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

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Ruffle&Riley: Insights from Designing and Evaluating a Large Language Model-Based Conversational Tutoring System 25th

Ruffle&Riley: Insights from Designing and Evaluating a Large...

引用

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

作者： Schmucker, Robin Xia, Meng Azaria, Amos Mitchell, Tom Carnegie Mellon Univ Pittsburgh PA 15213 USA Texas A&M Univ College Stn TX 77843 USA Ariel Univ IL-4070000 Ariel Israel

ISBN: (纸本)9783031643019;9783031643026

Conversational tutoring systems (CTSs) offer learning experiences through interactions based on natural language. They are recognized for promoting cognitive engagement and improving learning outcomes, especially in reasoning tasks. Nonetheless, the cost associated with authoring CTS content is a major obstacle to widespread adoption and to research on effective instructional design. In this paper, we discuss and evaluate a novel type of CTS that leverages recent advances in large language models (LLMs) in two ways: First, the system enables AIassisted content authoring by inducing an easily editable tutoring script automatically from a lesson text. Second, the system automates the script orchestration in a learning-by-teaching format via two LLM-based agents (Ruffle&Riley) acting as a student and a professor. The system allows for free-form conversations that follow the ITS-typical inner and outer loop structure. We evaluate Ruffle&Riley's ability to support biology lessons in two between-subject online user studies (N = 200) comparing the system to simpler QA chatbots and reading activity. Analyzing system usage patterns, pre/post-test scores and user experience surveys, we find that Ruffle&Riley users report high levels of engagement, understanding and perceive the offered support as helpful. Even though Ruffle&Riley users require more time to complete the activity, we did not find significant differences in short-term learning gains over the reading activity. Our system architecture and user study provide various insights for designers of future CTSs. We further open-source our system to support ongoing research on effective instructional design of LLM-based learning technologies.

关键词： conversational tutoring systems intelligent tutoring systems authoring tools conversation analysis large language models

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The Effect of Working Memory Capacity on the Figurative Language Processing of Chinese Second Language Learners 24th

The Effect of Working Memory Capacity on the Figurative Lang...

引用

24th Chinese Lexical Semantics Workshop (CLSW)

作者： Duan, Xun Chai, Xingsan Beijing Language & Culture Univ Inst Educ Policy & Evaluat Int Students Xueyuan Rd 15 Beijing 100083 Peoples R China

ISBN: (纸本)9789819705825;9789819705832

This study used a 3 (working memory capacity (WMC): high, medium, low) x3 (sentence type: metaphor, simile, metonymy) mixed design to investigate the impact of WMC on the comprehension of Figurative sentences by Chinese L2 (Second language) learners, as well as the cognitive representation and differences in processing metaphor, simile, and metonymy. The results of the study revealed the following: 1) Chinese L2 learners exhibited faster reaction time (RT) for metaphor sentences compared to simile and metonymy, and faster RT for simile compared to metonymy. The comprehension accuracy (ACC) for metaphor sentences was higher than that for simile and metonymy, and the comprehension ACC for simile was higher than that for metonymy. 2) The RT and ACC of metaphor, simile, and metonymy sentences were influenced by individual WMC, with higher WMC associated with faster processing speed and higher ACC. This study revealed that metaphor, simile, and metonymy processing require different cognitive resources. The comprehension of these sentences involves the comparison, interaction, and matching of semantic features between the source and target domains, which necessitates WMC for storage and integration, ultimately completing the cognitive mapping process.

关键词： Chinese Second Language Processing Metaphor Simile Metonymy Working memory capacity

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Enhancing Speech Emotion Recognition Using Transfer Learning from Speaker Embeddings 27th

Enhancing Speech Emotion Recognition Using Transfer Learning...

