Tengfei Ma


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Knowledge Graph Compression Enhances Diverse Commonsense Generation
EunJeong Hwang | Veronika Thost | Vered Shwartz | Tengfei Ma
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Generating commonsense explanations requires reasoning about commonsense knowledge beyond what is explicitly mentioned in the context. Existing models use commonsense knowledge graphs such as ConceptNet to extract a subgraph of relevant knowledge pertaining to concepts in the input. However, due to the large coverage and, consequently, vast scale of ConceptNet, the extracted subgraphs may contain loosely related, redundant and irrelevant information, which can introduce noise into the model. We propose to address this by applying a differentiable graph compression algorithm that focuses on the relevant knowledge for the task. The compressed subgraphs yield considerably more diverse outputs when incorporated into models for the tasks of generating commonsense and abductive explanations. Moreover, our model achieves better quality-diversity tradeoff than a large language model with 100 times the number of parameters. Our generic approach can be applied to additional NLP tasks that can benefit from incorporating external knowledge.

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CP-BCS: Binary Code Summarization Guided by Control Flow Graph and Pseudo Code
Tong Ye | Lingfei Wu | Tengfei Ma | Xuhong Zhang | Yangkai Du | Peiyu Liu | Shouling Ji | Wenhai Wang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Automatically generating function summaries for binaries is an extremely valuable but challenging task, since it involves translating the execution behavior and semantics of the low-level language (assembly code) into human-readable natural language. However, most current works on understanding assembly code are oriented towards generating function names, which involve numerous abbreviations that make them still confusing. To bridge this gap, we focus on generating complete summaries for binary functions, especially for stripped binary (no symbol table and debug information in reality). To fully exploit the semantics of assembly code, we present a control flow graph and pseudo code guided binary code summarization framework called CP-BCS. CP-BCS utilizes a bidirectional instruction-level control flow graph and pseudo code that incorporates expert knowledge to learn the comprehensive binary function execution behavior and logic semantics. We evaluate CP-BCS on 3 different binary optimization levels (O1, O2, and O3) for 3 different computer architectures (X86, X64, and ARM). The evaluation results demonstrate CP-BCS is superior and significantly improves the efficiency of reverse engineering.


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A Study of the Attention Abnormality in Trojaned BERTs
Weimin Lyu | Songzhu Zheng | Tengfei Ma | Chao Chen
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Trojan attacks raise serious security concerns. In this paper, we investigate the underlying mechanism of Trojaned BERT models. We observe the attention focus drifting behavior of Trojaned models, i.e., when encountering an poisoned input, the trigger token hijacks the attention focus regardless of the context. We provide a thorough qualitative and quantitative analysis of this phenomenon, revealing insights into the Trojan mechanism. Based on the observation, we propose an attention-based Trojan detector to distinguish Trojaned models from clean ones. To the best of our knowledge, we are the first to analyze the Trojan mechanism and develop a Trojan detector based on the transformer’s attention.


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Constructing contrastive samples via summarization for text classification with limited annotations
Yangkai Du | Tengfei Ma | Lingfei Wu | Fangli Xu | Xuhong Zhang | Bo Long | Shouling Ji
Findings of the Association for Computational Linguistics: EMNLP 2021

Contrastive Learning has emerged as a powerful representation learning method and facilitates various downstream tasks especially when supervised data is limited. How to construct efficient contrastive samples through data augmentation is key to its success. Unlike vision tasks, the data augmentation method for contrastive learning has not been investigated sufficiently in language tasks. In this paper, we propose a novel approach to construct contrastive samples for language tasks using text summarization. We use these samples for supervised contrastive learning to gain better text representations which greatly benefit text classification tasks with limited annotations. To further improve the method, we mix up samples from different classes and add an extra regularization, named Mixsum, in addition to the cross-entropy-loss. Experiments on real-world text classification datasets (Amazon-5, Yelp-5, AG News, and IMDb) demonstrate the effectiveness of the proposed contrastive learning framework with summarization-based data augmentation and Mixsum regularization.

