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

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Large Language Models With Holistically Thought Could Be Better Doctors 13th

Large Language Models With Holistically Thought Could Be Bet...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Weng, Yixuan Li, Bin Xia, Fei Zhu, Minjun Sun, Bin He, Shizhu Liu, Shengping Li, Kang Li, Shutao Zhao, Jun Chinese Acad Sci IA Lab Cognit & Decis Intelligence Complex Syst Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China Unisound Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9789819794331;9789819794348

The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care. Despite the success of large language models (LLMs) in complex reasoning tasks in various fields, such as mathematics, logic, and commonsense QA, they still need to improve with the increased complexity and specialization of the medical field. This is because medical CQA tasks require not only strong medical reasoning, but also the ability to think broadly and deeply. In this paper, to address these challenges in medical CQA tasks that need to be considered and understood in many aspects, we propose the Holistically Thought (HoT) method, which is designed to guide the LLMs to perform the diffused and focused thinking for generating high-quality medical responses. The proposed HoT method has been evaluated in three different medical CQA datasets containing English and Chinese languages. The extensive experimental results show that our method can produce more correct, professional, and considerate answers than several SOTA methods, manifesting its effectiveness.

关键词： Large Language Model Medical Conversational QA Holistically Thought

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ProSide: Knowledge Projector and Sideway for Pre-trained Language Models 13th

ProSide: Knowledge Projector and Sideway for Pre-trained Lan...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： He, Chaofan Lu, Gewei Shen, Liping Shanghai Jiao Tong Univ AI Inst Dept Comp Sci & Engn X LANCE LabMoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Research indicates that incorporating external knowledge into pre-trained language models (PLMs) can enhance their performance on knowledge-driven downstream tasks. However, most approaches either require retraining the model or hardly maintain complete information of knowledge graphs (KGs). In this paper, we introduce a simple but effective ProSide module for PLMs, which includes two components: Knowledge Projector and Knowledge Sideway. Knowledge Projector transforms knowledge representation from entity embedding in KG space to semantic space, while Knowledge Sideway retains complete KG information in sideway modules through pre-training. Our ProSide can accommodate different frozen language models, without the need for retraining them. Results on three common knowledge-driven tasks demonstrate that our ProSide method enhances model performance and reaches state-of-the-art level. Additionally, we provide further analysis and case studies to illustrate the mechanism from the perspective of representation space. The code and models are publicly available at Github (Code and models are publicly available at https://***/hcffffff/ProSide).

关键词： Knowledge Graph Pre-trained Language Models Parameter Efficient Fine-tuning Method

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Enhancing Cross-Lingual Named Entity Recognition via Dual Contrastive Learning Based on MRC Framework 13th

Enhancing Cross-Lingual Named Entity Recognition via Dual Co...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhuo, Aiqing Shi, Kunli Gu, Jinghang Qian, Longhua Zhoul, Guodong Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China Hong Kong Polytech Univ Dept Chinese & Bilingual Studies Hong Kong 999077 Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Cross-lingual Named Entity Recognition (NER) has recently become a research hotspot because it can transfer knowledge from high-resource languages to low-resource languages, thus meeting the challenge of data scarcity in low-resource languages. Most of the current model-transfer methods rely on directly using multilingual models to represent text yet ignoring the cross-lingual word alignment information in multi-lingual models, and also neglecting the utilization of prior knowledge. We propose a dual contrastive learning method based on machine reading comprehension (MRC) framework, by combining Query Contrastive Learning (QCL) and Translation Word Contrastive Learning (TWCL), to mitigate above problems. Specifically, we utilize QCL to design contrastive objectives for different query templates to enhance the representation ability of prior knowledge. In addition, we utilize TWCL to help the model capture the word alignment relationship between the source language and target language via a pseudo-parallel corpus. We conducted extensive experiments on 4 different datasets and the experimental results demonstrate the effectiveness of our method.

关键词： Cross-lingual Named Entity Recognition Contrastive Learning Machine Reading Comprehension

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Bread: A Hybrid Approach for Instruction Data Mining Through Balanced Retrieval and Dynamic Data Sampling 13th

Bread: A Hybrid Approach for Instruction Data Mining Through...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhuang, Xinlin Mao, Xin Jiang, Yuan-Hao Wu, Hongyi Zhao, Shangqing Cai, Li Liu, Shu Chen, Yang Song, Yuxiang Jia, Chenghao Zhou, Yuhao Lan, Man East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Nanyang Technol Univ Singapore Singapore Guizhou Univ Guiyang Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Recent advancements in Instruction Tuning (IT) have shown promise for aligning Large Language Models (LLMs) with users' intentions, yet its efficacy is often compromised by dependence on high-quality datasets. Previous works have concentrated on the aggregation or production of huge IT datasets through human labor or significant costintensive LLM APIs, which lacks adequate mechanisms to guarantee the quality of the resulting data. Moreover, training on such amount of IT data is both time-consuming and costly. To address these issues, we present Bread (Instruction Mining through Balanced REtrieval And Dynamic Data Sampling), a novel approach designed to minimize the requisite volume of IT data. Bread uses a two-stage strategy combining balanced retrieval and dynamic sampling to focus on data diversity and quality, offering a cost-saving solution without relying on any specific LLMs. Experimental results suggest that Bread outperforms baselines and shows great flexibility across various IT datasets and LLMs, thereby marking a step forward in efficient Instruction Tuning. Our code is available at https://***/mihara-bot/Bread.

