Xiaojian Jiang


2023

pdf bib
Complex Event Schema Induction with Knowledge-Enriched Diffusion Model
Yupu Hao | Pengfei Cao | Yubo Chen | Kang Liu | Jiexin Xu | Huaijun Li | Xiaojian Jiang | Jun Zhao
Findings of the Association for Computational Linguistics: EMNLP 2023

The concept of a complex event schema pertains to the graph structure that represents real-world knowledge of events and their multi-dimensional relationships. However, previous studies on event schema induction have been hindered by challenges such as error propagation and data quality issues. To tackle these challenges, we propose a knowledge-enriched discrete diffusion model. Specifically, we distill the abundant event scenario knowledge of Large Language Models (LLMs) through an object-oriented Python style prompt. We incorporate this knowledge into the training data, enhancing its quality. Subsequently, we employ a discrete diffusion process to generate all nodes and links simultaneously in a non-auto-regressive manner to tackle the problem of error propagation. Additionally, we devise an entity relationship prediction module to complete entity relationships between event arguments. Experimental results demonstrate that our approach achieves outstanding performance across a range of evaluation metrics.

pdf bib
Event Ontology Completion with Hierarchical Structure Evolution Networks
Pengfei Cao | Yupu Hao | Yubo Chen | Kang Liu | Jiexin Xu | Huaijun Li | Xiaojian Jiang | Jun Zhao
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Traditional event detection methods require predefined event schemas. However, manually defining event schemas is expensive and the coverage of schemas is limited. To this end, some works study the event type induction (ETI) task, which discovers new event types via clustering. However, the setting of ETI suffers from two limitations: event types are not linked into the existing hierarchy and have no semantic names. In this paper, we propose a new research task named Event Ontology Completion (EOC), which aims to simultaneously achieve event clustering, hierarchy expansion and type naming. Furthermore, we develop a Hierarchical Structure Evolution Network (HalTon) for this new task. Specifically, we first devise a Neighborhood Contrastive Clustering module to cluster unlabeled event instances. Then, we propose a Hierarchy-Aware Linking module to incorporate the hierarchical information for event expansion. Finally, we generate meaningful names for new types via an In-Context Learning-based Naming module. Extensive experiments indicate that our method achieves the best performance, outperforming the baselines by 8.23%, 8.79% and 8.10% of ARI score on three datasets.

2022

pdf bib
Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach
Bo Zhou | Chenhao Wang | Yubo Chen | Kang Liu | Jun Zhao | Jiexin Xu | Xiaojian Jiang | Qiuxia Li
Proceedings of the 29th International Conference on Computational Linguistics

Being able to infer possible events related to a specific target is critical to natural language processing. One challenging task in this line is event sequence prediction, which aims at predicting a sequence of events given a goal. Currently existing approach models this task as a statistical induction problem, to predict a sequence of events by exploring the similarity between the given goal and the known sequences of events. However, this statistical based approach is complex and predicts a limited variety of events. At the same time this approach ignores the rich knowledge of external events that is important for predicting event sequences. In this paper, in order to predict more diverse events, we first reformulate the event sequence prediction problem as a sequence generation problem. Then to leverage external event knowledge, we propose a three-stage model including augmentation, retrieval and generation. Experimental results on the event sequence prediction dataset show that our model outperforms existing methods, demonstrating the effectiveness of the proposed model.

pdf bib
Generating Temporally-ordered Event Sequences via Event Optimal Transport
Bo Zhou | Yubo Chen | Kang Liu | Jun Zhao | Jiexin Xu | Xiaojian Jiang | Qiuxia Li
Proceedings of the 29th International Conference on Computational Linguistics

Generating temporally-ordered event sequences in texts is important to natural language processing. Two emerging tasks in this direction are temporal event ordering (rearranging the set of events to correct order) and event infilling (generating an event at a specified position). To tackle the two related tasks, the existing method adopts a vanilla sequence-to-sequence model via maximum likelihood estimation (MLE). However, applying this approach to these tasks will cause two issues. One issue is that the MLE loss emphasizes strict local alignment and ignores the global semantics of the event. The other issue is that the model adopts a word-level objective to model events in texts, failing to evaluate the predicted results of the model from the perspective of event sequence. To alleviate these issues, we present a novel model to tackle the generation of temporally-ordered event sequences via Event Optimal Transport (EOT). First, we treat the events in the sequence as modeling units and explicitly extract the semantics of the events. Second, to provide event sequence-level evaluation of the predicted results of the model, we directly match events in sequences. Extensive experimental results show that our approach outperforms previous models on all evaluation datasets. In particular, the accuracy is improved by 7.7%, and the Macro F1 is improved by 7.2% on one of the datasets.