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How Children Recognize Emotions from Video and Audio 26th

How Children Recognize Emotions from Video and Audio

引用

26th International Conference on Speech and Computer

作者： Lyakso, Mena Froloya, Olga Nikolaev, Aleksandr Grechanyi, Severin Filatova, Yulia Nersisson, Ruban St Petersburg State Univ Child Speech Res Grp St Petersburg Russia Vellore Inst Technol Sch Elect Engn Vellore Tamil Nadu India

ISBN: (纸本)9783031780134;9783031780141

The paper presents the results of assessing the ability of children from different groups - typical development (TD), autism spectrum disorder (ASD), and intellectual disability (ID) - to recognize the emotions of other people using video and audio tests. The study included 225 children aged 5 to 16 years and 2 adults. We used the original dataset by the Child's Emotional Development Method (CEDM), selecting from dataset the video recordings of four test tasks performed by children to recognize emotions from photographs of children, photographs of adults, abstract images, and the speech and voice of children (audio test). Children's ability to recognize emotions correctly was assessed in two ways: by experts' ratings on a Likert scale and by children's correct answers. The results showed greater accuracy in recognizing emotional states by TD children compared to children with ASD and ID. Peculiarities of recognition by children with ASD and ID of various emotions using video and audio tests were revealed. The novelty of the results lies in the comparative data on the recognition of emotions by TD children and children with various developmental disorders. The problem of assessing children's emotion recognition using a standardized approach for further use of data in creating applications that teach children to express and recognize emotions is original and new.

关键词： Emotions Recognition Children with Autism Spectrum Disorders and Intellectual Disabilities Video and Audio Modalities

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Semantic Knowledge Enhanced and Global Pointer Optimized Method for Medical Nested Entity Recognition 13th

Semantic Knowledge Enhanced and Global Pointer Optimized Met...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Song, Yilin Kong, Fang Soochow Univ Lab Nat Language Proc Suzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

In the medical field, unstructured medical text holds rich medical knowledge. Identifying medical entities in this text accurately is crucial for structured medical databases, knowledge graphs, and intelligent diagnostic systems. Medical text has unique features, making it hard for traditional NER methods to identify complex medical entities. In particular, the recognition of nested entities within medical text poses a significant challenge, as it requires systems to recognize and understand the complex hierarchical relationships between entities, placing higher demands on traditional entity recognition systems. To overcome the challenges of nested entity recognition in medical text, we propose a method that combines semantic knowledge enhancement and global pointer optimization. Initially, we incorporate semantic prior knowledge of entity categories, capturing the interplay between labels and text by integrating label relationships. This allows us to obtain candidate entity information enriched with integrated label details. Following this, we establish a classification module to evaluate and score these candidate entities along with their labels, enabling entity prediction. To address nested entities, we introduce a Efficient GlobalPointer module that computes the likelihood of each text span being a specific entity type, thus bolstering nested entity recognition. By merging the outputs from both modules, we arrive at the final predicted entities. Experimental results indicate that our method excels on two flat entity datasets, CMedQANER and CCKS2017, as well as on the nested entity dataset CMeEE. Compared to baseline models, our approach demonstrates notable performance enhancements.

关键词： Named Entity Recognition Nested Entity Recognition Medical Named Entity Recognition

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Span-Based Chinese Few-Shot NER with Contrastive and Prompt Learning 13th

Span-Based Chinese Few-Shot NER with Contrastive and Prompt ...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Ye, Feiyang Lai, Peichao Yang, Sanhe Zhang, Zhengfeng Wang, Yilei Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China

ISBN: (纸本)9789819794331;9789819794348

For Chinese Named Entity Recognition (NER) tasks, achieving better performance with fewer training samples remains a challenge. Previous works primarily focus on enhancing model performance in NER by incorporating additional knowledge to construct entity features. These approaches neglect the semantic information of entity labels and the information of entity boundaries. Moreover, conventional methods typically treat NER as a sequence labeling task, which makes them inadequate for addressing the issue of nested entities. We propose a new span-based approach by using contrastive learning and prompt learning to address these problems. By pulling similar entities closer together, pushing dissimilar entities further apart, and leveraging entity label information, we improve model performance in few-shot scenarios effectively. Experimental results demonstrate that our method achieves significant performance improvements on a sampled Chinese nested medical dataset and several other flattened datasets, providing a new insight into addressing challenges in few-shot NER tasks.

