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YoloGPT: Enhancing Chinese Character Recognition and Correction 13th

YoloGPT: Enhancing Chinese Character Recognition and Correct...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Yang, Sheng Lian, Zhanbiao Li, Kunyu Liu, Peilin Wu, Fengge Zhao, Junsuo Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Helping people to write well is a fundamental area of Natural Language Processing (NLP) research. The purpose of writing assistance is to improve the accuracy and quality of the input text, with character checking being one of the essential abilities to detect and correct erroneous characters. The erroneous characters appear in humans' handwriting include faked characters (nonexistent characters due to handwriting errors) and misspelled characters (incorrect use of real characters due to spelling errors). In this paper, we introduce YoloGPT, a novel model that combines YOLO and GPT for detecting and correcting erroneous characters within visual form human written texts in real-world scenarios, including faked and misspelled Chinese characters. YoloGPT begins by using a YOLO object detection model customized to recognize character sets to form sentences with Chinese characters and label the faked ones. Then, it employs the powerful large language model GPT equipped with an automatic prompt optimization method specially designed to find an optimized prompt. The LLMs use the explicit guidance of the prompt to correct the erroneous characters and generate the final answer. The YoloGPT model has achieved remarkable results, securing first place in Task 1 of NLPCC2024, demonstrating the effectiveness of our method.

关键词： Chinese Character Checking YOLO Large Language Models

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Overview of the NLPCC 2024 Shared Task 3: Dialogue-Level Coreference Resolution and Relation Extraction 13th

Overview of the NLPCC 2024 Shared Task 3: Dialogue-Level Cor...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Xiong, Yiyun Li, Fei Li, Bobo Fei, Hao Ji, Donghong Teng, Chong Wuhan Univ Sch Cyber Sci & Engn Key Lab Aerosp Informat Secur & Trusted Comp Minist Educ Wuhan Peoples R China Natl Univ Singapore Sch Comp Singapore Singapore

ISBN: (纸本)9789819794423;9789819794430

In this report, we give an overview of the shared task about dialogue-level coreference resolution and relation extraction at the 13th CCF Conference on Natural Language Processing and Chinese Computing (NLPCC 2024). Dialogue Relation Extraction (DRE) focuses on the relations between arguments in a conversation, which can help better understand the interaction between interlocutors. The phenomenon of coreference is widely present in natural language, especially in conversations. Dialogue Coreference Resolution (DCR) can provide more accurate contextual understanding for the DRE task. We introduce dialogue-level joint coreference resolution and relation extraction task, and evaluate the ability of models to recognize dialogue-level coreference information and extract relations using coreference information. We describe the participating systems and their results to show the up-to-date progress in the DRE and DCR tasks, providing guidelines for future research.

关键词： Dialogue Coreference Resolution Dialogue Relation Extraction NLPCC 2024

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The ParlaSpeech Collection of Automatically Generated Speech and Text Datasets from Parliamentary Proceedings 26th

The ParlaSpeech Collection of Automatically Generated Speech...

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26th International Conference on Speech and Computer

作者： Ljubesic, Nikola Rupnik, Peter Korzinek, Danijel Jozef Stefan Inst Ljubljana Slovenia Univ Ljubljana Fac Informat & Commun Sci Ljubljana Slovenia Polish Japanese Acad Informat Technol Warsaw Poland

ISBN: (纸本)9783031779602;9783031779619

Recent significant improvements in speech and language technologies come both from self-supervised approaches over raw language data as well as various types of explicit supervision. To ensure high-quality processing of spoken data, the most useful type of explicit supervision is still the alignment between the speech signal and its corresponding text transcript, which is a data type that is not available for many languages. In this paper, we present our approach to building large and open speech-and-text-aligned datasets of less-resourced languages based on transcripts of parliamentary proceedings and their recordings. Our starting point are the ParlaMint comparable corpora of transcripts of parliamentary proceedings of 26 national European parliaments. In the pilot run on expanding the ParlaMint corpora with aligned publicly available recordings, we focus on three Slavic languages, namely Croatian, Polish, and Serbian. The main challenge of our approach is the lack of any global alignment between the ParlaMint texts and the available recordings, as well as the sometimes varying data order in each of the modalities, which requires a novel approach in aligning long sequences of text and audio in a large search space. The results of this pilot run are three high-quality datasets that span more than 5,000 h of speech and accompanying text transcripts. Although these datasets already make a huge difference in the availability of spoken and textual data for the three languages, we want to emphasize the potential of the presented approach in building similar datasets for many more languages.

关键词： Spoken corpora Parliamentary proceedings Long speech to text alignment

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Investigating the Utility of wav2vec 2.0 Hidden Layers for Detecting Multiple Sclerosis 26th

Investigating the Utility of wav2vec 2.0 Hidden Layers for D...