关键词： Large Language Models Instruction Tuning Data Selection

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Understanding and Improving Low-Resource Neural Machine Translation with Shallow Features 13th

Understanding and Improving Low-Resource Neural Machine Tran...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Sun, Yanming Liu, Xuebo Wong, Derek F. Lin, Yuchu Li, Bei Zhan, Runzhe Chao, Lidia S. Zhang, Min Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau Peoples R China Harbin Inst Technol Shenzhen Peoples R China DeepTranx Zhuhai Peoples R China Northeastern Univ Shenyang Peoples R China

ISBN: (纸本)9789819794362;9789819794379

During neural machine translation (NMT) tasks, we observe that despite the common assumption that increasing encoder depth leads to improved performance, this effect is less pronounced in low-resource scenarios and can even exacerbate overfitting issues. Our comparative analysis between NMT models equipped with shallow and deep encoders reveals that the majority of sentences are more effectively translated by a shallow encoder. Further analysis indicates that these sentences tend to be simpler, suggesting the shallow encoder's ability to capture unique features in simple text. Building on these insights, we introduce NATASHA, a novel training strategy that enhances the capabilities of deep models in low-resource Neural mAchine TrAnslation with SHallow feAtures extracted through sequence-level knowledge distillation from the shallow model. Experimental results on five low-resource NMT tasks show that NATASHA consistently improves over strong baselines by at least 1 BLEU point. Furthermore, when combined with other regularization methods, NATASHA achieves leading-edge performance on the IWSLT14 De-En translation task. Further analysis of our method's effectiveness reveals that integrating shallow features reduces the complexity of the training data, facilitating deep models in learning patterns and features within simple text during the early stages of training. This unleashes the deep model's ability to learn representations of low-frequency words and long sentences, thereby enhancing overall performance.

关键词： Low-Resource Neural Machine Translation Encoder Depth Shallow Features

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Part-of-Speech and Confusion-Set Constrained Language Model for Vietnamese Spelling Correction Corpus Construction 13th

Part-of-Speech and Confusion-Set Constrained Language Model ...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Li, Ying Chen, Xin Liu, Xiao Dong, Ling Yu, Zhengtao Mao, Cunli Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming Yunnan Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Supervised spelling error correction models have achieved outstanding performances on rich-source languages. However, these models are difficult to directly apply to Vietnamese spelling correction due to the corpus scarcity. To address this issue, we first construct a basic high-quality Vietnamese Spelling Correction (ViSC) corpus via automatic speech recognition (ASR) generation and human annotation. Then, we propose a part-of-speech and confusion-set double-constrained method to mimic the practical error distribution and use them as external knowledge to guide the large language models (LLMs) to construct diverse pseudo data. Finally, we exploit pseudo corpora to pre-train and ViSC corpus to fine-tune spelling error correction models. Experiments on the benchmark dataset show that our proposed corpus construction method consistently outperforms various baselines, leading to state-of-the-art results on all Vietnamese-specific pre-trained language model-enhanced spelling correction models. Detailed analysis demonstrates that part-of-speech and confusion-set are complementary and significant in controlling a stable and diverse corpus generation. In-depth comparison experiments reveal that the proper utilization of pseudo corpus is essential for improving Vietnamese spelling error correction. Besides, we release our codes and constructed corpus at https://***/DarkFanta3y/VSEC_corpus to facilitate future research.

关键词： Vietnamse spelling correction Double-constrained corpus construction Large language model

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Utilizing Speaker Models and Topic Markers for Emotion Recognition in Dialogues 26th

Utilizing Speaker Models and Topic Markers for Emotion Recog...

