Jie Yang


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ControversialQA: Exploring Controversy in Question Answering
Zhen Wang | Peide Zhu | Jie Yang
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Controversy is widespread online. Previous studies mainly define controversy based on vague assumptions of its relation to sentiment such as hate speech and offensive words. This paper introduces the first question-answering dataset that defines content controversy by user perception, i.e., votes from plenty of users. It contains nearly 10K questions, and each question has a best answer and a most controversial answer. Experimental results reveal that controversy detection in question answering is essential and challenging, and there is no strong correlation between controversy and sentiment tasks. We also show that controversial answers and most acceptable answers cannot be distinguished by retrieval-based QA models, which may cause controversy issues. With these insights, we believe ControversialQA can inspire future research on controversy in QA systems.


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[MASK] Insertion: a robust method for anti-adversarial attacks
Xinrong Hu | Ce Xu | Junlong Ma | Zijian Huang | Jie Yang | Yi Guo | Johan Barthelemy
Findings of the Association for Computational Linguistics: EACL 2023

Adversarial attack aims to perturb input sequences and mislead a trained model for false predictions. To enhance the model robustness, defensing methods are accordingly employed by either data augmentation (involving adversarial samples) or model enhancement (modifying the training loss and/or model architecture). In contrast to previous work, this paper revisits the masked language modeling (MLM) and presents a simple yet efficient algorithm against adversarial attacks, termed [MASK] insertion for defensing (MI4D). Specifically, MI4D simply inserts [MASK] tokens to input sequences during training and inference, maximizing the intersection of the new convex hull (MI4D creates) with the original one (the clean input forms). As neither additional adversarial samples nor the model modification is required, MI4D is as computationally efficient as traditional fine-tuning. Comprehensive experiments have been conducted using three benchmark datasets and four attacking methods. MI4D yields a significant improvement (on average) of the accuracy between 3.2 and 11.1 absolute points when compared with six state-of-the-art defensing baselines.

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Improving Machine Reading Comprehension through A Simple Masked-Training Scheme
Xun Yao | Junlong Ma | Xinrong Hu | Jie Yang | Yuan-Fang Li
Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)

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Red Teaming for Large Language Models At Scale: Tackling Hallucinations on Mathematics Tasks
Aleksander Buszydlik | Karol Dobiczek | Michał Teodor Okoń | Konrad Skublicki | Philip Lippmann | Jie Yang
Proceedings of the ART of Safety: Workshop on Adversarial testing and Red-Teaming for generative AI

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Student-Teacher Prompting for Red Teaming to Improve Guardrails
Rodrigo Revilla Llaca | Victoria Leskoschek | Vitor Costa Paiva | Cătălin Lupău | Philip Lippmann | Jie Yang
Proceedings of the ART of Safety: Workshop on Adversarial testing and Red-Teaming for generative AI

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YATO: Yet Another deep learning based Text analysis Open toolkit
Zeqiang Wang | Yile Wang | Jiageng Wu | Zhiyang Teng | Jie Yang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We introduce YATO, an open-source, easy-to-use toolkit for text analysis with deep learning. Different from existing heavily engineered toolkits and platforms, YATO is lightweight and user-friendly for researchers from cross-disciplinary areas. Designed in a hierarchical structure, YATO supports free combinations of three types of widely used features including 1) traditional neural networks (CNN, RNN, etc.); 2) pre-trained language models (BERT, RoBERTa, ELECTRA, etc.); and 3) user-customized neural features via a simple configurable file. Benefiting from the advantages of flexibility and ease of use, YATO can facilitate fast reproduction and refinement of state-of-the-art NLP models, and promote the cross-disciplinary applications of NLP techniques. The code, examples, and documentation are publicly available at https://github.com/jiesutd/YATO. A demo video is also available at https://www.youtube.com/playlist?list=PLJ0mhzMcRuDUlTkzBfAftOqiJRxYTTjXH.


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Answer Quality Aware Aggregation for Extractive QA Crowdsourcing
Peide Zhu | Zhen Wang | Claudia Hauff | Jie Yang | Avishek Anand
Findings of the Association for Computational Linguistics: EMNLP 2022

Quality control is essential for creating extractive question answering (EQA) datasets via crowdsourcing. Aggregation across answers, i.e. word spans within passages annotated, by different crowd workers is one major focus for ensuring its quality. However, crowd workers cannot reach a consensus on a considerable portion of questions. We introduce a simple yet effective answer aggregation method that takes into account the relations among the answer, question, and context passage. We evaluate answer quality from both the view of question answering model to determine how confident the QA model is about each answer and the view of the answer verification model to determine whether the answer is correct. Then we compute aggregation scores with each answer’s quality and its contextual embedding produced by pre-trained language models. The experiments on a large real crowdsourced EQA dataset show that our framework outperforms baselines by around 16% on precision and effectively conduct answer aggregation for extractive QA task.