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26th International Conference on Speech and Computer

作者： Gosztolya, Gabor Toth, Laszlo Svindt, Veronika Bona, Judit Hoffmann, Ildiko HUN REN SZTE Res Grp Artificial Intelligence Szeged Hungary Univ Szeged Inst Informat Szeged Hungary HUN REN Res Ctr Linguist Budapest Hungary Eotvos Lorand Univ Dept Linguist Budapest Hungary Univ Szeged Dept Psychiat Szeged Hungary

ISBN: (纸本)9783031779602;9783031779619

Multiple sclerosis (MS) is a chronic autoimmune neurodegenerative disease, affecting the central nervous system. The disease can induce various symptoms, such as adversarily affecting the speech of the subject in various ways, therefore allowing the use of automatic speech analysis for the detection of MS and for monitoring the condition of the patient. Owing to data scarcity, however, deep neural networks are usually not employed for this task as classifiers, but are used as feature extractors. This is the case for self-supervised networks such as wav2vec 2.0 as well, where a straightforward source of embeddings (used as features) are the last layers of the convolutional (lower) and fine-tuned (higher) blocks. In this study we investigate whether extracting the embeddings from some other, inner layer of the fine-tuned (transformer) block can help improve MS detection performance. Tested on two speech tasks, we found that the lowest one-third of the 24 fine-tuned layers proved to be the most suitable for feature extraction, which led to statistically significant improvements in the AUC scores for both speech tasks.

关键词： Multiple sclerosis . Pathological speech processing . Wav2vec 2.0 . Feature extraction

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How to Detect Imbalances in the Google Books Ngram Corpus? 26th

How to Detect Imbalances in the Google Books Ngram Corpus?

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26th International Conference on Speech and Computer

作者： Solovyev, Valery Ivleva, Anna Kazan Fed Univ Inst Philol & Intercultural Commun Lab Linguist & AI Kazan Russia

ISBN: (纸本)9783031780134;9783031780141

The Google Books Ngram diachronic corpus is an important tool to study changes in the language and society. The article draws attention to the problem of diachronic balance instability of various text genres in the corpus. The instability is shown not to correspond the publishing policy of the country. The method for detecting the imbalance is proposed. The Russian subcorpus is analyzed in detail to shows the presence of serious balance instabilities in the 21st century. We analyze the frequency dynamics of small groups of words, as well as make extended statistical research of the corpus. We calculate such parameters of word frequency dynamics as autocorrelations, linear trends, mathematical expectations and variances. We also apply the methods of singular spectral analysis and moving average to find unnatural peaks of the average "noise". Our conclusion is unreasonably large amount of fiction in the Russian subcorpus in some specific years. Similar effects are also found in the English subcorpus. These facts seem to be significant because the most often studied objects of psychological and sociological research are the high-frequency common words widespread in fiction. An example is given to demonstrate that ignoring this factor can result in erroneous conclusions.

关键词： the Google Books Ngram corpus diachronic balance instability imbalance of genres word frequency time series

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Model-Agnostic Knowledge Distillation Between Heterogeneous Models 13th

Model-Agnostic Knowledge Distillation Between Heterogeneous ...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Shen, Jiaxin Liu, Yanyao Jiang, Yong Chen, Yufeng Han, Wenjuan Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China Alibaba DAMO Acad Hangzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

The goal of KD is to transfer valuable knowledge from a strong teacher model to a weaker student model in order to bridge the performance gap. However, the conventional teacher-student paradigm of KD can not be applied to the teacher model and student model with different output representations (referred to as heterogeneous models). To overcome this limitation, we propose a model-agnostic approach for KD called MAKD. MAKD consists of two stages: informative sample generation and knowledge transfer. The key idea is to generate informative samples that convey the discrepancy between the teacher model and student model, so that the generated samples are effective and efficient. We formulate two ways for informative sample generation: word substitution and text generation. After generating desired samples, we perform knowledge transfer by mixing the informative samples into the original training set and retraining the student model to improve its performance. We conducted experiments on two tasks, dependency parsing, and grammatical error correction, and the results demonstrate that MAKD successfully enhances the performance of the student model on both tasks. Our data and code are available at https://***/WinnieHAN/Model_Agnostic_Knowledge_Distillation.

关键词： Knowledge Distillation Data Augmentation Model Agnostic

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Multilingual Temporal Answer Grounding in Video Corpus with Enhanced Visual-Textual Integration 13th

Multilingual Temporal Answer Grounding in Video Corpus with ...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Ma, Tianxing Hu, Yueyue Jiang, Shuang Yin, Zhenhao Zang, Tianning Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (纸本)9789819794423;9789819794430

This paper presents the experiment scheme of Team IIEleven in the NLPCC 2024 shared task, Multilingual Medical Instructional Video Question Answering (MMIVQA) challenge, Track 3: Multilingual Temporal Answer Grounding in Video Corpus (mTAGVC). The objective of the mTAGVC task is to identify video spans most relevant to the given questions from a large multilingual medical instructional video corpus. We propose an Multilingual Visual-Textual Span Enhancement (MVTSE) method, which simultaneously performs two subtasks of video corpus retrieval and temporal answering grounding in video, and further strengthens the cross language ability and comparative learning performance of the model by subtitle text supplementation, multilingual data augmentation and hard sample selection method. The experimental results on the given dataset show that the proposed method achieves state-of-the-art (SOTA) performance, and ranks first in the mTAGVC track of the MMIVQA challenge.

