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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Cross-Lingual Summarization of Speech-to-Speech Translation: A Baseline 26th

Cross-Lingual Summarization of Speech-to-Speech Translation:...

引用

26th International Conference on Speech and Computer

作者： Karande, Pranav Sarkar, Balaram Maurya, Chandresh Kumar Indian Inst Technol Indore Indore India

ISBN: (纸本)9783031779602;9783031779619

Cross-lingual speech-to-speech translation, which enables spoken language conversion from one language to another, plays a pivotal role in overcoming language barriers and promoting cross-cultural communication. The proliferation of multimedia content poses challenges for audiences to efficiently consume extended audio, such as news broadcasts, academic lectures, and political speeches. To address this, we propose a novel investigation of summarization in the context of cross-lingual speech-to-speech translation (S2S-Summ) with a focus on low-resource Indic languages. To the best of our knowledge, this task has not been explored in prior research. We develop and present a semi-synthetic dataset of translated summaries in Hindi (Hi), Bengali (Bn), Gujarati (Gu), and Tamil (Ta) languages, alongside baseline models for this task. The performance of our models is evaluated using metrics such as BERTScore, ROUGE, and UniEval. Our study aims to catalyze further exploration in this area, facilitating streamlined access to multilingual audio content and enhancing information dissemination across linguistic boundaries. Code and data is available at https://***/pranavkarande/S2S-Summ.

关键词： Speech summarization Low-resource languages

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Marking: Visual Grading with Highlighting Errors and Annotating Missing Bits 25th

Marking: Visual Grading with Highlighting Errors and Annotat...

引用

25th International Conference on artificial intelligence in Education (AIED)

作者： Sonkar, Shashank Liu, Naiming Mallick, Debshila B. Baraniuk, Richard G. Rice Univ Houston TX 77005 USA

ISBN: (纸本)9783031643019;9783031643026

In this paper, we introduce "Marking", a novel grading task that enhances automated grading systems by performing an in-depth analysis of student responses and providing students with visual highlights. Unlike traditional systems that provide binary scores, "marking" identifies and categorizes segments of the student response as correct, incorrect, or irrelevant and detects omissions from gold answers. We introduce a new dataset meticulously curated by Subject Matter Experts specifically for this task. We frame "Marking" as an extension of the Natural Language Inference (NLI) task, which is extensively explored in the field of Natural Language Processing. The gold answer and the student response play the roles of premise and hypothesis in NLI, respectively. We subsequently train language models to identify entailment, contradiction, and neutrality from student response, akin to NLI, and with the added dimension of identifying omissions from gold answers. Our experimental setup involves the use of transformer models, specifically BERT and RoBERTa, and an intelligent training step using the e-SNLI dataset. We present extensive baseline results highlighting the complexity of the "Marking" task, which sets a clear trajectory for the upcoming study. Our work not only opens up new avenues for research in AI-powered educational assessment tools, but also provides a valuable benchmark for the AI in education community to engage with and improve upon in the future. The code and dataset can be found at https://***/luffycodes/marking.

关键词： Automated Grading Highlighting Feedback Natural Language Inference

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Only One Relation Possible? Modeling the Ambiguity in Temporal Relation Extraction 13th

Only One Relation Possible? Modeling the Ambiguity in Tempor...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Hu, Yutong Huang, Quzhe Feng, Yansong Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China State Key Lab Gen Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Event Temporal Relation Extraction (ETRE) aims to identify the temporal relationship between two events. Most previous works follow a single-label classification paradigm, classifying an event pair into either a well-defined temporal relation (e.g., Before, After) when its temporal relation is clear and unique, or a special relation Vague when there are multiple possible temporal relations. This paradigm treats Vague independently, ignoring that the instances labeled as Vague also convey temporal information about well-defined relations. To better handle the special relation Vague, we propose Multi-label Event Temporal Relation Extraction (METRE), a new method treating the ETRE as a multi-label classification task. METRE predicts the probability of each well-defined relation separately and infers the relation Vague if there is more than one relation possible for an event pair, instead of directly predicting the probability of Vague. Considering the gold composition of Vague is not available, we design a speculation mechanism to explore the possible relations contributing to Vague in each case, which enables the latent information to be used efficiently. Experiments on TB-Dense, MATRES and UDS-T show that our method can make better use of the relation Vague and outperform state-of-the-art methods.

