Papers by Lingfei Wu
CP-BCS: Binary Code Summarization Guided by Control Flow Graph and Pseudo Code (2023.emnlp-main)
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| Challenge: | Current work on understanding assembly code is oriented towards generating function names, which involve numerous abbreviations that make them confusing. |
| Approach: | They propose a control flow graph and pseudo code guided binary code summarization framework to learn the comprehensive binary function execution behavior and logic semantics. |
| Outcome: | The proposed framework improves the efficiency of reverse engineering on 3 different binary optimization levels for 3 different computer architectures. |
Crossing Variational Autoencoders for Answer Retrieval (2020.acl-main)
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| Challenge: | Existing methods learned semantic representations with dual encoders or dual variational auto-encoders failed to capture the aligned semantics between question and answer. |
| Approach: | They propose to use two variational auto-encoders to generate questions with aligned answers and generating answers with align questions. |
| Outcome: | The proposed method outperforms the state-of-the-art answer retrieval method on SQuAD. |
Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward (2020.acl-main)
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| Challenge: | Abstractive summarization models for document encoders suffer from fabricated content and are often near-extractive. |
| Approach: | They propose a framework for abstractive summarization with Graph-Augmentation and semantic-driven RewarD that uses a sequential document encoder and a graph-structured encoder to maintain the global context and local characteristics of entities. |
| Outcome: | The proposed framework produces higher ROUGE scores than a variant without knowledge graph on New York Times and CNN/Daily Mail datasets. |
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks (2021.findings-emnlp)
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| Challenge: | In computational notebooks, one documentation in a markdown cell often corresponds to multiple code cells, and these code cells have an inherent structure. |
| Approach: | They propose a new task of code documentation generation for computational notebooks that uses hierarchical attention mechanism to consider code cells and code tokens information when generating documentation. |
| Outcome: | The proposed model outperforms baseline models on a corpus constructed from well-documented Kaggle notebooks. |
Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search (2022.emnlp-industry)
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| Challenge: | Existing approaches to e-commerce relevance matching ignore bipartite graphs in logs . experimental results show that proposed method improves human relevance judgment . |
| Approach: | They propose an efficient knowledge distillation framework for e-commerce relevance matching to exploit the advantages of Transformer-style and classical relevance matching models. |
| Outcome: | The proposed method significantly improves human relevance judgment on large-scale real-world data. |
SQL-to-Text Generation with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing approaches to generate SQL-to-text using seq2seq models do not capture graph-structured information in SQL query. |
| Approach: | They propose a graph-to-sequence model to encode global structure information into node embeddings. |
| Outcome: | The proposed model outperforms the Seq2Seq and Tree2Sq baselines on the WikiSQL and Stackoverflow datasets. |
A Multi-Perspective Architecture for Semantic Code Search (2020.acl-main)
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| Challenge: | Existing models do not model interactions between code and description until the final step when their global similarity is calculated. |
| Approach: | They propose a multi-perspective cross-lingual neural framework for code–text matching that captures both global and local similarities. |
| Outcome: | The proposed model performs better on the CoNaLa dataset than previous approaches that map code and text to a single joint embedding space. |
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph (2022.aacl-main)
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| Challenge: | Existing knowledge graph question answering methods only search for the answer in a large knowledge graph. |
| Approach: | They propose to partition retrieved knowledge subgraphs into smaller sub-KSGs and then use a graph-augmented learning to rank method to select the top-ranked sub-kSGs. |
| Outcome: | The proposed method can capture global interactions in question and subgraphs and local interactions on the full KSG and top-ranked sub-KSGs respectively. |
SkillQG: Learning to Generate Question for Reading Comprehension Assessment (2023.findings-acl)
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| Challenge: | Existing question generation systems focus on the literal nature of questions and rarely consider comprehension types of the generated questions. |
| Approach: | They propose a question generation framework with controllable comprehension types for machine reading comprehension models. |
| Outcome: | Empirical results show that SkillQG outperforms baselines in quality, relevance, and skill-controllability while showing a performance boost in downstream question answering task. |
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus (N19-1)
