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arXiv 2403.07581cs.CL
大语言模型还是小模型？基于大语言模型文本增强的人格检测模型
LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
Beijing Institute of Technology（北京理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Personality detection aims to detect one's personality traits underlying in social media posts. One challenge of this task is the scarcity of ground-truth personality traits which are collected from self-report questionnaires. Most existing methods learn post features directly by fine-tuning the pre-trained language models under the supervision of limited personality labels. This leads to inferior quality of post features and consequently affects the performance. In addition, they treat personality traits as one-hot classification labels, overlooking the semantic information within them. In this paper, we propose a large language model (LLM) based text augmentation enhanced personality detection model, which distills the LLM's knowledge to enhance the small model for personality detection, even when the LLM fails in this task. Specifically, we enable LLM to generate post analyses (augmentations) from the aspects of semantic, sentiment, and linguistic, which are critical for personality detection. By using contrastive learning to pull them together in the embedding space, the post encoder can better capture the psycho-linguistic information within the post representations, thus improving personality detection. Furthermore, we utilize the LLM to enrich the information of personality labels for enhancing the detection performance. Experimental results on the benchmark datasets demonstrate that our model outperforms the state-of-the-art methods on personality detection.
This paper describes our proposed method for the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Personality Prediction (PER) and Reactivity Index Prediction (IRI). In this paper, we adopt the prompt-based learning method with the pre-trained language model to accomplish these tasks. Specifically, the prompt is designed to provide knowledge of the extra personalized information for enhancing the pre-trained model. Data augmentation and model ensemble are adopted for obtaining better results. Moreover, we also provided the onli…
Augmenting Dialog with Think-Aloud Utterances for Modeling Individual Personality Traits by LLM
Institute of Science Tokyo（东京科学大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Fujitsu Limited（富士通株式会社）
Modeling human personality is important for several AI challenges, from the engineering of artificial psychotherapists to the design of persona bots. However, the field of computational personality analysis heavily relies on labeled data, which may be expensive, difficult or impossible to get. This problem is amplified when dealing with rare personality types or disorders (e.g., the anti-social psychopathic personality disorder). In this context, we developed a text-based data augmentation approach for human personality (PEDANT). PEDANT doesn't rely on the common type of labeled data but on th…
Beyond BFI: The CSI for Enhanced Reliability and Validity in Evaluating LLM Personality Traits
University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Microsoft Research Asia（微软亚洲研究院）；North Carolina State University（北卡罗来纳州立大学）
Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study
A2I Lab, School of Computing, Wichita State University（A2I实验室，计算学院，威斯康星州立大学）；EIS Lab, College of Engineering, University of Nevada, Reno（EIS实验室，工程学院，内华达大学，里诺）
Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives
School of Computer Science and Technology, Dalian University of Technology, Dalian, China（计算机科学与技术学院，大连理工大学，大连，中国）；Criminal Investigation Police University of China, Shenyang, China（中国刑事侦查警察大学，沈阳，中国）；School of Foreign Languages, Dalian University of Technology, Dalian, China（外语学院，大连理工大学，大连，中国）
Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end…
EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling
School of Electronics and Computer Science, University of Southampton（电子工程与计算机科学学院，南安普顿大学）；School of Electronic Engineering and Computer Science, Queen Mary University of London（电子工程与计算机科学学院，伦敦女王学院）；Department of Computational Biology, Mohamed bin Zayed University of Artificial Intelligence（计算生物学系，Mohamed bin Zayed人工智能大学）
National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of Sciences（中国科学院自动化研究所模式识别国家实验室）；School of Artificial IntelligenceUniversity of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis). Experimental results on two benchmark datasets EventStoryLine corpus and Causal-TimeBank show that 1) KnowDis can augment avai…
The task of verbalization of RDF triples has known a growth in popularity due to the rising ubiquity of Knowledge Bases (KBs). The formalism of RDF triples is a simple and efficient way to store facts at a large scale. However, its abstract representation makes it difficult for humans to interpret. For this purpose, the WebNLG challenge aims at promoting automated RDF-to-text generation. We propose to leverage pre-trainings from augmented data with the Transformer model using a data augmentation strategy. Our experiment results show a minimum relative increases of 3.73%, 126.05% and 88.16% in …
University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Microsoft Dynamics 365 AI（微软Dynamics 365人工智能）
Data augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, designing label-preserving transformations for text data tends to be more challenging. In this paper, we propose a novel data augmentation framework dubbed CoDA, which synthesizes diverse and informative augmented examples by integrating multiple transformations organically. Moreover, a contrastive regularization objective is introduced to capture the global relationship among all the data samples. A momentum encoder a…
Lahore University of Management Sciences（拉合尔管理科学大学）