关键词： temporal relation extraction multi-label classification

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Evaluating Human-Large Language Model Alignment in Group Process 13th

Evaluating Human-Large Language Model Alignment in Group Pro...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： He, Yidong Liu, Yongbin Ouyang, Chunping Liu, Huan Han, Wenyong Gao, Yu Zhu, Chi Tang, Yi Zhong, Jin Zhou, Shuda Huang, Le Univ South China Sch Comp Hengyang Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Exploring the alignment of multi-agent systems with human values during group process is an essential step towards the development of artificial general intelligence. In this work, we present a novel approach to systematically evaluate factors that influence the value orientation of large language models (LLMs) in simulating human group process. Our proposed framework, which requires neither fine-tuning nor pre-training, enables LLMs to simulate debates among personas on a given topic, autonomously assess response confidence, and retrieve external information to enhance low-confidence responses. We conduct comparative experiments using the same framework with human participants. Additionally, we introduce a comprehensive set of evaluation metrics to reveal discrepancies in value orientation alignment between LLM systems and human systems in group process.

关键词： large language models group process natural language process

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Assessment of Children's Ability to Manifest Emotions in Facial Expressions, Voice and Speech by Humans, Automatic, and on a Likert Scale 26th

Assessment of Children's Ability to Manifest Emotions in Fac...

引用

26th International Conference on Speech and Computer

作者： Lyakso, Elena Frolova, Olga Matveev, Anton Nikolaev, Aleksandr 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)9783031779602;9783031779619

The goal of the study was to assess children's ability to manifest emotions in facial expressions and speech by humans, automatic and using Likert scale scores. To achieve this goal, two studies were conducted. The first study performed a perceptual and automatic analysis of the emotions "joy - neutral - sadness - anger" in typically developing (TD) children;the second - compared emotion recognition in four groups of children - TD, autism spectrum disorders (ASD), intellectual disabilities (ID) and Down syndrome (DS) by expert and automatic, and analyzed Likert scale scores for completing test tasks. The participants of the study were 110 children aged 5 - 16 years, 18 adults. The original dataset containing video and audio fragments of children's emotional states was used. Experts recognize the emotions of children from all groups by video and speech more accurately than automatic classifications, with higher UAR values for TD children by audio and video in a perceptual experiment and by audio in the automatic classification of emotions. Differences in the classification accuracy of emotions in children with ASD, ID, and DS were identified. Sadness and anger states are automatically classified poorly by audio and video in children with ASD, ID, and DS. The novelty of the results lays in the obtaining normative data on the recognition of emotions in TD children and in comparative data on groups of TD children, children with ASD, ID, DS.

关键词： Emotional State Perceptual and Automatic Recognition Children Video Audio Modalities Likert Scale Scores

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Identifying and Mitigating Algorithmic Bias in Student Emotional Analysis 25th

Identifying and Mitigating Algorithmic Bias in Student Emoti...

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25th International Conference on artificial intelligence in Education (AIED)

作者： Ashwin, T. S. Biswas, Gautam Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA

ISBN: (纸本)9783031642982;9783031642999

Algorithmic bias in educational environments has garnered increasing scrutiny, with numerous studies highlighting its significant impacts. This research contributes to the field by investigating algorithmic biases, i.e., selection, label, and data biases in the assessment of students' affective states through video analysis in two educational settings: (1) an open-ended science learning environment and (2) an embodied learning context, involving 41 and 12 students, respectively. Utilizing the advanced High-speed emotion recognition library (HSEmotion) and Multi-task Cascaded Convolutional Networks (MTCNN), and contrasting these with the commercially available iMotions platform, our study delves into biases in these systems. We incorporate real student data to better represent classroom demographics. Our findings not only corroborate the existence of algorithmic bias in detecting student emotions but also highlight successful bias mitigation strategies. The research advances the development of equitable educational technologies and supports the emotional well-being of students by demonstrating that targeted interventions can effectively diminish biases.

关键词： Algorithmic Bias Classification Bias Open-ended Learning Environment Embodied Learning Bias Mitigation Techniques Emotion Recognition Bias Affective Computing Facial Expressions

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CEUS: Comment Emotion and User Stance Fusion Network for Fake News Detection 13th

CEUS: Comment Emotion and User Stance Fusion Network for Fak...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Geng, Ning Tan, Zhenhua Zhang, Tao Wu, Danke Northeastern Univ Software Engn Coll Shenyang 110004 Liaoning Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Online social platforms provide people with a new way of communication and exchange, and also make the rapid spread of fake news possible. Therefore, automatic detection of fake news has become crucial. Most existing fake news detection methods use deep learning models based on news content and social context. However, fake news often has strong misleading characteristics and can trigger users' high emotions and questioning stances, which can provide useful information for fake news detection. In this paper, we propose a novel fake news detection model that integrates Comment Emotion and User Stance, namely CEUS. Specifically, We first train the emotion recognition task and fake news detection task jointly by multi-task learning, which extracts high-dimensional representations of emotional features from news comments. Then, based on the semantic representations of news content and comments, we calculate their pairwise similarity and take into account the temporal nature of user comments to obtain the forward and backward user stance. Finally, we concatenate emotion features and stance features to detect fake news together. Extensive experiments on real-world fake news datasets Weibo-16 and Weibo-20 show that the CEUS outperforms the state-of-the-art methods, and learns useful emotion features and stance features to efficiently improve the performance of fake news detection.