关键词： Multimodal Multilingual Temporal Answer Grounding Video Retrieval

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Cross-Cultural Automatic Depression Detection Based on Audio Signals 26th

Cross-Cultural Automatic Depression Detection Based on Audio...

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26th International Conference on Speech and Computer

作者： Marnontov, Danila Zepf, Sebastian Karpov, Alexey Minker, Wolfgang Ulm Univ Ulm Germany ITMO Univ St Petersburg Russia Russian Acad Sci St Petersburg Inst Informat & Automat St Petersburg Fed Res Ctr Russian Acad Sci SPC RAS St Petersburg Russia

ISBN: (纸本)9783031779602;9783031779619

Depression is a frequently occurring mental health disorder globally, and early detection is critical for effective treatment. In this paper, we explore the effectiveness of machine learning techniques in cross-cultural depression detection using audio signals from Chinese and English-speaking populations. We investigate the influence of temporal context length, feature sets, and classifiers on classification performance across two single and two cross-corpus settings. Our results show that hand-crafted features offer advantages in single and combined dataset settings, while deep learning-based features, particularly from emotion recognition tasks, demonstrate superior cross-dataset generalization. The optimal length of a temporal context strongly depends on the specific dataset. These findings highlight the importance of considering dataset-specific characteristics and feature selection in developing reliable and culturally adaptable models for depression detection. In the cross-corpus settings on MENHIR and CMDC datasets, we obtained the best F1 scores of 0.77 and 0.63, respectively. Future research should focus on enhancing model performance and data accessibility to ensure effective inclusion across diverse populations, ultimately contributing to better mental health outcomes globally.

关键词： Depression recognition Deep embeddings OpenSMILE Cross-corpus analysis CMDC MENHIR

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WConF: Weighted Contrastive Fusion for Multimodal Sentiment Analysis 13th

WConF: Weighted Contrastive Fusion for Multimodal Sentiment ...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Zeng, Biqing Li, Ruiyuan Lu, Liuxing Lu, Liangqi Wang, Jiazhen Chen, Weihai Dong, Huimin South China Normal Univ Sch Software Foshan Peoples R China South China Normal Univ Aberdeen Inst Data Sci & Artificial Intelligence Foshan Peoples R China South China Normal Univ Sch Econ & Management Guangzhou Peoples R China Guangdong AIB Polytech Sch Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

With the continuous development of multimedia, Multimodal Sentiment Analysis has become a highly regarded field. Recent research proposes learning effective unimodal representations to facilitate multimodal fusion, which mainly contain two parts of information: modality-common and modality-specific information. However, previous work does not consider the sentiment span information between different samples during the fusion process. In this paper, we propose a novel framework Weighted Contrastive Fusion (WConF) to extract sentiment span information for multimodal fusion. First, we apply modality contrastive learning to capture modality-common sentiment information and separate modality-specific sentiment information. Then, considering sentiment spans as weights, we perform weighted contrastive learning on the multimodal representations. Moreover, we design a loss function to assist in weighted contrastive fusion. Finally, we conduct extensive experiments on four benchmark datasets: MOSI, MOSEI, CH-SIMS, and CH-SIMSV2. Experimental results demonstrate that our model achieves superior performance compared to previous models.

关键词： Weighted Contrastive Fusion Multimodal Sentiment Analysis Contrastive Learning

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Prompt-Based Joint Contrastive Learning for Zero-Shot Relation Extraction 13th

Prompt-Based Joint Contrastive Learning for Zero-Shot Relati...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Zou, Jianjian Xiao, Yuhui Zhou, Sichi Li, Wei Yang, Qun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Nari Grp Corp State Grid Elect Power Res Inst Nanjing Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Zero-shot relation extraction is proposed to address the issue that the model performance drops dramatically when identifying novel relations that cannot be observed at the training stage. Existing works focus more on the pattern of matching between instances and relation descriptions, however, they ignore the capabilities of pre-trained model in zero-shot scenarios. In this paper, we propose a novel prompt-based joint contrastive learning framework (PCL) to enhance zero-shot relation extraction. Specifically, we construct a prompt template to leverage prior knowledge in PLMs for better representation of the relation. In order to learn more effective features, we design two contrastive learning modules to jointly optimize the obtained representations. Experimental results on two publicly available datasets show that PCL outperforms the previous methods.

关键词： zero-shot relation extraction prompt learning contrastive learning

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