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27th International Conference on Text, Speech, and Dialogue (TSD)

作者： Jakubec, Maros Jarina, Roman Lieskovska, Eva Kasak, Peter Spisiak, Michal Univ Zilina Fac Elect Engn & Informat Technol Univ 8215-1 Zilina 01026 Slovakia

ISBN: (纸本)9783031705656;9783031705663

Understanding and identifying emotions from speech is a key challenge in automatic Speech Emotion Recognition (SER). Speech carries a variety of information about speaker's emotional state or contextual emotions, but the lack of large and diverse emotional datasets makes it hard to apply advanced deep learning models for development of realiable and robust SER systems. Our study introduces a methodology that uses transfer learning and data augmentation to improve SER systems' ability to classify emotional states accurately. Specifically, we focus on enhancing and assessing the performance of x-vector and r-vector speaker embedding models for SER task through pretraining the models on a large amount of speaker-labeled data following by fine-tuning on downstream emotional dataset. Testing the proposed approach on IEMOCAP and CREMA-D datasets shows notable increment in SER accuracy and thus usefulness of such cross-task transfer learning. Using transfer learning and data augmentation, our approach notably improved SER performance, achieving above 74% and 80% of accuracy on the IEMOCAP, and CREMA-D datasets, respectively.

关键词： speech emotion recognition IEMOCAP CREMA-D transfer learning speaker embeddings affective computing

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An Analysis of One-Dimensional Adjectives from the Perspective of Lexical Semantic Typology 24th

An Analysis of One-Dimensional Adjectives from the Perspecti...

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24th Chinese Lexical Semantics Workshop (CLSW)

作者： Kim, Chaeri Shanghai Normal Univ Sch Humanities & Social Sci Dept Chinese Language & Literature Shanghai Peoples R China

ISBN: (纸本)9789819705825;9789819705832

This paper examines the meanings and usages of one-dimensional spatial dimension adjectives in 13 languages using the theoretical framework and research methods of MLexT. Past studies have shown that 'HIGH/LOW', 'DEEP/SHALLOW', 'FAR/NEAR', and are different from 'LONG/SHORT', and they are not linear adjectives. Furthermore, 'FAR/NEAR' cannot be used to describe the shape of objects. Consequently, scholars rarely explore the associations between 'HIGH/LOW', 'DEEP/SHALLOW', 'FAR/NEAR', and 'LONGSHORT' in more detail. However, in specific use cases, they may intersect when describing one-dimensional point-to-point distances. Therefore, the author examines how 'LONG/SHORT', 'HIGH/LOW', 'DEEP/SHALLOW', and 'FAR/NEAR' are used, with a focus on the differences between those spatial dimension adjectives for one-dimensional space in basic meanings, which are highlighted in this paper. The objective of this study is to assist second language acquisition learners in accurately understanding the distinctions between spatial dimension adjectives as used and their native languages, thereby facilitating cross-linguistic language acquisition.

关键词： Lexical Typology Spatial Dimension Adjectives Basic Meanings Semantic Maps

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VerAs: Verify Then Assess STEM Lab Reports 25th

VerAs: Verify Then Assess STEM Lab Reports

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

作者： Atil, Berk Karizaki, Mahsa Sheikhi Passonneau, Rebecca J. Penn State Univ University Pk PA 16801 USA

ISBN: (纸本)9783031643019;9783031643026

With an increasing focus in STEM education on critical thinking skills, science writing plays an ever more important role. A recently published dataset of two sets of college level lab reports from an inquiry-based physics curriculum relies on analytic assessment rubrics that utilize multiple dimensions, specifying subject matter knowledge and general components of good explanations. Each analytic dimension is assessed on a 6-point scale, to provide detailed feedback to students that can help them improve their science writing skills. Manual assessment can be slow, and difficult to calibrate for consistency across all students in large enrollment courses with many sections. While much work exists on automated assessment of open-ended questions in STEM subjects, there has been far less work on long-form writing such as lab reports. We present an end-to-end neural architecture that has separate verifier and assessment modules, inspired by approaches to Open Domain Question Answering (OpenQA). VerAs first verifies whether a report contains any content relevant to a given rubric dimension, and if so, assesses the relevant sentences. On the lab reports, VerAs outperforms multiple baselines based on OpenQA systems or Automated Essay Scoring (AES). VerAs also performs well on an analytic rubric for middle school physics essays.

