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HTCSI: A Hierarchical Text Classification Method Based on Selection-Inference 13th

HTCSI: A Hierarchical Text Classification Method Based on Se...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Xu, Yiming Feng, Jianzhou Gu, Chenghan Qin, Haonan Xue, Kehan Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066000 Hebei Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Hierarchical Text Classification (HTC) is a complex task that involves categorizing text within a hierarchical labeling system. Current methodologies primarily focus on integrating hierarchical information into encoding models to achieve enhanced text representations. However, the intricate nature of the process obtaining representation often diminishes the model's interpretability, leaving researchers uncertain about the model's effective utilization of hierarchical information and the rationale behind its results. In this paper, we introduce a novel Hierarchical Text Classification method based on Selection-Inference(HTCSI) using large language models (LLMs) to enhance interpretability. Specifically, we leverage classification paths within the labeling hierarchy as cue information to guide LLM reasoning. Additionally, we develop a path selection module to identify the most probable classification paths, thereby minimizing the impact of irrelevant information on LLM reasoning. We employ the Parameter-Efficient Fine-Tuning (PEFT) method to bolster the reasoning capabilities of LLMs, thus improving the accuracy of hierarchical text classification while ensuring a more reliable reasoning process. Experiments conducted on several standard datasets demonstrate that HTCSI not only enhances credible classification reasoning but also surpasses other advanced HTC methods.

关键词： Hierarchical Text Classification Large Language Models Chain of Thought

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Integrating Syntax Tree and Graph Neural Network for Conversational Question Answering over Heterogeneous Sources 13th

Integrating Syntax Tree and Graph Neural Network for Convers...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Li, Meiwen Cai, Tianyu Wu, Lingyan Chen, Li Ju, Shenggen Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Conversational question answering (ConvQA) over heterogeneous sources leverages information from heterogeneous sources to enhance answer coverage, thereby improving the performance of ConvQA systems. Current mainstream methods rely on neural Seq2Seq models focusing on the complete conversation history. These models extract question entities and relation phrases implicit in the conversation history through sequence generation. However, numerous errors occur in extracting question entities because the conversation history contains irrelevant noise, and the sequence generation approach may lead to inaccuracies. To address this problem, we propose a model that incorporates a syntax tree and a graph neural network for ConvQA over heterogeneous sources. The model constructs a syntax tree by syntactically analyzing the user's question and conversation history. It then learns node representations and predicts node scores of the word nodes in the tree. This is based on the node's entity information and its relevance to the current question within the neighborhood. A graph neural network that includes entity-level attention is used to complete the extraction of the question entities. Experiments on the ConvMix and ConvQuestions datasets verify the overall performance improvement and validate the effectiveness of the proposed model.

关键词： Conversational Question Answering Graph Neural Network Constituency Parsing

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Improving Non-autoregressive Machine Translation with Error Exposure and Consistency Regularization 13th

Improving Non-autoregressive Machine Translation with Error ...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Chen, Xinran Duan, Sufeng Liu, Gongshen Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Being one of the IR-NAT (Iterative-refinement-based NAT) frameworks, the Conditional Masked Language Model (CMLM) adopts the mask-predict paradigm to re-predict the masked low-confidence tokens. However, CMLM suffers from the data distribution discrepancy between training and inference, where the observed tokens are generated differently in the two cases. In this paper, we address this problem with the training approaches of error exposure and consistency regularization (EECR). We construct the mixed sequences based on model prediction during training, and propose to optimize over the masked tokens under imperfect observation conditions. We also design a consistency learning method to constrain the data distribution for the masked tokens under different observing situations to narrow down the gap between training and inference. The experiments on five translation benchmarks obtains an average improvement of 0.68 and 0.40 BLEU scores compared to the base models, respectively, and our CMLMC-EECR achieves the best performance with a comparable translation quality with the Transformer. The experiments results demonstrate the effectiveness of our method.

关键词： Non-autoregressive Machine Translation Mask-predict

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TARGET: Template-Transferable Backdoor Attack Against Prompt-Based NLP Models via GPT4 13th

TARGET: Template-Transferable Backdoor Attack Against Prompt...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Tan, Zihao Chen, Qingliang Huang, Yongjian Liang, Chen Jinan Univ Dept Comp Sci Guangzhou 510632 Peoples R China Guangzhou Xuanyuan Res Inst Co Ltd Guangzhou 510006 Peoples R China Hong Kong Polytech Univ Dept Comp Hung Hom Kowloon Hong Kong Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Prompt-based learning has been widely applied in many low-resource natural language processing (NLP) tasks such as few-shot scenarios. However, this paradigm has been shown to be vulnerable to backdoor attacks. Most of the existing attack methods focus on inserting manually predefined templates as triggers in the pre-training phase to train the victim model and utilize the same triggers in the downstream task to perform inference, which tends to ignore the transferability and stealthiness of the templates. In this work, we propose a novel approach of TARGET, which is a data-independent attack method. Specifically, we first utilize GPT4 to reformulate manual templates to generate tone-strong and normal templates, and the former are injected into the model as a backdoor trigger in the pre-training phase. Then, we not only directly employ the above templates in the downstream task, but also use GPT4 to generate templates with similar tone to the above templates to carry out transferable attacks. Finally we have conducted extensive experiments on five NLP datasets and three BERT series models, with experimental results justifying that our TARGET method has better attack performance and stealthiness compared to the two-external baseline methods on direct attacks, and in addition achieves satisfactory attack capability in the unseen tone-similar templates.