关键词： Fake news detection Emotion recognition Deep learning

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DuFCALF: Instilling Sentience in Computerized Song Analysis 26th

DuFCALF: Instilling Sentience in Computerized Song Analysis

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26th International Conference on Speech and Computer

作者： Mukherjee, Himadri Marciano, Matteo Dhar, Ankita Roy, Kaushik West Bengal State Univ Dept Comp Sci Barasat India New York Univ Abu Dhabi Dept Arts & Humanities Gazelien Records Lab Mus Program Abu Dhabi U Arab Emirates Sister Nivedita Univ Dept Comp Sci & Engn Chakpachuria India

ISBN: (纸本)9783031780134;9783031780141

Music recommendation systems have evolved significantly in the past couple of years and have become extremely popular with the advancement of artificial intelligence (AI). Such systems categorize songs based on disparate perspectives, like genre, artist, tempo, etc. However, there have been fewer developments in the purview of song categorization based on feelings. Songs amplify the mood of a person and often, listeners choose the type of song they want to listen to based on their mood. Hence, modeling the emotional content of songs is crucial. However, this is a challenging affair because songs embody multiple instruments and vocals in a single instance. Each of them is often modulated differently for a better experience for the listeners as well. This is very common for present-day songs and many songs of different emotions often have very similar instruments and chord progressions which further enhances the challenge. In this paper, a system is presented to distinguish song clips based on their emotion. The clips were parameterized using two features which were fed to disparate deep networks. Thereafter, a dual feature cross architecture late fusion (DuFCALF) strategy was used to distinguish the moods. Experiments were performed with multitudinous sections of songs to test its efficacy for limited data and the sadness of songs was captured with over 80% accuracy. An overall increase of 4.43% in distinction of the emotions was obtained using DuFCALF over the best-performing baseline system.

关键词： Music analysis Emotion Audioscape Deep learning

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Enhancing Logical Rules Based on Self-Distillation for Document-Level Relation Extraction 13th

Enhancing Logical Rules Based on Self-Distillation for Docum...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Mao, Yanxu Cui, Tiehan Ding, Ying Henan Univ Sch Software Kaifeng Peoples R China Henan Inst Sci & Technol Sch Comp Sci & Technol Xinxiang Henan Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Document-level Relation Extraction (DocRE) aims to identify and extract relations between entities from entire documents. Unlike sentence-level relation extraction, document-level relation extraction requires considering cross-sentence contextual information to capture complex relations expressed across multiple sentences or paragraphs. Previous research has shown that the application of Logical Rules (LR) can improve the efficiency of relation extraction. However, identifying and extracting these logical rules require significant memory consumption, and the obtained logical rules lack specificity. To address this issue, we propose a novel approach in this paper (ELRSD (The implementation code for ELRSD can be obtained from the GitHub link: https://***/maoxuxu/elrsd.)), Based on the Self-Distillation (SD) training process, utilize Multi-head Graph Convolution Networks and Transformer synergistic learning to improve the extraction of logical rules, thereby enhancing the extraction of entity relations. We conduct comprehensive experiments on three popular benchmark datasets. The results obtained under the same experimental settings indicate that our method achieved state-of-the-art performance.

关键词： DocRE LR Graph Convolution Networks Self-Distillation

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Low-Resource Event Causality Identification With Global Consistency Constraints 13th

Low-Resource Event Causality Identification With Global Cons...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Ning, Kangyun Liu, Jian Xu, Jinan Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Event causality identification (ECI) primarily involves discerning causal relations between pairs of events within sentences. However, previous methods heavily rely on large volumes of high-quality annotated data, making them impractical in low-resource scenarios. Moreover, traditional methods often make independent predictions about event pairs, ignoring the influence between these relations, leading to incorrect predictions. We propose a low-resource ECI method with global consistency constraints to address these challenges. Our approach consists of two strategies: first, we efficiently utilize high-quality data through combined domain adaptation adversarial training and semi-supervised methods with external data sources. Second, we incorporate global consistency constraints into the training process, enhancing the model's ability to learn chain causal relations. Our method significantly improves the F1 score on the 10% Event Storyline Corpus (ESC) and 5% ESC extending the manually annotated relations within document event co-reference chains with external Causal News Corpus (CNC) and noisy causal data from Wikipedia (WNC) compared to the baseline. The addition of global consistency constraints further increases the model's prediction consistency.

关键词： Event Causality Identification Low Resources Consistency Constraints

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