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| Challenge: | Existing methods for text style transfer have demonstrated considerable success, but a parallel corpus may not always be available for a transfer task. |
| Approach: | They propose a text style transfer model that uses an attention-based encoder-decoder to transfer a sentence from the source style to the target style. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two different style transfer tasks. |
Deep Learning on Graphs for Natural Language Processing (2021.naacl-tutorials)
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| Challenge: | Graph Neural Networks (GNNs) are powerful tools for non-Euclidean data modeling and are used in many graph-related NLP tasks. |
| Approach: | This tutorial will cover applying deep learning on graph techniques to NLP using Graph Neural Networks (GNNs) Graph4NLP is the first library for researchers and practitioners for easy use of GNNs for various NLP tasks. |
| Outcome: | This tutorial will cover the latest developments in deep learning on graph techniques and their applications in various NLP tasks. |
A Joint Neural Model for Information Extraction with Global Features (2020.acl-main)
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| Challenge: | Existing joint neural models for Information Extraction use local task-specific classifiers to predict labels for individual instances. |
| Approach: | They propose a joint neural framework that extracts the optimal IE result as a graph from an input sentence. |
| Outcome: | The proposed model achieves new state-of-the-art on all subtasks and does not use any language-specific feature. |
Adversarial Attack against Cross-lingual Knowledge Graph Alignment (2021.emnlp-main)
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Zeru Zhang, Zijie Zhang, Yang Zhou, Lingfei Wu, Sixing Wu, Xiaoying Han, Dejing Dou, Tianshi Che, Da Yan
| Challenge: | Existing studies on cross-lingual entity alignment under adversarial attacks have not been conducted. |
| Approach: | They propose to use adversarial attack techniques to perturb cross-lingual entity alignment under adversarials. |
| Outcome: | The proposed model hides the attacked entities in dense regions in two KGs, and reduces the gradient vanishing issues in the process of adversarial attacks for further improving the attack effectiveness. |
Technical Question Answering across Tasks and Domains (2021.naacl-industry)
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| Challenge: | Existing methods for technical QA have a limited data size and question and answer overlaps . |
| Approach: | They propose a framework of deep transfer learning to address technical QA across tasks and domains using document retrieval and reading comprehension tasks. |
| Outcome: | The proposed framework performs better than state-of-the-art methods on the TechQA task. |
Word Mover’s Embedding: From Word2Vec to Document Embedding (D18-1)
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Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
| Challenge: | Recent work has demonstrated that Word Mover’s Distance (WMD) that aligns semantically similar words yields unprecedented KNN classification accuracy. |
| Approach: | They propose a Word Mover’s Distance (WMD) method that aligns semantically similar words to generate unsupervised sentences or documents embeddings. |
| Outcome: | The proposed method consistently outperforms state-of-the-art techniques on 9 benchmark text classification datasets and 22 textual similarity tasks. |
Automatic Scene-based Topic Channel Construction System for E-Commerce (2022.emnlp-industry)
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| Challenge: | Recent scene marketing has proved effective for offline shopping. |
| Approach: | They propose a novel product form, scene-based topic channel, which consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words. |
| Outcome: | The proposed system can be automated and tested on a real-world e-commerce recommendation platform. |
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases (N19-1)
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| Challenge: | Existing methods for knowledge base question answering ignore subtle inter-relationships between the question and the KB. |
| Approach: | They propose to model the two-way flow of interactions between questions and KBs using a bidirectional attentive memory network. |
| Outcome: | The proposed method outperforms existing methods on the WebQuestions benchmark and offers better interpretability compared to baselines. |
Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization (2024.findings-naacl)
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| Challenge: | Existing methods to generate source code summaries are coarse-grained and noise-filled . however, they do not capture contextual code semantics and are often outdated in continuous software iteration. |
| Approach: | They propose a fine-grained Token-level retrieval-augmented mechanism on the decoder side to enhance performance of neural models. |
| Outcome: | The proposed method produces more low-frequency tokens and is interpretable. |
Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport (2021.emnlp-main)
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| Challenge: | Existing methods for timeline summarization ignore the events’ intra-structures and inter-structure connections. |
| Approach: | They propose to represent news articles as an event-graph, thus compressing the whole graph to its salient sub-graph. |