关键词： Named Entity Recognition Contrastive Learning Few-shot Learning

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Introducing Structural Information of Argumentative Essays Into Pre-trained Models 13th

Introducing Structural Information of Argumentative Essays I...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Wang, Chuhan Li, Dailin Wang, Yuzhao Quo, Xuening Zhang, Bo Wang, Man Dalian Univ Technol Sch Comp Sci & Technol Dalian Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Argument Mining (AM) is a significant task in the field of Natural Language Processing (NLP). However, effectively utilizing the structural information of these components remains a challenge for traditional approaches. Transformer architectures, such as RoBERTa and DeBERTa, have emerged as the unified paradigm for NLP tasks. Integrating structural information effectively using transformer architectures remains a challenge. To tackle this challenge, we employed advanced modeling techniques by utilizing RoBERTa for sentence-level modeling, integrating attention mechanisms to capture comprehensive textual information. Concurrently, we utilized DeBERTa, leveraging prompts to introduce structural information. We observed significant differences in predictions between models incorporating full-text information through attention mechanisms and those using prompt-based approaches, and simultaneously among models with varying parameter sizes. Therefore, we applied model ensemble techniques to enhance predictive performance. To ensure the reliability of the ensemble results, we supplemented our predictions with inference from large language models. By integrating these approaches, we established a system for argument components identification. In the NLPCC-2024 shared task 5, our system achieved fifth place with a score of 74.54. We released our code at https://***/captainpickstar/New Folder-on-NLPCC2024-task5.

关键词： Argument Components Identification Pre-trained Language Model Ensemble Learning

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ACTOR: Advancing Argument Components Identification Through In-Context Learning and Proximity Information Awareness 13th

ACTOR: Advancing Argument Components Identification Through ...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zeng, Peijian Zeng, Weixiong Wu, Hongyan Lin, Nankai Yang, Aimin Hou, Shengyi Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Lingnan Normal Univ Sch Comp Sci & Intelligence Educ Zhanjiang 524000 Guangdong Peoples R China Guangzhou Coll Commerce Sch Informat Technol & Engn Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Argumentative writing plays a crucial role in developing and enhancing high school students' critical thinking skills, serving as a key form of argumentative expression. The task of identifying argument components aids students in understanding the structure of argumentative essays and assists teachers in evaluating students' proficiency in scientific argument mining. However, existing research lacks a detailed classification of argument types. This paper addresses the NLPCC 2024 Shared Task 5 "Argument Mining for Chinese Argumentative Essays" by proposing a Argument Components idenTificatiOn fRamework (ACTOR). We employ a proximity information awareness (PIA) strategy to provide the model with more relevant information and use the in-context learning (ICL) method to offer pertinent reference examples. Experimental results indicate that our method is competitive in the argument component identification task.

关键词： Argument Components Identification Proximity Information Awareness In-context Learning

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SafeLLMs: A Benchmark for Secure Bilingual Evaluation of Large Language Models 13th

SafeLLMs: A Benchmark for Secure Bilingual Evaluation of Lar...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Liang, Wenhan Wu, Huijia Gao, Jun Shang, Yuhu He, Zhaofeng Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Since the advent of the GPT-3.5 model, numerous large language models(LLMs) have emerged in China. With the increasing number of users, the security of these models has garnered extensive attention from researchers. However, the current evaluation samples lack comprehensiveness and diversity, and the evaluation is primarily conducted in a single language. In this paper, we introduced the SafeLLMs benchmark as a method to assess the security of models. Creative innovation was employed as we proposed seven evaluation dimensions and compiled a dataset consisting of 19,763 evaluation samples to conduct a comprehensive assessment of LLMs. Under both zero-shot and three-shot settings, in both Chinese and English, we conducted evaluations on 16 models. Furthermore, we analyzed the disparities in security performance among different models when processing the two languages. We found ample room for improvement in dimensions such as Bias and Discrimination. We posit that the findings from this evaluation experiment hold the potential to enhance the security performance of LLMs. Our evaluation dataset and results will soon be showcased.

