Papers by Jialong Han

CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction (2021.acl-long)

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Zhengbao Jiang, Jialong Han, Bunyamin Sisman, Xin Luna Dong

Challenge:	Existing methods to integrate extracted knowledge from the Web to knowledge graphs (KGs) however, the predictions are made independently, which can be mutually inconsistent.
Approach:	They propose a relation integration model that aligns free-text relations to relations in a target KG . they propose combining two stages to make independent predictions and a collective model that accesses all candidate predictions.
Outcome:	The proposed model outperforms baseline models on two datasets and improves AUC from .677 to .748 and from 1.716 to 1.780.

hyperdoc2vec: Distributed Representations of Hypertext Documents (P18-1)

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Jialong Han, Yan Song, Wayne Xin Zhao, Shuming Shi, Haisong Zhang

Representation Learning

Challenge:	Conventional text embedding methods suffer from information loss if directly adapted to hyper-documents.
Approach:	They propose an embedding approach for hyper-documents that incorporates four criteria to preserve necessary information for embeddable models.
Outcome:	The proposed model outperforms several existing models on two tasks in the academic domain.

A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land? (2020.emnlp-main)

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Hongyu Lin, Yaojie Lu, Jialong Tang, Xianpei Han, Le Sun, Zhicheng Wei, Nicholas Jing Yuan

Language Models

Challenge:	Named entity recognition (NER) is a fundamental task of information extraction.
Approach:	They propose to perform randomization tests on standard NER benchmarks to examine name regularity, mention coverage and context diversity.
Outcome:	The proposed model performs better on standard NER benchmarks than other models on open datasets.

KEPLET: Knowledge-Enhanced Pretrained Language Model with Topic Entity Awareness (2023.findings-emnlp)

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Yichuan Li, Jialong Han, Kyumin Lee, Chengyuan Ma, Benjamin Yao, Xiaohu Liu

Semantic Text Processing

Challenge:	Pre-trained language models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks.
Approach:	They propose a Knowledge-Enhanced Pre-trained LanguagE model with Topic entity awareness that incorporates the interactions between tokens and mentioned entities in pre-training.
Outcome:	The proposed model incorporates the interactions between tokens and mentioned entities in pre-training and is more effective on entity-centric tasks.

Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)

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Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, Shaoyi Chen

Information Extraction & Text Mining

Challenge:	Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks.
Approach:	They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm .
Outcome:	The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings.

Syntactic and Semantic-driven Learning for Open Information Extraction (2020.findings-emnlp)

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Jialong Tang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Xinyan Xiao, Hua Wu

Information Extraction & Text Mining

Challenge:	Experimental results show that our approach significantly outperforms the supervised counterparts, and can even achieve competitive performance to supervised state-of-the-art (SoA) model.
Approach:	They propose a syntactic and semantic-driven learning approach that can learn open IE models without human-labelled data by leveraging syntakic and semantic knowledge as noisier, higher-level supervision.
Outcome:	The proposed approach outperforms supervised counterparts and can achieve competitive performance to supervised state-of-the-art models.

Hypernymy Detection for Low-Resource Languages via Meta Learning (2020.acl-main)

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Changlong Yu, Jialong Han, Haisong Zhang, Wilfred Ng

Semantic Text Processing

Challenge:	Existing studies focus on monolingual hypernymy detection on high-resource languages, but few investigate low-resourced scenarios.
Approach:	They propose to combine high-resource languages to solve low-resourced hypernymy detection problem . they extensively compare three joint training paradigms and propose meta learning .
Outcome:	The proposed method significantly improves performance of extremely low-resource languages by preventing over-fitting on small datasets.

From Discourse to Narrative: Knowledge Projection for Event Relation Extraction (2021.acl-long)

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Jialong Tang, Hongyu Lin, Meng Liao, Yaojie Lu, Xianpei Han, Le Sun, Weijian Xie, Jin Xu

Information Extraction & Text Mining

Challenge:	Existing event-centric knowledge graphs rely on explicit connectives to extract relations between events.
Approach:	They propose a knowledge projection paradigm for event relation extraction using commonalities between events.
Outcome:	The proposed method achieves state-of-the-art performance and extrinsic results verify the extracted event relations.

A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check (D18-1)

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Dingmin Wang, Yan Song, Jing Li, Jialong Han, Haisong Zhang

Challenge:	Chinese spelling check (CSC) is a challenging but meaningful task that serves as a preprocessing in many natural language processing(NLP) applications.
Approach:	They propose to construct Chinese spelling check corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to OCR- and ASR-based methods. Experimental results demonstrate the effectiveness of the approach.
Outcome:	The proposed method is based on visual or phonologically similar spelling errors, and is validated with respect to three standard test sets.

When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models (2020.emnlp-main)

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Changlong Yu, Jialong Han, Peifeng Wang, Yangqiu Song, Hongming Zhang, Wilfred Ng, Shuming Shi

Responsible & Trustworthy NLP

Challenge:	a taxonomy is a semantic hierarchy of words or concepts organized w.r.t. their hypernymy relationships.
Approach:	They propose a framework for hypernymy detection using large textual corpora . they quantify the non-negligible existence of specific sparsity cases .
Outcome:	The proposed framework quantifies the non-negligible existence of specific sparsity cases on several benchmark datasets.

Feasible is Not Enough: Cost-Aware Optimal Tool-Chain Planning on Multi-Solution Tool Graphs (2026.findings-acl)

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Liangliang Liu, Yanming Li, Yigang Liu, Jialong Han, Rujia Shen, Yi Guan, Yi Lin, Jingchi Jiang

Green & Sustainable NLP

Challenge:	Existing tools and benchmarks often form tool learning (TL) as a single-solution setting . exploring large-scale TG is computationally expensive, especially under constrained context budgets.
Approach:	They propose a framework for learning optimal TL policies over large tool graphs . they train a reinforcement learning agent to acquire transferable expansion skills .
Outcome:	The proposed framework improves task success and solution optimality by 46.21% and 66.34% on multiSoTLBench.

Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders (2020.acl-main)

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Yu Duan, Canwen Xu, Jiaxin Pei, Jialong Han, Chenliang Li

Language Models

Challenge:	Existing conditional generation models cannot handle emerging conditions due to their joint end-to-end learning fashion.
Approach:	They propose a framework for conditional text generation that decouples the text generation module from the condition representation module to allow "one-to-many" conditional generation.
Outcome:	The proposed framework decouples the text generation module from the condition representation module to allow “one-to-many” conditional generation.