关键词： Automated Assessment Lab Reports Analytic Rubrics

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Effects of Training Strategies and the Amount of Speech Data on the Quality of Speech Synthesis 27th

Effects of Training Strategies and the Amount of Speech Data...

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27th International Conference on Text, Speech, and Dialogue (TSD)

作者： Vladar, Lukas Matousek, Jindrich Univ West Bohemia Pilsen Fac Appl Sci Dept Cybernet Plzen Czech Republic Univ West Bohemia Pilsen Fac Appl Sci New Technol Informat Soc Plzen Czech Republic

ISBN: (纸本)9783031705656;9783031705663

During the development of a speech synthesizer, we often face a lack of training data. This paper describes how the amount of data used to train a speech synthesizer affects the quality of the final synthetic speech. To answer this question, we trained multiple VITS synthesizers using different amounts of training data and we compared them using listening tests and the MCD objective measure. Furthermore, we compared three training strategies: training a speech synthesizer from scratch, fine-tuning a single-speaker model and fine-tuning a multi-speaker model.

关键词： Fine-tuning Speech synthesis Training data VITS

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Combining Constraint-Based and Imperative Programming in MABS for More Reliable Modelling 24th

Combining Constraint-Based and Imperative Programming in MAB...

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24th International Workshop on Multi-Agent-Based Simulation (MABS)

作者： Edmonds, Bruce Polhill, J. Gareth Manchester Metropolitan Univ Ctr Policy Modelling Manchester Lancs England James Hutton Inst Aberdeen Scotland

ISBN: (纸本)9783031610332;9783031610349

We argue for a combination of declarative/constraint and imperative programming approaches for MABS: a declarative layer that specified the ontology, assumptions, types, internal and checks for a simulation and the imperative code that satisfied the statements of the declarative layer - instantiating the behaviours. Such a system would be a generalisation of common elements of existing simulations. The two layers would be separately developed and communicated but work together. Using such a system one might: (a) start by importing an ontology of entities that have been previously agreed within a field, (b) work with domain experts to implement declarative statements that reflect what is known about the system, (c) develop the implementation starting with declarative internal checks and the outlines of the implementation, (d) slowly add imperative statements to fill in details, (e) finally when the simulation has been completely verified, the declarative layer could be switched off to allow faster exploration. This would ensure for a more reliable simulation and ensure its consistency with common ontologies etc. It would facilitate: joining models together with fewer mistakes, comparing models, provide enhanced and flexible error checking, make modules more reusable, allow for rapid prototyping, support the automation of modelling tools/add-ons, and allow the selective exploration of all possible behaviours of a sub-model using constraint programming techniques. Examples are given of previous work that moves in this direction.

关键词： Constraint programming declarative programming imperative programming Multi-Agent-Based Simulation MABS model joining model comparison internal checks unit tests strong typing

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Fine-Tuning a Large Language Model with Reinforcement Learning for Educational Question Generation 25th

Fine-Tuning a Large Language Model with Reinforcement Learni...

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

作者： Lamsiyah, Salima El Mahdaouy, Abdelkader Nourbakhsh, Aria Schommer, Christoph Univ Luxembourg Fac Sci Technol & Med Dept Comp Sci Esch Sur Alzette Luxembourg Mohammed VI Polytech Univ Coll Comp Ben Guerir Morocco

ISBN: (纸本)9783031643019;9783031643026

Educational Natural Language Generation (EduQG) aims to automatically generate educational questions from textual content, which is crucial for the expansion of online education. Prior research in EduQG has predominantly relied on cross-entropy loss for training, which can lead to issues such as exposure bias and inconsistencies between training and testing metrics. To mitigate this issue, we propose a reinforcement learning (RL) based large language model (LLM) for educational question generation. In particular, we fine-tune the Google FLAN-T5 model using a mixed objective function that combines cross-entropy and RL losses to ensure the generation of questions that are syntactically and semantically accurate. The experimental results on the SciQ question generation dataset show that the proposed method is competitive with current state-of-the-art systems in terms of predictive performance and linguistic quality.