Paraphrase detection is an important task in text analytics with numerous applications such as plagiarism detection, duplicate question identification, and enhanced customer support helpdesks. Deep models have been proposed for representing and classifying paraphrases. These models, however, require large quantities of human-labeled data, which is expensive to obtain. In this work, we present a data augmentation strategy and a multi-cascaded model for improved paraphrase detection in short texts. Our data augmentation strategy considers the notions of paraphrases and non-paraphrases as binary …
Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Sapienza University of Rome（罗马萨皮恩扎大学）；Babelscape
We introduce EUREKA, an ensemble-based approach for performing automatic euphemism detection. We (1) identify and correct potentially mislabelled rows in the dataset, (2) curate an expanded corpus called EuphAug, (3) leverage model representations of Potentially Euphemistic Terms (PETs), and (4) explore using representations of semantically close sentences to aid in classification. Using our augmented dataset and kNN-based methods, EUREKA was able to achieve state-of-the-art results on the public leaderboard of the Euphemism Detection Shared Task, ranking first with a macro F1 score of 0.881. …
Data augmentation (DA) aims to generate constrained and diversified data to improve classifiers in Low-Resource Classification (LRC). Previous studies mostly use a fine-tuned Language Model (LM) to strengthen the constraints but ignore the fact that the potential of diversity could improve the effectiveness of generated data. In LRC, strong constraints but weak diversity in DA result in the poor generalization ability of classifiers. To address this dilemma, we propose a {D}iversity-{E}nhanced and {C}onstraints-\{R}elaxed {A}ugmentation (DECRA). Our DECRA has two essential components on top of…
TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classification
School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC研究院）；Shaoxing K3i Technology Co. Ltd（绍兴K3i科技有限公司）；State Key Laboratory of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation
Huazhong University of Science and Technology（华中科技大学）；Nanchang University（南昌大学）；Zhejiang University（浙江大学）；Zhejiang Lab（之江实验室）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Detecting offensive language on social media is an important task. The ICWSM-2020 Data Challenge Task 2 is aimed at identifying offensive content using a crowd-sourced dataset containing 100k labelled tweets. The dataset, however, suffers from class imbalance, where certain labels are extremely rare compared with other classes (e.g, the hateful class is only 5% of the data). In this work, we present Dager (Data Augmenter), a generation-based data augmentation method, that improves the performance of classification on imbalanced and low-resource data such as the offensive language dataset. Dage…
Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size. We propose methods for automatically improving datasets by viewing them as graphs with expected semantic properties. We construct a paraphrase graph from the provided sentence pair labels, and create an augmented dataset by directly inferring labels from the original sentence pairs using a transitivity property. We use structural balance theory to identify likely mislabelings in the graph, and flip their labels. We evaluate our methods on paraphrase models trained using these datasets starting from a …
Direct speech-to-speech translation (S2ST) models suffer from data scarcity issues as there exists little parallel S2ST data, compared to the amount of data available for conventional cascaded systems that consist of automatic speech recognition (ASR), machine translation (MT), and text-to-speech (TTS) synthesis. In this work, we explore self-supervised pre-training with unlabeled speech data and data augmentation to tackle this issue. We take advantage of a recently proposed speech-to-unit translation (S2UT) framework that encodes target speech into discrete representations, and transfer pre-…
Revisiting Interpolation Augmentation for Speech-to-Text Generation
College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；ByteDance（字节跳动）；NiuTrans Research（NiuTrans研究院）
University College Dublin（都柏林大学学院）；Insight Centre for Data Analytics（洞察数据分析中心）
Leveraging unlabelled data through weak or distant supervision is a compelling approach to developing more effective text classification models. This paper proposes a simple but effective data augmentation method, which leverages the idea of pseudo-labelling to select samples from noisy distant supervision annotation datasets. The result shows that the proposed method improves the accuracy of biased news detection models.
Counterfactual fairness methods address the question: How would the prediction change if the sensitive identity attributes referenced in the text instance were different? These methods are entirely based on generating counterfactuals for the given training and test set instances. Counterfactual instances are commonly prepared by replacing sensitive identity terms, i.e., the identity terms present in the instance are replaced with other identity terms that fall under the same sensitive category. Therefore, the efficacy of these methods depends heavily on the quality and comprehensiveness of ide…
We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombination. We demonstrate their effectiveness on generative commonsense reasoning, a.k.a. the CommonGen task, through experiments using both BART and T5 models. Through extensive automatic and human evaluation, we show that SAPPHIRE noticeably improves model performance. An in-depth qualitative analysis illustrates that SAPPHIRE effectively addresses many issues of the baseline model generations, including lack of commonsens…
