Xun Wang


2023

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Smart Word Suggestions for Writing Assistance
Chenshuo Wang | Shaoguang Mao | Tao Ge | Wenshan Wu | Xun Wang | Yan Xia | Jonathan Tien | Dongyan Zhao
Findings of the Association for Computational Linguistics: ACL 2023

Enhancing word usage is a desired feature for writing assistance. To further advance research in this area, this paper introduces “Smart Word Suggestions” (SWS) task and benchmark. Unlike other works, SWS emphasizes end-to-end evaluation and presents a more realistic writing assistance scenario. This task involves identifying words or phrases that require improvement and providing substitution suggestions. The benchmark includes human-labeled data for testing, a large distantly supervised dataset for training, and the framework for evaluation. The test data includes 1,000 sentences written by English learners, accompanied by over 16,000 substitution suggestions annotated by 10 native speakers. The training dataset comprises over 3.7 million sentences and 12.7 million suggestions generated through rules. Our experiments with seven baselines demonstrate that SWS is a challenging task. Based on experimental analysis, we suggest potential directions for future research on SWS. The dataset and related codes will be available for research purposes.

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Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data
Rumeng Li | Xun Wang | Hong Yu
Findings of the Association for Computational Linguistics: EMNLP 2023

Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text mining, a domain with scarce, sensitive, and imbalanced medical data, is under-explored. We investigate whether LLMs can augment clinical data for detecting Alzheimer’s Disease (AD)-related signs and symptoms from electronic health records (EHRs), a challenging task that requires high expertise. We create a novel pragmatic taxonomy for AD sign and symptom progression based on expert knowledge and generated three datasets: (1) a gold dataset annotated by human experts on longitudinal EHRs of AD patients; (2) a silver dataset created by the data-to-label method, which labels sentences from a public EHR collection with AD-related signs and symptoms; and (3) a bronze dataset created by the label-to-data method which generates sentences with AD-related signs and symptoms based on the label definition. We train a system to detect AD-related signs and symptoms from EHRs. We find that the silver and bronze datasets improves the system performance, outperforming the system using only the gold dataset. This shows that LLMs can generate synthetic clinical data for a complex task by incorporating expert knowledge, and our label-to-data method can produce datasets that are free of sensitive information, while maintaining acceptable quality.

2016

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Exploring Text Links for Coherent Multi-Document Summarization
Xun Wang | Masaaki Nishino | Tsutomu Hirao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Summarization aims to represent source documents by a shortened passage. Existing methods focus on the extraction of key information, but often neglect coherence. Hence the generated summaries suffer from a lack of readability. To address this problem, we have developed a graph-based method by exploring the links between text to produce coherent summaries. Our approach involves finding a sequence of sentences that best represent the key information in a coherent way. In contrast to the previous methods that focus only on salience, the proposed method addresses both coherence and informativeness based on textual linkages. We conduct experiments on the DUC2004 summarization task data set. A performance comparison reveals that the summaries generated by the proposed system achieve comparable results in terms of the ROUGE metric, and show improvements in readability by human evaluation.

2015

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Empty Category Detection With Joint Context-Label Embeddings
Xun Wang | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Update Summarization using a Multi-level Hierarchical Dirichlet Process Model
Jiwei Li | Sujian Li | Xun Wang | Ye Tian | Baobao Chang
Proceedings of COLING 2012

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Implicit Discourse Relation Recognition by Selecting Typical Training Examples
Xun Wang | Sujian Li | Jiwei Li | Wenjie Li
Proceedings of COLING 2012