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FineCSDA: Boosting Document-Level Event Argument Extraction with Fine-Grained Data Augmentation 13th

FineCSDA: Boosting Document-Level Event Argument Extraction ...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Chen, Junxiu Long, Kehan Li, Shasha Tang, Jintao Wang, Ting Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Document-level event argument extraction (EAE) aims to extract structured event information from a document. In this paper, we first identify the issue of the long-tail distribution of argument roles in document-level event extraction datasets. This makes it challenging to extract low-frequency argument roles, despite their potential inclusion of crucial semantic information that enriches event descriptions. However, the manual annotation for these rare argument roles is costly. In contrast, generating synthetic data with Large Language Models (LLMs) is cost-effective and has demonstrated the ability to produce fluent and coherent text. Current research only conducts augmentation at the sample level, overlooking these rare argument roles, and is limited to sentence-level data augmentation, struggling to ensure the integrity of event arguments. Context-level augmentation enables more precise and fine-grained supplementation of arguments, and it poses greater challenges in quality control. In our work, we propose FineCSDA, a method integrating fine-grained data augmentation at both sample and context levels, which contains two novel and effective modules. The Sample-level Data Augmentation Module (SDAM) generates document-level samples of rare event types using the LLMs to increase the number of rare argument roles belonging to these rare event types. The Context-level Data Augmentation Module (CDAM) supplements argument roles that are inherently rare within common event types by utilizing LLMs to generate missing arguments and their corresponding contexts. Experimental results demonstrate that FineCSDA is capable of generating high-quality textual data and effectively improving the extraction performance of rare arguments.

关键词： Event argument extraction Data augmentation Rare argument roles

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MultiAICL: Multi-task Tuning for Augmented In-Context Learning in Text Style Transfer 13th

MultiAICL: Multi-task Tuning for Augmented In-Context Learni...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhu, Linan Zhou, Zehai Chen, Xiangfan Guo, Xiaolei Kong, Xiangjie Zhejiang Univ Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9789819794362;9789819794379

In-context learning (ICL) enhances the performance of large language models (LLMs) across various natural language process (NLP) tasks by simply demonstrating a few-shot of examples or instructions during inference. However, ICL still encounters significant challenges on the text style transfer (TST) tasks, which require high levels of model reasoning. The existing ICL ability has not been further developed because LLMs lack the process of training and learning in context. To address these issues, we introduce Multi-Task Tuning for Augmented In-Context Learning (MultiAICL), a framework designed to enhance model ICL ability by simulating LLM's supervised fine-tuning steps. MultiAICL contains three main components: firstly, we construct example instructions for multiple tasks from the text corpus, where these examples are in the form of text-label pairs;secondly, we propose the Multi-Task Tuning (MTT) module, which tunes the model by randomly combining example instructions;and thirdly, we design the Augmented In-context Learning (AICL) module, which incorporates different tasks into example templates to infer the model. MultiAICL improves the ICL ability of LLMs while maintaining their generalization across multiple tasks, thus encouraging models to generate high-quality text. Extensive experiments show that MultiAICL achieves excellent results on all 6 TST tasks, even outperforming larger LLMs. The code and data are available at https://***/fuz999/NLPCC-2024- MultiAICL.

关键词： In-Context Learning Text Style Transfer Large Language Models

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Emotion Cause Extraction in Conversations with Response Graphing 13th

Emotion Cause Extraction in Conversations with Response Grap...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Tian, Yuanhe Cheng, Pengsen Xia, Fei Liu, Jiayong Zhang, Yongdong Song, Yan Univ Washington Seattle WA 98195 USA Univ Sci & Technol China Hefei Peoples R China Sichuan Univ Chengdu Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Emotion cause extraction in conversations (ECEC) is an important task in emotion analysis, aiming to extract the text spans, i.e., parts in utterances, that reflect the causes of a certain type of emotion embedded in a target utterance in the conversation history. Since conversations are interactions between individuals, where one responds to the content from other participants, it is crucial to identify the potential responsive relations among utterances so as to build a response graph over the utterances to illustrate the underlying structure of the conversation and thus contribute to ECEC. However, existing studies, including the ones using large language models (LLMs), focus little on modeling the responsive relations when they perform the task. In this paper, we propose to improve ECEC with LLM and response graphing over utterances, where responsive relation decoding (RRD) process is proposed to identify responsive relations among utterances by learning from the responsive relations automatically extracted by off-the-shelf toolkits. Attentive relational graph convolutional networks (A-RGCN) are further applied to the response graph to weigh and leverage the utterances, where the encoded information is used as prompt to instruct the LLM to perform ECEC. Experimental results and analyses on English benchmark datasets demonstrate the effectiveness of the proposed approach, where our approach achieves state-of-the-art results on the datasets (Our code is available at https://***/synlp/ECEC-RG).