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Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport
Manling Li | Tengfei Ma | Mo Yu | Lingfei Wu | Tian Gao | Heng Ji | Kathleen McKeown
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged. Previous methods generally generate summaries separately for each date after they determine the key dates of events. These methods overlook the events’ intra-structures (arguments) and inter-structures (event-event connections). Following a different route, we propose to represent the news articles as an event-graph, thus the summarization becomes compressing the whole graph to its salient sub-graph. The key hypothesis is that the events connected through shared arguments and temporal order depict the skeleton of a timeline, containing events that are semantically related, temporally coherent and structurally salient in the global event graph. A time-aware optimal transport distance is then introduced for learning the compression model in an unsupervised manner. We show that our approach significantly improves on the state of the art on three real-world datasets, including two public standard benchmarks and our newly collected Timeline100 dataset.


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Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
Hanlu Wu | Tengfei Ma | Lingfei Wu | Tariro Manyumwa | Shouling Ji
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Evaluation of a document summarization system has been a critical factor to impact the success of the summarization task. Previous approaches, such as ROUGE, mainly consider the informativeness of the assessed summary and require human-generated references for each test summary. In this work, we propose to evaluate the summary qualities without reference summaries by unsupervised contrastive learning. Specifically, we design a new metric which covers both linguistic qualities and semantic informativeness based on BERT. To learn the metric, for each summary, we construct different types of negative samples with respect to different aspects of the summary qualities, and train our model with a ranking loss. Experiments on Newsroom and CNN/Daily Mail demonstrate that our new evaluation method outperforms other metrics even without reference summaries. Furthermore, we show that our method is general and transferable across datasets.


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Pre-Training BERT on Domain Resources for Short Answer Grading
Chul Sung | Tejas Dhamecha | Swarnadeep Saha | Tengfei Ma | Vinay Reddy | Rishi Arora
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Pre-trained BERT contextualized representations have achieved state-of-the-art results on multiple downstream NLP tasks by fine-tuning with task-specific data. While there has been a lot of focus on task-specific fine-tuning, there has been limited work on improving the pre-trained representations. In this paper, we explore ways of improving the pre-trained contextual representations for the task of automatic short answer grading, a critical component of intelligent tutoring systems. We show that the pre-trained BERT model can be improved by augmenting data from the domain-specific resources like textbooks. We also present a new approach to use labeled short answering grading data for further enhancement of the language model. Empirical evaluation on multi-domain datasets shows that task-specific fine-tuning on the enhanced pre-trained language model achieves superior performance for short answer grading.

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Graph Enhanced Cross-Domain Text-to-SQL Generation
Siyu Huo | Tengfei Ma | Jie Chen | Maria Chang | Lingfei Wu | Michael Witbrock
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)

Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations. Existing deep learning approaches for semantic parsing have shown promise on a variety of benchmark data sets, particularly on text-to-SQL parsing. However, most text-to-SQL parsers do not generalize to unseen data sets in different domains. In this paper, we propose a new cross-domain learning scheme to perform text-to-SQL translation and demonstrate its use on Spider, a large-scale cross-domain text-to-SQL data set. We improve upon a state-of-the-art Spider model, SyntaxSQLNet, by constructing a graph of column names for all databases and using graph neural networks to compute their embeddings. The resulting embeddings offer better cross-domain representations and SQL queries, as evidenced by substantial improvement on the Spider data set compared to SyntaxSQLNet.


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Multilingual Training of Crosslingual Word Embeddings
Long Duong | Hiroshi Kanayama | Tengfei Ma | Steven Bird | Trevor Cohn
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Crosslingual word embeddings represent lexical items from different languages using the same vector space, enabling crosslingual transfer. Most prior work constructs embeddings for a pair of languages, with English on one side. We investigate methods for building high quality crosslingual word embeddings for many languages in a unified vector space. In this way, we can exploit and combine strength of many languages. We obtained high performance on bilingual lexicon induction, monolingual similarity and crosslingual document classification tasks.


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Learning Crosslingual Word Embeddings without Bilingual Corpora
Long Duong | Hiroshi Kanayama | Tengfei Ma | Steven Bird | Trevor Cohn
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


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Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach
Tengfei Ma | Hiroshi Nakagawa
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing


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Named Entity Recognition in Chinese News Comments on the Web
Xiaojun Wan | Liang Zong | Xiaojiang Huang | Tengfei Ma | Houping Jia | Yuqian Wu | Jianguo Xiao
Proceedings of 5th International Joint Conference on Natural Language Processing


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Opinion Target Extraction in Chinese News Comments
Tengfei Ma | Xiaojun Wan
Coling 2010: Posters