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N24News: A New Dataset for Multimodal News Classification
Zhen Wang | Xu Shan | Xiangxie Zhang | Jie Yang
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Current news datasets merely focus on text features on the news and rarely leverage the feature of images, excluding numerous essential features for news classification. In this paper, we propose a new dataset, N24News, which is generated from New York Times with 24 categories and contains both text and image information in each news. We use a multitask multimodal method and the experimental results show multimodal news classification performs better than text-only news classification. Depending on the length of the text, the classification accuracy can be increased by up to 8.11%. Our research reveals the relationship between the performance of a multimodal classifier and its sub-classifiers, and also the possible improvements when applying multimodal in news classification. N24News is shown to have great potential to prompt the multimodal news studies.

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DDI-MuG: Multi-aspect Graphs for Drug-Drug Interaction Extraction
Jie Yang | Yihao Ding | Siqu Long | Josiah Poon | Soyeon Caren Han
Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)

Drug-drug interaction (DDI) may leads to adverse reactions in patients, thus it is important to extract such knowledge from biomedical texts. However, previously proposed approaches typically focus on capturing sentence-aspect information while ignoring valuable knowledge concerning the whole corpus. In this paper, we propose a Multi-aspect Graph-based DDI extraction model, named DDI-MuG. We first employ a bio-specific pre-trained language model to obtain the token contextualized representations. Then we use two graphs to get syntactic information from input instance and word co-occurrence information within the entire corpus, respectively. Finally, we combine the representations of drug entities and verb tokens for the final classification. It is encouraging to see that the proposed model outperforms all baseline models on two benchmark datasets. To the best of our knowledge, this is the first model that explores multi-aspect graphs to the DDI extraction task, and we hope it can establish a foundation for more robust multi-aspect works in the future.


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aiai at the FinSBD-2 Task: Sentence, list and Item Boundary Detection and Items classification of Financial Texts Using Data Augmentation and Attention
Ke Tian | Hua Chen | Jie Yang
Proceedings of the Second Workshop on Financial Technology and Natural Language Processing


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Subword Encoding in Lattice LSTM for Chinese Word Segmentation
Jie Yang | Yue Zhang | Shuailong Liang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We investigate subword information for Chinese word segmentation, by integrating sub word embeddings trained using byte-pair encoding into a Lattice LSTM (LaLSTM) network over a character sequence. Experiments on standard benchmark show that subword information brings significant gains over strong character-based segmentation models. To our knowledge, this is the first research on the effectiveness of subwords on neural word segmentation.

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Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content
Sepideh Mesbah | Jie Yang | Robert-Jan Sips | Manuel Valle Torre | Christoph Lofi | Alessandro Bozzon | Geert-Jan Houben
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Social media provides a timely yet challenging data source for adverse drug reaction (ADR) detection. Existing dictionary-based, semi-supervised learning approaches are intrinsically limited by the coverage and maintainability of laymen health vocabularies. In this paper, we introduce a data augmentation approach that leverages variational autoencoders to learn high-quality data distributions from a large unlabeled dataset, and subsequently, to automatically generate a large labeled training set from a small set of labeled samples. This allows for efficient social-media ADR detection with low training and re-training costs to adapt to the changes and emergence of informal medical laymen terms. An extensive evaluation performed on Twitter and Reddit data shows that our approach matches the performance of fully-supervised approaches while requiring only 25% of training data.


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Chinese NER Using Lattice LSTM
Yue Zhang | Jie Yang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with character-based methods, our model explicitly leverages word and word sequence information. Compared with word-based methods, lattice LSTM does not suffer from segmentation errors. Gated recurrent cells allow our model to choose the most relevant characters and words from a sentence for better NER results. Experiments on various datasets show that lattice LSTM outperforms both word-based and character-based LSTM baselines, achieving the best results.

