Ge Shi


2024

pdf bib
RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts
Hongzheng Li | Ruojin Wang | Ge Shi | Xing Lv | Lei Lei | Chong Feng | Fang Liu | Jinkun Lin | Yangguang Mei | Linnan Xu
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Move structures have been studied in English for Specific Purposes (ESP) and English for Academic Purposes (EAP) for decades. However, there are few move annotation corpora for Research Article (RA) abstracts. In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts. The primary objective of RAAMove is to facilitate move analysis and automatic move identification. This paper provides a thorough discussion of the corpus construction process, including the scheme, data collection, annotation guidelines, and annotation procedures. The corpus is constructed through two stages: initially, expert annotators manually annotate high-quality data; subsequently, based on the human-annotated data, a BERT-based model is employed for automatic annotation with the help of experts’ modification. The result is a large-scale and high-quality corpus comprising 33,988 annotated instances. We also conduct preliminary move identification experiments using the BERT-based model to verify the effectiveness of the proposed corpus and model. The annotated corpus is available for academic research purposes and can serve as essential resources for move analysis, English language teaching and writing, as well as move/discourse-related tasks in Natural Language Processing (NLP).

2023

pdf bib
A Hybrid Detection and Generation Framework with Separate Encoders for Event Extraction
Ge Shi | Yunyue Su | Yongliang Ma | Ming Zhou
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

The event extraction task typically consists of event detection and event argument extraction. Most previous work models these two subtasks with shared representation by multiple classification tasks or a unified generative approach. In this paper, we revisit this pattern and propose to use independent encoders to model event detection and event argument extraction, respectively, and use the output of event detection to construct the input of event argument extraction. In addition, we use token-level features to precisely control the fusion between two encoders to achieve joint bridging training rather than directly reusing representations between different tasks. Through a series of careful experiments, we demonstrate the importance of avoiding feature interference of different tasks and the importance of joint bridging training. We achieved competitive results on standard benchmarks (ACE05-E, ACE05-E+, and ERE-EN) and established a solid baseline.

pdf bib
Boosting Event Extraction with Denoised Structure-to-Text Augmentation
Bo Wang | Heyan Huang | Xiaochi Wei | Ge Shi | Xiao Liu | Chong Feng | Tong Zhou | Shuaiqiang Wang | Dawei Yin
Findings of the Association for Computational Linguistics: ACL 2023

Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations. In most NLP applications, involving a large scale of synthetic training data is a practical and effective approach to alleviate the problem of data scarcity. However, when applying to the task of event extraction, recent data augmentation methods often neglect the problem of grammatical incorrectness, structure misalignment, and semantic drifting, leading to unsatisfactory performances. In order to solve these problems, we propose a denoised structure-to-text augmentation framework for event extraction (DAEE), which generates additional training data through the knowledge-based structure-to-text generation model and selects the effective subset from the generated data iteratively with a deep reinforcement learning agent. Experimental results on several datasets demonstrate that the proposed method generates more diverse text representations for event extraction and achieves comparable results with the state-of-the-art.

2022

pdf bib
Dynamic Prefix-Tuning for Generative Template-based Event Extraction
Xiao Liu | Heyan Huang | Ge Shi | Bo Wang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We consider event extraction in a generative manner with template-based conditional generation. Although there is a rising trend of casting the task of event extraction as a sequence generation problem with prompts, these generation-based methods have two significant challenges, including using suboptimal prompts and static event type information. In this paper, we propose a generative template-based event extraction method with dynamic prefix (GTEE-DynPref) by integrating context information with type-specific prefixes to learn a context-specific prefix for each context. Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005 and achieves the best performances on ERE.Additionally, our model is proven to be portable to new types of events effectively.

2018

pdf bib
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction
Ge Shi | Chong Feng | Lifu Huang | Boliang Zhang | Heng Ji | Lejian Liao | Heyan Huang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions. Previous studies address this problem by discovering a shared space across genres using manually crafted features, which requires great human effort. To effectively automate this process, we design a genre-separation network, which applies two encoders, one genre-independent and one genre-shared, to explicitly extract genre-specific and genre-agnostic features. Then we train a relation classifier using the genre-agnostic features on the source genre and directly apply to the target genre. Experiment results on three distinct genres of the ACE dataset show that our approach achieves up to 6.1% absolute F1-score gain compared to previous methods. By incorporating a set of external linguistic features, our approach outperforms the state-of-the-art by 1.7% absolute F1 gain. We make all programs of our model publicly available for research purpose