Papers by Xiaojuan Ma
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset (2022.lrec-1)
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Tiezheng Yu, Rita Frieske, Peng Xu, Samuel Cahyawijaya, Cheuk Tung Yiu, Holy Lovenia, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung
| Challenge: | In this paper, we address the problem of data scarcity for the Hong Kong Cantonese language . due to the popularization of deep learning, ASR technology has led to a significant improvement in recognizing many languages. |
| Approach: | They propose to use a dataset to analyze the data available for the Hong Kong Cantonese language . they use zh-HK as a source and a state-of-the-art ASR model to build a powerful model . |
| Outcome: | The proposed model improves on the biggest existing dataset, Common Voice zh-HK. |
Rethinking Personality Assessment from Human-Agent Dialogues: Fewer Rounds May Be Better Than More (2025.findings-emnlp)
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| Challenge: | Existing personality assessment datasets based on natural language do not consider interactivity. |
| Approach: | They propose to use a Chinese dataset to study the effects of different interaction rounds and agent personalities on personality assessment. |
| Outcome: | The proposed dataset contains 1260 interaction rounds between humans and agents with different personalities. |
Recognizing Humour using Word Associations and Humour Anchor Extraction (C18-1)
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| Challenge: | Using humour anchors to improve the performance of humor recognition and interpretation is difficult for computers. |
| Approach: | They propose to use word associations to improve humour recognition models by using humor anchors to improve the performance of semantic features. |
| Outcome: | The proposed models improve the performance of humour recognition and interpretation tasks. |
Learning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization (2020.emnlp-main)
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| Challenge: | Physical commonsense learning is an essential part of human-robot interaction . existing methods of learning physical commons sense suffer from generalization . |
| Approach: | They propose to use physical commonsense learning as a knowledge graph completion problem to better use latent relationships among training samples. |
| Outcome: | The proposed method outperforms existing methods in the human-robot interaction problem. |
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach (D19-1)
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| Challenge: | Existing methods for text emotion distribution learning require a large amount of training data, which is difficult to obtain due to inconsistent perception of fine-grained emotion intensity. |
| Approach: | They propose a meta-learning approach to learn text emotion distributions from a small sample using tensor decomposition to capture contextual semantic similarity. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a widely used EDL dataset. |
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)
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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer
| Challenge: | Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. |
| Approach: | They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills. |
| Outcome: | The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. |
Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition (D19-1)
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| Challenge: | Existing methods for humor recognition require a large amount of training data with labels to learn effective features. |
| Approach: | They propose a tensor embedding method that can extract lexical humor features for continuous humor recognition by using word-word co-occurrence to encode contextual content of documents, and then decompose the tenor to get corresponding vector representations. |
| Outcome: | The proposed method achieves a distance of 0.887 on a global humor ranking task, comparable to the top performing systems from SemEval 2017 Task 6B, but without the need for any external training corpus. |
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization (2022.acl-long)
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| Challenge: | Existing methods to generate educational questions of fairytales or storybooks are difficult to implement due to adults lacking the skills or time to integrate such interactive opportunities. |
| Approach: | They propose a question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. |
| Outcome: | The proposed method performs well on automatic and human evaluation metrics on a newly proposed educational question-answering dataset FairytaleQA. |
CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command Recognition (2022.lrec-1)
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Wenliang Dai, Samuel Cahyawijaya, Tiezheng Yu, Elham J. Barezi, Peng Xu, Cheuk Tung Yiu, Rita Frieske, Holy Lovenia, Genta Winata, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung
| Challenge: | In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety. |
| Approach: | They propose a dataset for in-car command recognition in the cantonese language with both video and audio data. |
| Outcome: | The proposed model can achieve a considerable quality on the clean test set, but the speech recognition quality on noisy data is still inferior. |
ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation (2022.lrec-1)
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Holy Lovenia, Samuel Cahyawijaya, Genta Winata, Peng Xu, Yan Xu, Zihan Liu, Rita Frieske, Tiezheng Yu, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung
| Challenge: | Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. |
| Approach: | They propose to collect Mandarin Chinese-English code-switching corpus from read speech rather than spontaneous speech to address this phenomenon. |
| Outcome: | ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English. |