models have presented state-of-the-art performance for different IE tasks. However, an issue with current JIE methods is that they only focus on standard supervised learning setting where training and test data comes from the same domain. Cross-domain/domain adaptation learning with training and test data in different domains have not been explored for JIE, thus hindering the application of this technology to different domains in practice. To address this issue, our work introduces the first study to evaluate performance of JIE models in unsupervised domain adaptation setting. In addition, we present a novel method to induce domain-invariant representations for the tasks in JIE, called Domain Adaptation for Joint Information Extraction (DA4JIE). In DA4JIE, we propose an Instance-relational Domain Adaptation mechanism that seeks to align representations of task instances in JIE across domains through a generalized version of domain-adversarial learning approach. We further devise a Context-invariant Structure Learning technique to filter domain-specialized contextual information from induced representations to boost performance of JIE models in new domains. Extensive experiments and analyses demonstrate that DA4JIE can significantly improve out-of-domain performance for current state-of-the-art JIE systems for all IE tasks.

## introduction an information extraction (ie) system extracting structured information from unstructured text typically involves four major tasks: event trigger detection (etd), event argument extraction (eae), ## conclusion we present da4jie, a novel framework that jointly solves four ie tasks (eme, etd, re, and eae) in uda setting. in particular, da4jie employs instance-relational domain adaptation method that generalizes the standard domain-adversarial training approach to simultaneously align high-level type representations of all downstream tasks between domains. additionally, we incorporate a context-invariant graph learning module into the encoder to encourage the usage of domainindependent information at low-level, thus extracting more transferable features to improve model's performance in new domains. the extensive experiments demonstrate the effectiveness of the proposed framework. in the future, we plan to extend our approach to more general settings such as multisource domain adaptation with more ie subtasks such as entity/event coreference resolution.