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Feifei Zhai

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4 papers

2 author rows

ECAI Conference 2024 Conference Paper

A Hybrid Approach towards Chinese Spelling and Splitting Error Correction

Junhong Liang
Junnan Zhu
Feifei Zhai
Nanchang Cheng
Chengqing Zong
Yu Zhou 0001

Existing Chinese spelling check (CSC) methods have limitations in correcting variable-length error characters, requiring the input and output to be the same length. They mainly focus on modelling Chinese characters’ phonetic information and generating candidates for each position. In contrast, few approaches delve into the intricacies of splitting Chinese characters to address glyph errors and splitting variable-length corrections. We define the Chinese Splitting Error Correction (CSEC) task and develop CSEC datasets in news and social media domains to address this issue. We then propose Soft-Masked Multi-feature Error Correction (SoMu) model, which first generates semantic, phonetic, graphic, and unique Chinese Wubi embeddings, then integrates those features through selective gating fusion, followed by a soft-mask strategy to filter incorrect tokens and finally use transformer layers to predict the correct ones. This model effectively addresses both spelling and splitting errors. Extensive analysis shows that our model significantly improves character-splitting information modelling for CSEC. Our dataset is available at https: //github. com/Skywalker-Harrison/SoMu.

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AAAI Conference 2024 Conference Paper

DIUSum: Dynamic Image Utilization for Multimodal Summarization

Min Xiao
Junnan Zhu
Feifei Zhai
Yu Zhou
Chengqing Zong

Existing multimodal summarization approaches focus on fusing image features in the encoding process, ignoring the individualized needs for images when generating different summaries. However, whether intuitively or empirically, not all images can improve summary quality. Therefore, we propose a novel Dynamic Image Utilization framework for multimodal Summarization (DIUSum) to select and utilize valuable images for summarization. First, to predict whether an image helps produce a high-quality summary, we propose an image selector to score the usefulness of each image. Second, to dynamically utilize the multimodal information, we incorporate the hard and soft guidance from the image selector. Under the guidance, the image information is plugged into the decoder to generate a summary. Experimental results have shown that DIUSum outperforms multiple strong baselines and achieves SOTA on two public multimodal summarization datasets. Further analysis demonstrates that the image selector can reflect the improved level of summary quality brought by the images.

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AAAI Conference 2017 Conference Paper

Neural Models for Sequence Chunking

Feifei Zhai
Saloni Potdar
Bing Xiang
Bowen Zhou

Many natural language understanding (NLU) tasks, such as shallow parsing (i. e. , text chunking) and semantic slot ﬁlling, require the assignment of representative labels to the meaningful chunks in a sentence. Most of the current deep neural network (DNN) based methods consider these tasks as a sequence labeling problem, in which a word, rather than a chunk, is treated as the basic unit for labeling. These chunks are then inferred by the standard IOB (Inside-Outside- Beginning) labels. In this paper, we propose an alternative approach by investigating the use of DNN for sequence chunking, and propose three neural models so that each chunk can be treated as a complete unit for labeling. Experimental results show that the proposed neural sequence chunking models can achieve start-of-the-art performance on both the text chunking and slot ﬁlling tasks.

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AAAI Conference 2017 Conference Paper

SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents

Ramesh Nallapati
Feifei Zhai
Bowen Zhou

We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.

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