关键词： Emotion Cause Extraction Responsive Relation Attentive Relational Graph Convolutional Networks

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Autogenerated MQM Data for Quality Estimation Based on Sequence Labeling 13th

Autogenerated MQM Data for Quality Estimation Based on Seque...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Ge, Wei Lei, Tao Gong, Zhengxian Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Quality Estimation (QE) for machine translation aims to evaluate translation quality without reference translations. The WMT 2022 competition introduced a novel QE task focused on predicting MQM labels, i.e. human annotations for the locations, categories, and severity of errors in machine translations under the Multidimensional Quality Metrics framework. However, such annotation is costly and results in data scarcity. Inspired by named entity recognition tasks, we leverage a sequence labeling model to fully exploit the temporal dependencies and contextual information in MQM data. The model is trained to automatically simulate human annotations by identifying error locations and severity in machine translations. Experimental results demonstrate that the pseudo MQM data generated by this sequence labeling model can significantly enhance the performance of quality estimation models.

关键词： Quality Estimation Sequence Labeling Data Augmentation

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Evaluating the Fidelity of Image Captioning via Weighted Boolean Question Answering 13th

Evaluating the Fidelity of Image Captioning via Weighted Boo...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Wang, Kaixuan Li, Shasha Tang, Jintao Long, Kehan Miao, Yongzhu Chen, Fangda Wang, Ting Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Image captioning evaluation is of great significance in guiding caption generation, practically valuable evaluation requires fine-grained evaluation metrics. However, most of the current metrics are only capable of measuring the overall quality of the caption, which means users are not informed of any details about the caption's defects when given a low score. As fidelity is one of the quality criteria of image captioning and has a great impact on the application, we proposed WBQA, a metric that focuses on evaluating the fidelity of the caption by Weighted Boolean Question Answering. WBQA can display which part of captions is unfaithful because natural language is used to express questions to check whether the caption matches the image. Experiments show that our metric is excellent for measuring fidelity (from 0.31 and 0.34 to 0.52 and 0.58 with Pearson correlation coefficient) and achieves state-of-the-art on multiple image captioning evaluation datasets.

关键词： Image captioning evaluation Fidelity Question answering

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Generation of Scientific Literature Surveys Based on Large Language Models (LLM) and Multi-Agent Systems (MAS) 13th

Generation of Scientific Literature Surveys Based on Large L...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Qi, Ruihua Li, Weilong Lyu, Haobo Dalian Univ Foreign Languages Sch Software Engn Dalian 116044 Peoples R China Dalian Univ Foreign Languages Res Ctr Language Intelligence Dalian 116044 Peoples R China

ISBN: (纸本)9789819794423;9789819794430

With the rapid increase in the number and speed of scientific publications, researchers face significant time pressure when conducting literature reviews. This paper presents an automatic literature review generation method leveraging large language models (LLMs) and multi-agent systems (MAS). By designing multiple agent roles, including reference parsing, analysis, generation, and integration agents-this method fully utilizes the natural language processing capabilities of LLMs and the collaborative strengths of MAS to produce high-quality literature reviews. In the NLPCC2024 evaluation task, our method excelled in multiple automatic evaluation metrics (such as SoftHeadingRecall and ROUGE) and manual evaluations, showcasing its great potential for practical applications.

关键词： Large Language Models Multi-Agent Systems Literature Review Generation Natural Language Processing Collaborative Generation

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DDR-ECC: Dictionary-Driven Chinese ASR Entity Correction with Controllable Decoding 13th

DDR-ECC: Dictionary-Driven Chinese ASR Entity Correction wit...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Wang, Dejun Peking Univ Beijing Peoples R China

ISBN: (纸本)9789819794393;9789819794409

ASR error correction is an effective method for optimizing ASR recognition results. The current mainstream ASR error correction system is mainly based on the encoder-decoder structure, by learning the mapping of incorrect text to correct text in a data-driven approach. Although this approach showed good results in ASR error correction, it mainly solves the spelling errors that occur at high frequencies, while struggling with low-frequency ASR entity errors, especially those unseen entities in the training set. Another downside of the end-to-end structure is that it may produce unreasonable correction results during decoding due to the lack of controllable mechanism. Therefore, how to design a controllable ASR error correction decoding strategy becomes a critical problem. In this paper, we introduce DDR-ECC-a Dictionary-Driven Entity Correction model with Controllable Decoding for Chinese ASR. We first retrieve relevant entities from an entity dictionary based on the similarity of pinyin and phonetic information, and then fuse them with ASR text for joint encoding. Next, a controlled decoding strategy is proposed to generate more reasonable decoding results. Experimental results on three publicly available datasets, Aishell, MAGICDATA(SMALL) and MAGICDATA(OOV), demonstrate the effectiveness of our proposed method on ASR error correction, especially on ASR entity error correction. To be specific, compared to a solid encoder-decoder baseline(SC_BART) on the three datasets, the average SER and CER are decreased by 6.10% and 0.54% respectively, while the average F1 score of entity correction are improved by 9.06%. Finally, we define a new metric TD (tolerance degree) for measuring unreasonable decoding results, from which our proposed controllable decoding strategy can further improve the quality of error entity correction. Our code and data will be available at https://***/SeaEagleI/DDR-ECC.