关键词： Educational Question Generation Large Language Model Google FLAN-T5 Reinforcement Learning Self-Critical Sequence Training

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Research on the Structure of Pediatric Epilepsy Electronic Medical Records Based on Transfer Learning 24th

Research on the Structure of Pediatric Epilepsy Electronic M...

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24th Chinese Lexical Semantics Workshop (CLSW)

作者： Song, Yu Wu, Pengcheng Dai, Dongming Zhang, Kunli Zhang, Chenghao Zhang, Hengxing Liu, Xiaomei Li, Jie Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Henan Peoples R China Kunming Childrens Hosp Kunming 650032 Yunnan Peoples R China Zhengzhou Zhongye Technol Co Ltd Zhengzhou 450001 Henan Peoples R China

ISBN: (纸本)9789819705856;9789819705863

Medical institutions commonly utilize electronic medical records (EMRs) to document patients' medical conditions, which contain valuable medical information. However, EMRs often consist of semi-structured or unstructured data, presenting significant challenges in processing and analysis. This paper addresses the needs of subsequent tasks such as assisting diagnosis and information extraction (IE). We present the process of structuring electronic health records based on pediatric epilepsy cases, including EMRs structural analysis, data preprocessing, named entity recognition (NER), and data integration. NER is a critical step in the processing pipeline. Therefore, we propose a pediatric epilepsy EMRs entity recognition model, T-RoBERTa-BiLSTM-CRF, based on transfer learning. After training on the constructed pediatric epilepsy dataset, T-RoBERTa-BiLSTM-CRF is used to perform NER tasks in EMRs structuring, achieving an F1 score of 79.25%. Identified medical entities are integrated at the text level to achieve EMRs structuring aligned with the needs of subsequent tasks.

关键词： Structured Electronic Medical Records Pediatric Epilepsy Transfer Learning Named Entity Recognition

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Joint-Average Mean and Variance Feature Matching (JAMVFM) Semi-supervised GAN with Additional-Objective Training Function for Intent Detection 27th

Joint-Average Mean and Variance Feature Matching (JAMVFM) Se...

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27th International Conference on Text, Speech, and Dialogue (TSD)

作者： Kumar, Ankit Georges, Munir Exzellenzstiftung Ingolstadter Wissensch Ignaz Ko Ingolstadt Germany Tech Hsch Ingolstadt AImot Bavaria Ingolstadt Germany UPES Sch Comp Sci Dehra Dun Uttarakhand India

ISBN: (纸本)9783031705656;9783031705663

Intent detection, a crucial task in spoken language understanding (SLU) systems, often faces challenges due to the requirement for extensive labeled training data. However, the process of collecting such data is both resource-intensive and time-consuming. To mitigate these challenges, leveraging Semi-Supervised Generative Adversarial Networks (SS-GANs) presents a promising strategy. By employing SS-GANs, it becomes possible to fine-tune pre-trained transformer models like BERT using unlabeled data, thereby improving intent detection performance without the need for extensive labeled datasets. This article introduces a novel approach called Joint-Average Mean and Variance Feature Matching GAN (JAMVFM-GAN) with the additional objective to improve SS-GAN learning. By incorporating information about both mean and variance during latent feature learning, JAMVFM-GAN aims to more accurately capture the underlying data manifold. Except JAMVFM, we proposed an additional loss function for discriminator to enhance its discriminative capabilities. Experimental results demonstrate that JAMVFM-GAN along with additional objective function outperforms traditional SS-GAN in Intent Detection tasks. The results indicate the maximum relative improvement of 3.84%, 3.85%, and 1.04% over the baseline on the ATIS, SLURP, and SNIPS datasets, respectively.

关键词： Natural Language Understanding NLP GAN SLU

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