关键词： Model Evaluation Security Assessment Large Language Models

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Empowering LLMs for Long-Text Information Extraction in Chinese Legal Documents 13th

Empowering LLMs for Long-Text Information Extraction in Chin...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Shen, Chenchen Ji, Chengwei Yue, Shengbin Shen, Xiaoyu Song, Yun Huang, Xuanjing Wei, Zhongyu Fudan Univ Sch Data Sci Shanghai Peoples R China Fudan Univ Shanghai Ctr Math Sci Shanghai Peoples R China Eastern Inst Technol Ningbo Peoples R China Northwest Univ Polit & Law Xian Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Existing large language models(LLMs) in the legal domain typically only handle text within a limited context window size, making it challenging to fully meet the requirements of long legal texts. In this paper, we propose to achieve long-text information extraction for Chinese judgement documents by training on short-text models with low fine-tuning costs. Through training on short-text data and improving the rotational positional encoding method, we achieve a model capable of directly inferring on long texts with relatively low fine-tuning costs. Overall experimental results demonstrate that the our model for long judgement documents information extraction outperforms all compared LLMs.

关键词： Information extraction Large Language Model Instruction Tuning Intelligent Law

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Steganographic Text Generation Based on Large Language Models in Dialogue Scenarios 13th

Steganographic Text Generation Based on Large Language Model...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zeng, Qingwei Wang, Kaixi Qingdao Univ Sch Comp Sci & Technol Qingdao 266071 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

The text comprehension and generation capabilities of large language models (LLMs) have become extremely powerful, fulfilling the needs of many application scenarios. And online chatting is very popular, which provides new application scenarios for text steganography. In this paper, we propose a steganography scheme that generates conversational text with specified topics and grammars based on the LLMs. The algorithm maps a secret as prompts input into the LLMs to generate steganographic text. To improve imperceptibility, after statistical analysis of tense usage in different topics, its frequencies are used to code the tenses in variable lengths to ensure that the generated steganographic text aligns the tense of the generated steganographic text with that of normal text. The comprehensive analyses are performed to evaluate the generated steganographic text. The results show that in term of Perplexity, a steganographic text has good imperceptibility, and the metrics such as Kullback-Leibler divergence (KL) and Jensen-Shannon divergence (JS) indicate it is statistically difficult to distinguish the generated steganographic text from normal text, which indicates the steganographic text has strong anti-steganalysis ability.

关键词： Large language models NLP application Text steganography Conversational generation

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Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models 13th

Towards Building a Robust Knowledge Intensive Question Answe...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Hong, Xingyun Shao, Yan Wang, Zhilin Duan, Manni Jin, Xiongnan Zhejiang Lab Hangzhou Peoples R China Hangzhou Res & Dev Ctr China Mobile Hangzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

The development of LLMs has greatly enhanced the intelligence and fluency of question answering, while the emergence of retrieval enhancement has enabled models to better utilize external information. However, the presence of noise and errors in retrieved information poses challenges to the robustness of LLMs. In this work, to evaluate the model's performance under multiple interferences, we first construct a dataset based on machine reading comprehension datasets simulating various scenarios, including critical information absence, noise, and conflicts. To address the issue of model accuracy decline caused by noisy external information, we propose a data augmentation-based fine-tuning method to enhance LLM's robustness against noise. Additionally, contrastive learning approach is utilized to preserve the model's discrimination capability of external information. We have conducted experiments on both existing LLMs and our approach, the results are evaluated by GPT-4, which indicates that our proposed methods improve model robustness while strengthening the model's discrimination capability.

关键词： Retrieval-augmented LLM Question answering Robustness

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UHDF: Hallucination Detection Using Open Source Models Beyond Close Source Models Methods 13th

UHDF: Hallucination Detection Using Open Source Models Beyon...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Liu, Dongxu Xu, Bufan Zhao, Zhilong Xu, Bing Yang, Muyun Harbin Inst Technol Harbin 150006 Peoples R China

ISBN: (纸本)9789819794423;9789819794430

With the emergence of multimodal large models, the problem of hallucination has been plaguing their development and deployment. How to reliably detect the presence of hallucinations inmLLMshas become an important issue. We propose UHDF, which replaces the closed-source models in it with open-source models by improving UniHD [15], and dramatically outperforms it. By optimizing the external information used in UniHD and achieving decoupling between different external information sources, we minimize the hallucinations introduced in pipeline, and thus improve the effectiveness of hallucinations detection. UHDF using the open-source model outperforms UniHD using the closed-source model (GPT-4v), achieving 86.6% (dev set)/85.3% (test set) on MacroF1 and achieved the first place in NLPCC2024 Shared Task 10 Track1 (Open Source). Our code and models are available at https://***/codetalker125/UHDF.

关键词： mLLM hallucination detection external information

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