关键词： ASR entity correction Fuzzy match Controllable decoding

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LaiDA: Linguistics-Aware In-Context Learning with Data Augmentation for Metaphor Components Identification 13th

LaiDA: Linguistics-Aware In-Context Learning with Data Augme...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Liu, Hongde He, Chenyuan Meng, Feiyang Niu, Changyong Jia, Yuxiang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Metaphor Components Identification (MCI) contributes to enhancing machine understanding of metaphors, thereby advancing downstream natural language processing tasks. However, the complexity, diversity, and dependency on context and background knowledge pose significant challenges for MCI. Large language models (LLMs) offer new avenues for accurate comprehension of complex natural language texts due to their strong semantic analysis and extensive commonsense knowledge. In this research, a new LLM-based framework is proposed, named Linguistics-aware In-context Learning with Data Augmentation (LaiDA). Specifically, ChatGPT and supervised fine-tuning are utilized to tailor a high-quality dataset. LaiDA incorporates a simile dataset for pre-training. A graph attention network encoder generates linguistically rich feature representations to retrieve similar examples. Subsequently, LLM is fine-tuned with prompts that integrate linguistically similar examples. LaiDA ranked 2nd in Subtask 2 of NLPCC2024 Shared Task 9, demonstrating its effectiveness. Code and data are available at https://***/WXLJZ/LaiDA.

关键词： Metaphor Components Identification Linguistics-aware In-context Learning Data Augmentation Large Language Models

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Topic-Awared Contrastive Learning for Incoming Fake News Detection in News Streams 13th

Topic-Awared Contrastive Learning for Incoming Fake News Det...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhang, Yongcheng Xiang, Changpeng Ren, Kai Wei, Xiaomei Huazhong Agr Univ Coll Informat Wuhan Peoples R China South Cent Minzu Univ Coll Comp Sci Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Agr Wuhan Peoples R China

ISBN: (纸本)9789819794423;9789819794430

The prevalence of social media streams has transformed them into essential sources of real-time information on various topics. However, the simultaneous spread of fake news poses a significant challenge to the authenticity of information dissemination. Traditional fake news detection methods, which focus on analyzing individual articles in news streams, often neglect the topic-based correlations crucial for identifying misinformation patterns. This oversight hinders their ability to effectively detect fake news, especially when it relates to similar topics with a tendency for falsehoods. To mitigate this limitation, we propose the Topic-Awared Contrastive Learning Framework (TCLF), an efficient mechanism for analyzing topic-based correlations. The TCLF comprises two key components: the dynamic correlation mapping module, which employs incremental clustering to categorize news articles into familiar and unfamiliar topic domains, and the contrastive correlation learning module, which applies contrastive learning techniques to identify fake news across diverse topics. Our framework better detects fake news in news streams, outperforming existing state-of-the-art methods.

关键词： Fake News Detection Incremental Clustering Contrastive Learning

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RAG and Few-Shot Prompting in Emotional Text Generation 26th

RAG and Few-Shot Prompting in Emotional Text Generation

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26th International Conference on Speech and Computer

作者： Vologina, Elizaveta Matveeva, Anastasiia Makhnytka, Olesia Matveev, Yuri Burambayeva, Nursaule ITMO Univ St Petersburg 197101 Russia LN Gumilyov Eurasian Natl Univ Astana 010000 Kazakhstan

ISBN: (纸本)9783031780134;9783031780141

This scientific article describes a modification of the Retrieval Augmented Generation (RAG) method aimed at increasing the emotional content of model responses without supervised fine-tuning. In this model, RAG is not used to expand text generation beyond language knowledge but serves as a key tool for forming prompts based on several examples during a dialogue. The retrieval block of the model finds texts with suitable emotional contexts, which are later used to create prompts. This approach allows the model to generate responses rich in emotions that correspond to the context without the need for strict tuning. An investigation of this kind is being conducted for the first time for Russian-speaking dialogue agents. The developed approach enables enhancing emotional expressiveness and the quality of responses. Applying this method has shown a significant improvement in SSA values by 44% compared to baseline models trained on a dataset with emotional annotations, as well as a 25% enhancement in automatic evaluation based on a classifier.

关键词： Retrieval-augmented generation Emotional text generation Dialogue agents

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