Jian Wu


2024

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Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences
Sai Koneru | Jian Wu | Sarah Rajtmajer
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Hypothesis formulation and testing are central to empirical research. A strong hypothesis is a best guess based on existing evidence and informed by a comprehensive view of relevant literature. However, with exponential increase in the number of scientific articles published annually, manual aggregation and synthesis of evidence related to a given hypothesis is a challenge. Our work explores the ability of current large language models (LLMs) to discern evidence in support or refute of specific hypotheses based on the text of scientific abstracts. We share a novel dataset for the task of scientific hypothesis evidencing using community-driven annotations of studies in the social sciences. We compare the performance of LLMs to several state of the art methods and highlight opportunities for future research in this area. Our dataset is shared with the research community: https://github.com/Sai90000/ScientificHypothesisEvidencing.git

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Mind’s Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models
Weize Liu | Guocong Li | Kai Zhang | Bang Du | Qiyuan Chen | Xuming Hu | Hongxia Xu | Jintai Chen | Jian Wu
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Large language models (LLMs) have achieved remarkable advancements in natural language processing. However, the massive scale and computational demands of these models present formidable challenges when considering their practical deployment in resource-constrained environments. While techniques such as chain-of-thought (CoT) distillation have displayed promise in distilling LLMs into small language models (SLMs), there is a risk that distilled SLMs may still inherit flawed reasoning and hallucinations from LLMs. To address these issues, we propose a twofold methodology: First, we introduce a novel method for distilling the self-evaluation capability from LLMs into SLMs, aiming to mitigate the adverse effects of flawed reasoning and hallucinations inherited from LLMs. Second, we advocate for distilling more comprehensive thinking by incorporating multiple distinct CoTs and self-evaluation outputs, to ensure a more thorough and robust knowledge transfer into SLMs. Experiments on three NLP benchmarks demonstrate that our method significantly improves the performance of distilled SLMs, offering a new perspective for developing more effective and efficient SLMs in resource-constrained environments.

2023

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TACR: A Table Alignment-based Cell Selection Method for HybridQA
Jian Wu | Yicheng Xu | Yan Gao | Jian-Guang Lou | Börje Karlsson | Manabu Okumura
Findings of the Association for Computational Linguistics: ACL 2023

Hybrid Question-Answering (HQA), which targets reasoning over tables and passages linked from table cells, has witnessed significant research in recent years. A common challenge in HQA and other passage-table QA datasets is that it is generally unrealistic to iterate over all table rows, columns, and linked passages to retrieve evidence. Such a challenge made it difficult for previous studies to show their reasoning ability in retrieving answers. To bridge this gap, we propose a novel Table-alignment-based Cell-selection and Reasoning model (TACR) for hybrid text and table QA, evaluated on the HybridQA and WikiTableQuestions datasets. In evidence retrieval, we design a table-question-alignment enhanced cell-selection method to retrieve fine-grained evidence. In answer reasoning, we incorporate a QA module that treats the row containing selected cells as context. Experimental results over the HybridQA and WikiTableQuestions (WTQ) datasets show that TACR achieves state-of-the-art results on cell selection and outperforms fine-grained evidence retrieval baselines on HybridQA, while achieving competitive performance on WTQ. We also conducted a detailed analysis to demonstrate that being able to align questions to tables in the cell-selection stage can result in important gains from experiments of over 90% table row and column selection accuracy, meanwhile also improving output explainability.

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Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification
Jiahuan Yan | Haojun Gao | Zhang Kai | Weize Liu | Danny Chen | Jian Wu | Jintai Chen
Findings of the Association for Computational Linguistics: EMNLP 2023

Deep learning approaches exhibit promising performances on various text tasks. However, they are still struggling on medical text classification since samples are often extremely imbalanced and scarce. Different from existing mainstream approaches that focus on supplementary semantics with external medical information, this paper aims to rethink the data challenges in medical texts and present a novel framework-agnostic algorithm called Text2Tree that only utilizes internal label hierarchy in training deep learning models. We embed the ICD code tree structure of labels into cascade attention modules for learning hierarchy-aware label representations. Two new learning schemes, Similarity Surrogate Learning (SSL) and Dissimilarity Mixup Learning (DML), are devised to boost text classification by reusing and distinguishing samples of other labels following the label representation hierarchy, respectively. Experiments on authoritative public datasets and real-world medical records show that our approach stably achieves superior performances over classical and advanced imbalanced classification methods. Our code is available at https://github.com/jyansir/Text2Tree.