引用

26th International Conference on Speech and Computer

作者： Makhnytkina, Olesia Matveev, Yuri Zubakov, Alexander Matveev, Anton ITMO Univ St Petersburg Russia

ISBN: (纸本)9783031780134;9783031780141

In modern speech emotion recognition (SER), improving upon state-of-the-art systems requires increasing sophistication of extracting discriminative information from training data. The key aspects of our research include utilizing automatic speaker verification (ASV) and topic detection models to enhance emotion recognition in multimodal (audio and text) dialogues. We conduct a comparative analysis of modern SER models and experiment with various models for text (RoBERTa, TodKat, COSMIC) and audio (WavLM, Wav2Vec2) modalities. We also employ attention-based fusion of the modalities and a BiGRU-based classification approach. The results show that our proposed model, which combines RoBERTa, TodKat, Wav2Vec2, WavLM, and attention-based fusion, achieves an F1-score of 0.657, outperforming the state-of-the-art systems using the same selection of modalities. This performance is only surpassed by models that utilize additional modalities beyond audio and text. We demonstrate the effectiveness of our approach in improving SER by leveraging advanced techniques for extracting discriminative information from multimodal training data. The incorporation of ASV and topic detection, along with the novel fusion and classification methods, contributes to the enhanced performance of our proposed model compared to the existing state-of-the-art SER systems.

关键词： Speech emotion recognition Multimodality Modality fusion Dialogues ASR ASV Language model Topic detection

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Resolving Unseen Rumors with Retrieval-Augmented Large Language Models 13th

Resolving Unseen Rumors with Retrieval-Augmented Large Langu...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Chen, Lei Wei, Zhongyu Fudan Univ Sch Data Sci Shanghai Peoples R China Fudan Univ Res Inst Intelligent & Complex Syst Shanghai Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Social media has become the primary source of information for individuals, yet much of this information remains unverified. The rise of generative artificial intelligence has further accelerated the creation of unverified content. Adaptive rumor resolution systems are imperative for maintaining information integrity and public trust. Traditional methods have relied on encoder-based frameworks to enhance rumor representation and propagation characteristics. However, these models are often small in scale and lack generalizability for unforeseen events. Recent advances in Large Language Models show promise but are unreliable in discerning truth from falsehood. Our work leverages LLMs by creating a testbed for predicting unprecedented rumors and designing a retrieval-augmented framework that integrates historical knowledge and collective intelligence. Experiments on two real-world datasets demonstrate the effectiveness of our proposed framework.

关键词： Rumor Detection Retrieval-Augmented LLMs

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LLM Assists Hypothesis Generation and Testing for Deliberative Questions 13th

LLM Assists Hypothesis Generation and Testing for Deliberati...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Wang, Fuchun Zhou, Xian Hu, Wenpeng Luo, Zhunchen Luo, Wei Bai, Xiaoying PLA Acad Mil Sci Informat Res Ctr Mil Sci Beijing 100080 Peoples R China

ISBN: (纸本)9789819794331;9789819794348

In the present era, the proliferation and widespread adoption of Large Language Models (LLMs) have substantially elevated the capacity for understanding and reasoning in various textual tasks. Yet, there is a current shortage of effective methodologies for tackling Deliberative Questions, which require thoughtful consideration and may encompass evolving scenarios. To bridge this gap, this paper introduces a systematic approach that employs relevant impact analysis to build a cognitive framework, whose results guide LLMs to assist generate hypotheses and testing, known as Abductive Reasoning, for addressing deliberative questions, with the goal of fostering a deliberative thinking mindset. Our comprehensive evaluation on deliberative questions gathered from Reddit and Zhihu showed that pipeline generates superior answers in 65.5% and 73.6% of the cases on our datasets in ChatGPT and QwenMAX view, particularly excelling with an 88.3% dominance rate in addressing reasoning-type questions and and 84.4% in predictive-type questions.

关键词： Deliberative Question Large Language Model Abductive Reasoning

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Chinese Grammar Correction Model Based on Semantic Enhancement and Feedback Mechanism 13th

Chinese Grammar Correction Model Based on Semantic Enhanceme...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhang, Zhujian Zhao, Peiyu Liu, Bo Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

ISBN: (纸本)9789819794393;9789819794409

The Chinese grammar detection and correction system can automatically identify error positions and correct erroneous characters. Currently, there are still challenges in the research, such as the limited size of publicly available Chinese error correction datasets, the diversity of Chinese grammar error forms, and the difficulty in representing the distribution of grammar errors. Additionally, pre-trained Chinese language models lack the ability to differentiate between similar characters or words, affecting the accuracy of Chinese sentence detection and correction. In this paper, we propose a model based on grammar enhancement and feedback mechanism. During the model training phase, the Pairwise Character Interaction (PCI) module is used to enhance the grammatical representation of the text encoder. It employs various gating mechanisms based on character pairs to highlight the semantic features of grammatical errors at erroneous character positions. Furthermore, during the testing phase, our model (PCIFM) utilizes a feedback mechanism to edit and iteratively correct erroneous results. The proposed model was evaluated on publicly available CGED datasets, achieving the highest detection F1 scores on the test sets of CGED 2017, CGED 2018, and CGED 2020, respectively.

关键词： Chinese Text Error Correction Pairwise Character Interaction Feedback Mechanism

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