Weikang Wang


2022

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CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers
Yiming Ju | Weikang Wang | Yuanzhe Zhang | Suncong Zheng | Kang Liu | Jun Zhao
Proceedings of the 29th International Conference on Computational Linguistics

Forcing the answer of the Question Answering (QA) task to be a single text span might be restrictive since the answer can be multiple spans in the context. Moreover, we found that multi-span answers often appear with two characteristics when building the QA system for a real-world application. First, multi-span answers might be caused by users lacking domain knowledge and asking ambiguous questions, which makes the question need to be answered with conditions. Second, there might be hierarchical relations among multiple answer spans. Some recent span-extraction QA datasets include multi-span samples, but they only contain unconditional and parallel answers, which cannot be used to tackle this problem. To bridge the gap, we propose a new task: conditional question answering with hierarchical multi-span answers, where both the hierarchical relations and the conditions need to be extracted. Correspondingly, we introduce CMQA, a Conditional Multiple-span Chinese Question Answering dataset to study the new proposed task. The final release of CMQA consists of 7,861 QA pairs and 113,089 labels, where all samples contain multi-span answers, 50.4% of samples are conditional, and 56.6% of samples are hierarchical. CMQA can serve as a benchmark to study the new proposed task and help study building QA systems for real-world applications. The low performance of models drawn from related literature shows that the new proposed task is challenging for the community to solve.

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Multilingual Sentence Transformer as A Multilingual Word Aligner
Weikang Wang | Guanhua Chen | Hanqing Wang | Yue Han | Yun Chen
Findings of the Association for Computational Linguistics: EMNLP 2022

Multilingual pretrained language models (mPLMs) have shown their effectiveness in multilingual word alignment induction. However, these methods usually start from mBERT or XLM-R. In this paper, we investigate whether multilingual sentence Transformer LaBSE is a strong multilingual word aligner. This idea is non-trivial as LaBSE is trained to learn language-agnostic sentence-level embeddings, while the alignment extraction task requires the more fine-grained word-level embeddings to be language-agnostic. We demonstrate that the vanilla LaBSE outperforms other mPLMs currently used in the alignment task, and then propose to finetune LaBSE on parallel corpus for further improvement. Experiment results on seven language pairs show that our best aligner outperforms previous state-of-the-art models of all varieties. In addition, our aligner supports different language pairs in a single model, and even achieves new state-of-the-art on zero-shot language pairs that does not appear in the finetuning process.

2021

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BLCUFIGHT at SemEval-2021 Task 10: Novel Unsupervised Frameworks For Source-Free Domain Adaptation
Weikang Wang | Yi Wu | Yixiang Liu | Pengyuan Liu
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such assumption is rarely plausible in the real-world and may causes data-privacy issues, especially when the label of the source domain can be a sensitive attribute as an identifier. SemEval-2021 task 10 focuses on these issues. We participate in the task and propose novel frameworks based on self-training method. In our systems, two different frameworks are designed to solve text classification and sequence labeling. These approaches are tested to be effective which ranks the third among all system in subtask A, and ranks the first among all system in subtask B.

2020

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CDCPP:跨领域中文标点符号预测(CDCPP: Cross-Domain Chinese Punctuation Prediction)
Pengyuan Liu (刘鹏远) | Weikang Wang (王伟康) | Likun Qiu (邱立坤) | Bingjie Du (杜冰洁)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

标点符号对文本理解起很大作用。但目前,在中文文本特别是在社交媒体及问答领域文本中的标点符号使用存在非常多的错误或缺失的情况,这严重影响对其进行语义分析及机器翻译等各项自然语言处理的效果。当前对标点符号进行预测的相关研究多集中于英文对话的语音转写文本,缺少对社交媒体及问答领域文本进行标点预测的相关研究,也没有这些领域公开的数据集。本文首先提出跨领域中文标点符号预测任务,该任务是要利用标点符号基本规范正确的大规模新闻领域文本,建立标点符号预测模型,然后在标点符号标注不规范的社交媒体及问答领域,进行跨领域标点符号预测。随后构建了新闻、社交媒体及问答三个领域的相应数据集。最后还实现了一个基于BERT的标点符号预测基线模型,并在该数据集上进行了实验与分析。实验结果表明,直接利用新闻领域训练的模型,在社交媒体及问答领域上进行标点符号预测的性能均有所下降,在问答领域下降较小,在微博领域下降较大,超过20%,跨领域标点符号预测任务具有一定的挑战性。

2019

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Incremental Learning from Scratch for Task-Oriented Dialogue Systems
Weikang Wang | Jiajun Zhang | Qian Li | Mei-Yuh Hwang | Chengqing Zong | Zhifei Li
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Clarifying user needs is essential for existing task-oriented dialogue systems. However, in real-world applications, developers can never guarantee that all possible user demands are taken into account in the design phase. Consequently, existing systems will break down when encountering unconsidered user needs. To address this problem, we propose a novel incremental learning framework to design task-oriented dialogue systems, or for short Incremental Dialogue System (IDS), without pre-defining the exhaustive list of user needs. Specifically, we introduce an uncertainty estimation module to evaluate the confidence of giving correct responses. If there is high confidence, IDS will provide responses to users. Otherwise, humans will be involved in the dialogue process, and IDS can learn from human intervention through an online learning module. To evaluate our method, we propose a new dataset which simulates unanticipated user needs in the deployment stage. Experiments show that IDS is robust to unconsidered user actions, and can update itself online by smartly selecting only the most effective training data, and hence attains better performance with less annotation cost.

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Are You for Real? Detecting Identity Fraud via Dialogue Interactions
Weikang Wang | Jiajun Zhang | Qian Li | Chengqing Zong | Zhifei Li
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Identity fraud detection is of great importance in many real-world scenarios such as the financial industry. However, few studies addressed this problem before. In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules. One is the knowledge graph (KG) constructor organizing the personal information for each loan applicant. The other is structured dialogue management that can dynamically generate a series of questions based on the personal KG to ask the applicants and determine their identity states. We also present a heuristic user simulator based on problem analysis to evaluate our method. Experiments have shown that the trainable dialogue system can effectively detect fraudsters, and achieve higher recognition accuracy compared with rule-based systems. Furthermore, our learned dialogue strategies are interpretable and flexible, which can help promote real-world applications.

2018

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A Teacher-Student Framework for Maintainable Dialog Manager
Weikang Wang | Jiajun Zhang | Han Zhang | Mei-Yuh Hwang | Chengqing Zong | Zhifei Li
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Reinforcement learning (RL) is an attractive solution for task-oriented dialog systems. However, extending RL-based systems to handle new intents and slots requires a system redesign. The high maintenance cost makes it difficult to apply RL methods to practical systems on a large scale. To address this issue, we propose a practical teacher-student framework to extend RL-based dialog systems without retraining from scratch. Specifically, the “student” is an extended dialog manager based on a new ontology, and the “teacher” is existing resources used for guiding the learning process of the “student”. By specifying constraints held in the new dialog manager, we transfer knowledge of the “teacher” to the “student” without additional resources. Experiments show that the performance of the extended system is comparable to the system trained from scratch. More importantly, the proposed framework makes no assumption about the unsupported intents and slots, which makes it possible to improve RL-based systems incrementally.