| Outcome: | The proposed method significantly improves on the state-of-the-art on three real-world datasets, including two public benchmarks and a Timeline100 dataset. |
Constructing contrastive samples via summarization for text classification with limited annotations (2021.findings-emnlp)
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| Challenge: | Various contrastive learning methods have been developed and lead to state-of-the-art performance in many computer vision tasks. |
| Approach: | They propose a method to construct efficient contrastive samples using text summarization to gain better representations of text classification tasks with limited annotations. |
| Outcome: | The proposed framework gains better representations on text classification tasks with limited annotations and is compared with existing methods on real-world text classification datasets. |
Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning (2020.emnlp-main)
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| Challenge: | Existing methods for document summarization consider the informativeness of the assessed summary and require human-generated references for each test summary. |
| Approach: | They propose to evaluate summary qualities without reference summaries by unsupervised contrastive learning. |
| Outcome: | The proposed method outperforms other evaluation metrics even without reference summaries. |
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
| Approach: | They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form . |
| Outcome: | The proposed model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. |
Graph Enhanced Cross-Domain Text-to-SQL Generation (D19-53)
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| Challenge: | Existing deep learning approaches for semantic parsing do not generalize to unseen data sets . existing benchmarks have shown text-to-SQL parsers do not generally perform well to unsen SQL queries. |
| Approach: | They propose a new cross-domain learning scheme to perform text-to-SQL translation . they demonstrate its use on a large-scale cross- domain text- to-Sql data set Spider . |
| Outcome: | The proposed learning scheme improves on a large-scale text-to-SQL data set. |
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020.findings-emnlp)
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| Challenge: | Graph2Tree model encodes graph-structured input and decodes tree-structures output. |
| Approach: | They propose a novel Graph-to-Tree Neural Network consisting of a graph encoder and a hierarchical tree decoder that encodes an augmented graph-structured input and decodes a tree-structure-output. |
| Outcome: | The proposed model outperforms or matches the performance of other state-of-the-art models on two problems, neural semantic parsing and math word problem. |
FAC2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are evaluated by overall performance on various text understanding and generation tasks. |
| Approach: | They propose a framework for Fine-grAined and Cognition-grounded LLMs’ Capability Evaluation that dissociates the language-related capabilities from cognition-related ones. |
| Outcome: | The proposed framework dissociates the language-related capabilities from cognition-related ones and breaks down the process of applying a specific capability into three sub-steps: recalling relevant knowledge, utilizing knowledge, and solving problems. |
A Technical Question Answering System with Transfer Learning (2020.emnlp-demos)
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| Challenge: | StackOverflow and AskUbuntu are popular open forum communities for technical question-answering, but it is expensive for human experts to provide timely and helpful responses. |
| Approach: | They develop a system that automatically responds to questions from a siamese ALBERT network based on previously answered questions . |
| Outcome: | The proposed system responds automatically to questions based on answers from previous users. |
Feeding What You Need by Understanding What You Learned (2022.acl-long)
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| Challenge: | Existing research on machine reading comprehension rely heavily on large-size models and corpus to improve performance. |
| Approach: | They propose a framework that assesses model capabilities in an explainable and multi-dimensional manner. |
| Outcome: | The proposed framework achieves an 11.22% / 8.71% improvement of EM / F1 on MRC tasks. |
From “Thinking” to “Justifying”: Aligning High-Stakes Explainability with Professional Communication Standards (2026.findings-acl)
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| Challenge: | Existing explanations for large language models (LLMs) need to be able to verify outputs. |
| Approach: | They propose a method that constrains output communication to present a conclusion before its structured justification. |
| Outcome: | The proposed approach achieves 83.9% accuracy and correctness over CoT. |
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance (2022.emnlp-main)
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| Challenge: | Existing metrics for assessing question generation fail to take into account the input context of generation. |
| Approach: | They propose a context-aware Relevance evaluation metric for Question Generation that takes into account the context of question generation into account. |
| Outcome: | The proposed metric achieves higher correlation with human judgments while being much more robust to adversarial samples. |