2022

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DialMed: A Dataset for Dialogue-based Medication Recommendation
Zhenfeng He | Yuqiang Han | Zhenqiu Ouyang | Wei Gao | Hongxu Chen | Guandong Xu | Jian Wu
Proceedings of the 29th International Conference on Computational Linguistics

Medication recommendation is a crucial task for intelligent healthcare systems. Previous studies mainly recommend medications with electronic health records (EHRs). However, some details of interactions between doctors and patients may be ignored or omitted in EHRs, which are essential for automatic medication recommendation. Therefore, we make the first attempt to recommend medications with the conversations between doctors and patients. In this work, we construct DIALMED, the first high-quality dataset for medical dialogue-based medication recommendation task. It contains 11, 996 medical dialogues related to 16 common diseases from 3 departments and 70 corresponding common medications. Furthermore, we propose a Dialogue structure and Disease knowledge aware Network (DDN), where a QA Dialogue Graph mechanism is designed to model the dialogue structure and the knowledge graph is used to introduce external disease knowledge. The extensive experimental results demonstrate that the proposed method is a promising solution to recommend medications with medical dialogues. The dataset and code are available at https://github.com/f-window/DialMed.

2021

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Extractive Research Slide Generation Using Windowed Labeling Ranking
Athar Sefid | Prasenjit Mitra | Jian Wu | C Lee Giles
Proceedings of the Second Workshop on Scholarly Document Processing

Presentation slides generated from original research papers provide an efficient form to present research innovations. Manually generating presentation slides is labor-intensive. We propose a method to automatically generates slides for scientific articles based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites. The sentence labeling module of our method is based on SummaRuNNer, a neural sequence model for extractive summarization. Instead of ranking sentences based on semantic similarities in the whole document, our algorithm measures the importance and novelty of sentences by combining semantic and lexical features within a sentence window. Our method outperforms several baseline methods including SummaRuNNer by a significant margin in terms of ROUGE score.

2020

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Acknowledgement Entity Recognition in CORD-19 Papers
Jian Wu | Pei Wang | Xin Wei | Sarah Rajtmajer | C. Lee Giles | Christopher Griffin
Proceedings of the First Workshop on Scholarly Document Processing

Acknowledgements are ubiquitous in scholarly papers. Existing acknowledgement entity recognition methods assume all named entities are acknowledged. Here, we examine the nuances between acknowledged and named entities by analyzing sentence structure. We develop an acknowledgement extraction system, AckExtract based on open-source text mining software and evaluate our method using manually labeled data. AckExtract uses the PDF of a scholarly paper as input and outputs acknowledgement entities. Results show an overall performance of F_1=0.92. We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by AckExtract including persons and organizations and find that only up to 50–60% of named entities are actually acknowledged. We further analyze chronological trends of acknowledgement entities in CORD-19 papers. All codes and labeled data are publicly available at https://github.com/lamps-lab/ackextract.

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SmartCiteCon: Implicit Citation Context Extraction from Academic Literature Using Supervised Learning
Chenrui Guo | Haoran Cui | Li Zhang | Jiamin Wang | Wei Lu | Jian Wu
Proceedings of the 8th International Workshop on Mining Scientific Publications

We introduce SmartCiteCon (SCC), a Java API for extracting both explicit and implicit citation context from academic literature in English. The tool is built on a Support Vector Machine (SVM) model trained on a set of 7,058 manually annotated citation context sentences, curated from 34,000 papers from the ACL Anthology. The model with 19 features achieves F1=85.6%. SCC supports PDF, XML, and JSON files out-of-box, provided that they are conformed to certain schemas. The API supports single document processing and batch processing in parallel. It takes about 12–45 seconds on average depending on the format to process a document on a dedicated server with 6 multithreaded cores. Using SCC, we extracted 11.8 million citation context sentences from ~33.3k PMC papers in the CORD-19 dataset, released on June 13, 2020. We will provide continuous supplementary data contribution to the CORD-19 and other datasets. The source code is released at https://gitee.com/irlab/SmartCiteCon.

2015

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Tibetan Unknown Word Identification from News Corpora for Supporting Lexicon-based Tibetan Word Segmentation
Minghua Nuo | Huidan Liu | Congjun Long | Jian Wu
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Zipf’s Law and Statistical Data on Modern Tibetan
Huidan Liu | Minghua Nuo | Jian Wu
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2012

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Building Large Scale Text Corpus for Tibetan Natural Language Processing by Extracting Text from Web Pages
Huidan Liu | Minghua Nuo | Jian Wu | Yeping He
Proceedings of the 10th Workshop on Asian Language Resources

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Tibetan Base Noun Phrase Identification Framework Based on Chinese-Tibetan Sentence Aligned Corpus
Ming Hua Nuo | Hui Dan Liu | Wei Na Zhao | Long Long Ma | Jian Wu | Zhi Ming Ding
Proceedings of COLING 2012

2011

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Tibetan Word Segmentation as Syllable Tagging Using Conditional Random Field
Huidan Liu | Minghua Nuo | Longlong Ma | Jian Wu | Yeping He
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

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Compression Methods by Code Mapping and Code Dividing for Chinese Dictionary Stored in a Double-Array Trie
Huidan Liu | Minghua Nuo | Longlong Ma | Jian Wu | Yeping He
Proceedings of 5th International Joint Conference on Natural Language Processing

2010

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Tibetan Number Identification Based on Classification of Number Components in Tibetan Word Segmentation
Huidan Liu | Weina Zhao | Minghua Nuo | Li Jiang | Jian Wu | Yeping He
Coling 2010: Posters