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YEDDA: A Lightweight Collaborative Text Span Annotation Tool
Jie Yang | Yue Zhang | Linwei Li | Xingxuan Li
Proceedings of ACL 2018, System Demonstrations

In this paper, we introduce Yedda, a lightweight but efficient and comprehensive open-source tool for text span annotation. Yedda provides a systematic solution for text span annotation, ranging from collaborative user annotation to administrator evaluation and analysis. It overcomes the low efficiency of traditional text annotation tools by annotating entities through both command line and shortcut keys, which are configurable with custom labels. Yedda also gives intelligent recommendations by learning the up-to-date annotated text. An administrator client is developed to evaluate annotation quality of multiple annotators and generate detailed comparison report for each annotator pair. Experiments show that the proposed system can reduce the annotation time by half compared with existing annotation tools. And the annotation time can be further compressed by 16.47% through intelligent recommendation.

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NCRF++: An Open-source Neural Sequence Labeling Toolkit
Jie Yang | Yue Zhang
Proceedings of ACL 2018, System Demonstrations

This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer. It provides users with an inference for building the custom model structure through configuration file with flexible neural feature design and utilization. Built on PyTorch http://pytorch.org/, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. It also includes the implementations of most state-of-the-art neural sequence labeling models such as LSTM-CRF, facilitating reproducing and refinement on those methods.

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Design Challenges and Misconceptions in Neural Sequence Labeling
Jie Yang | Shuailong Liang | Yue Zhang
Proceedings of the 27th International Conference on Computational Linguistics

We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.


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Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring
Fei Dong | Yue Zhang | Jie Yang
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Neural network models have recently been applied to the task of automatic essay scoring, giving promising results. Existing work used recurrent neural networks and convolutional neural networks to model input essays, giving grades based on a single vector representation of the essay. On the other hand, the relative advantages of RNNs and CNNs have not been compared. In addition, different parts of the essay can contribute differently for scoring, which is not captured by existing models. We address these issues by building a hierarchical sentence-document model to represent essays, using the attention mechanism to automatically decide the relative weights of words and sentences. Results show that our model outperforms the previous state-of-the-art methods, demonstrating the effectiveness of the attention mechanism.

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Neural Reranking for Named Entity Recognition
Jie Yang | Yue Zhang | Fei Dong
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

We propose a neural reranking system for named entity recognition (NER), leverages recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as Barack Obama, into their entity types, such as PER. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, “PER was born in LOC” can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.

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Neural Word Segmentation with Rich Pretraining
Jie Yang | Yue Zhang | Fei Dong
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.

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Universal Dependencies Parsing for Colloquial Singaporean English
Hongmin Wang | Yue Zhang | GuangYong Leonard Chan | Jie Yang | Hai Leong Chieu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.


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LibN3L:A Lightweight Package for Neural NLP
Meishan Zhang | Jie Yang | Zhiyang Teng | Yue Zhang
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present a light-weight machine learning tool for NLP research. The package supports operations on both discrete and dense vectors, facilitating implementation of linear models as well as neural models. It provides several basic layers which mainly aims for single-layer linear and non-linear transformations. By using these layers, we can conveniently implement linear models and simple neural models. Besides, this package also integrates several complex layers by composing those basic layers, such as RNN, Attention Pooling, LSTM and gated RNN. Those complex layers can be used to implement deep neural models directly.

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Unsupervised Multi-Author Document Decomposition Based on Hidden Markov Model
Khaled Aldebei | Xiangjian He | Wenjing Jia | Jie Yang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Unsupervised Decomposition of a Multi-Author Document Based on Naive-Bayesian Model
Khaled Aldebei | Xiangjian He | Jie Yang
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)


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Enhancing image-based Arabic document translation using noisy channel correction model
Yi Chang | Ying Zhang | Stephan Vogel | Jie Yang
Proceedings of Machine Translation Summit XI: Papers


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Competitive Evaluation of Commercially Available Speech Recognizers in Multiple Languages
Susanne Burger | Zachary A. Sloane | Jie Yang
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Recent improvements in speech recognition technology have resulted in products that can now demonstrate commercial value in a variety of applications. Many vendors are marketing products which combine ASR applications including continuous dictation, command-and-control interfaces, and transcription of recorded speech at an accuracy of 98%. In this study, we measured the accuracy of certain commercially available desktop speech recognition engines in multiple languages. Using word error rate as a benchmark, this work compares recognition accuracy across eight languages and the products of three manufacturers. Results show that two systems performed almost the same while a third system recognized at lower accuracy, although none of the systems reached the claimed accuracy. Read speech was recognized better than spontaneous speech. The systems for US-English, Japanese and Spanish showed higher accuracy than the systems for UK-English, German, French and Chinese.


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Towards Automatic Sign Translation
Jie Yang | Jiang Gao | Ying Zhang | Alex Waibel
Proceedings of the First International Conference on Human Language Technology Research