Zhiqiang Ma


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Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks
Xianzhi Li | Samuel Chan | Xiaodan Zhu | Yulong Pei | Zhiqiang Ma | Xiaomo Liu | Sameena Shah
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

The most recent large language models (LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation. How effective are such models in the finance domain? Understanding this basic question would have a significant impact on many downstream financial analytical tasks. In this paper, we conduct empirical studies and provide experimental evidences of their performance on a wide variety of financial text analytical problems, using eight benchmark datasets from five categories of tasks. We report both the strengths and limitations of the current models by comparing them to the state-of-the-art fine-tuned approaches and the recently released domain-specific pretrained models. We hope our study can help to understand the capability of the existing models in the financial domain and facilitate further improvements.


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面向 Transformer 模型的蒙古语语音识别词特征编码方法(Researching of the Mongolian word encoding method based on Transformer Mongolian speech recognition)
Xiaoxu Zhang (张晓旭) | Zhiqiang Ma (马志强) | Zhiqiang Liu (刘志强) | Caijilahu Bao (宝财吉拉呼)
Proceedings of the 21st Chinese National Conference on Computational Linguistics

“针对 Transformer 模型在蒙古语语音识别任务中无法学习到带有控制符的蒙古语词和语音之间的对应关系,造成模型对蒙古语的不适应问题。提出一种面向 Transformer 模型的蒙古语词编码方法,方法使用蒙古语字母特征与词特征进行混合编码,通过结合蒙古语字母信息使 Transformer 模型能够区分带有控制符的蒙古语词,学习到蒙古语词与语音之间的对应关系。在 IMUT-MC 数据集上,构建 Transformer 模型并进行了词特征编码方法的消融实验和对比实验。消融实验结果表明,词特征编码方法在 HWER、WER、SER 上分别降低了 23.4%、6.9%、2.6%;对比实验结果表明,词特征编码方法领先于所有方法,HWER 和 WER 分别达到 11.8%、19.8%。”

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基于注意力的蒙古语说话人特征提取方法(Attention based Mongolian Speaker Feature Extraction)
Fangyuan Zhu (朱方圆) | Zhiqiang Ma (马志强) | Zhiqiang Liu (刘志强) | Caijilahu Bao (宝财吉拉呼) | Hongbin Wang (王洪彬)
Proceedings of the 21st Chinese National Conference on Computational Linguistics


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ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
Zhiyu Chen | Shiyang Li | Charese Smiley | Zhiqiang Ma | Sameena Shah | William Yang Wang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The community is experiencing the shift of the challenge from how to model language to the imitation of complex reasoning abilities like human beings. In this work, we investigate the application domain of finance that involves real-world, complex numerical reasoning. We propose a new large-scale dataset, ConvFinQA, aiming to study the chain of numerical reasoning in conversational question answering. Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations. We conduct comprehensive experiments and analyses with both the neural symbolic methods and the prompting-based methods, to provide insights into the reasoning mechanisms of these two divisions. We believe our new dataset should serve as a valuable resource to push forward the exploration of real-world, complex reasoning tasks as the next research focus. Our dataset and code is publicly available at https://github.com/czyssrs/ConvFinQA.


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The USTC-NEL Speech Translation system at IWSLT 2018
Dan Liu | Junhua Liu | Wu Guo | Shifu Xiong | Zhiqiang Ma | Rui Song | Chongliang Wu | Quan Liu
Proceedings of the 15th International Conference on Spoken Language Translation

This paper describes the USTC-NEL (short for ”National Engineering Laboratory for Speech and Language Information Processing University of science and technology of china”) system to the speech translation task of the IWSLT Evaluation 2018. The system is a conventional pipeline system which contains 3 modules: speech recognition, post-processing and machine translation. We train a group of hybrid-HMM models for our speech recognition, and for machine translation we train transformer based neural machine translation models with speech recognition output style text as input. Experiments conducted on the IWSLT 2018 task indicate that, compared to baseline system from KIT, our system achieved 14.9 BLEU improvement.