关键词： Prompt-based Learning Backdoor Attack Few-shot Classification Tasks

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High-Quality Distractors Generation for Human Exam Based on Reinforcement Learning from Preference Feedback 13th

High-Quality Distractors Generation for Human Exam Based on ...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Wang, Ruofan Jiang, Yuru Tao, Yuyang Li, Mengyuan Wang, Xia Ge, Shili Beijing Informat Sci & Technol Univ Intelligent Informat Proc Inst Beijing 100192 Peoples R China Beijing Informat Sci & Technol Univ Sch Foreign Studies Beijing 100192 Peoples R China Guangdong Univ Foreign Studies Lab Language & Artificial Intelligence Guangzhou 510420 Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Distractors are incorrect answer options designed to mislead or confuse test-takers in multiple-choice reading comprehension questions. In real-world exam settings, creating distractors for English reading comprehension questions is complex and varied, with subjective and diverse evaluation standards. Developing a distractor generation technique that meets real-world requirements is a highly challenging task with significant research value. To address these challenges, we introduce DGRL (Distractors Generation based on Reinforcement Learning from preference feedback), a method using cutting-edge large language models trained through reinforcement learning to generate multiple distractors for real-world human exam. First, the distractor generation model is fine-tuned through supervised fine-tuning (SFT) on a reading comprehension question dataset. Then, using preference feedback reinforcement learning, we build and train a reward model to evaluate the quality of individual distractors. Combining the reward model with a diversity evaluation metric, we design an objective function and further train the fine-tuned model using reinforcement learning. Experiments show that the DGRL, after SFT and reinforcement learning, can generate multiple high-quality distractors that meet the real-world requirements in one go, serving as a valuable reference and aid in real-world question-setting for human exam.

关键词： distractor generation reinforcement learning reading comprehension question generation

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Modeling Comparative Logical Relation with Contrastive Learning for Text Generation 13th

Modeling Comparative Logical Relation with Contrastive Learn...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Dan, Yuhao Tian, Junfeng Zhou, Jie Yan, Ming Zhang, Ji Chen, Qin He, Liang East China Normal Univ Lab Artificial Intelligence Educ Shanghai Peoples R China ECNU Shanghai Inst Artificial Intelligence Educ Shanghai Peoples R China ECNU Sch Comp Sci & Tecluiol Shanghai Peoples R China Xiaohongshu Inc Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Data-to-Text Generation (D2T), a classic natural language generation problem, aims at producing fluent descriptions for structured input data, such as a table. Existing D2T works mainly focus on describing the superficial associative relations among entities, while ignoring the deep comparative logical relations, such as A is better than B in a certain aspect with a corresponding opinion, which is quite common in our daily life. In this paper, we introduce a new D2T task named comparative logical relation generation (CLRG). Additionally, we propose a Comparative Logic (CoLo) based text generation method, which generates texts following specific comparative logical relations with contrastive learning. Specifically, we first construct various positive and negative samples by fine-grained perturbations in entities, aspects and opinions. Then, we perform contrastive learning in the encoder layer to have a better understanding of the comparative logical relations, and integrate it in the decoder layer to guide the model to correctly generate the relations. Noting the data scarcity problem, we construct a Chinese Comparative Logical Relation Dataset (CLRD), which is a high-quality human-annotated dataset and challenging for text generation with descriptions of multiple entities and annotations on their comparative logical relations. Extensive experiments show that our method achieves impressive performance in both automatic and human evaluations.

关键词： Natural language processing Data-to-text generation Contrastive learning Dataset construction

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Classifiers Guided Controllable Text Generation for Discrete Diffusion Language Models 13th

Classifiers Guided Controllable Text Generation for Discrete...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Jiang, Hang Cai, Guoyong Li, Sihui Guilin Univ Elect Technol Guilin Peoples R China

ISBN: (纸本)9789819794362;9789819794379

The classifier-guided method is a simple yet effective technique for controllable text generation. However, its application to the discrete diffusion model, a promising non-autoregressive language model, remains unexplored. In this paper, controllable generation is achieved through multiple iterations of conditional backward diffusion, each iteration comprising two key steps: conditional prediction and masking. The conditional prediction distribution is approximated by the product of classifier probabilities and language model probabilities. Two distinct algorithms, Largest-M and gradient search, are introduced to sample from this joint distribution. Largest-M operates within the discrete token sequence space, while gradient search functions within the continuous hidden state space. Both algorithms aim to identify texts that maximize the joint probability. Experiments across four fine-grained controllable tasks demonstrate the effectiveness of the proposed algorithms, achieving success rates up to 99% on certain tasks. Furthermore, the inherent multi-modality problem in discrete diffusion models is mitigated by framing multi-modality suppression as a controllable generation task. The experiment result indicates that integrating multi-modality classifiers effectively enhances the performance of discrete diffusion models.

关键词： classifiers guided generation controllable text generation discrete diffusion language model

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Detecting Depression from Audio Data 26th

Detecting Depression from Audio Data

引用

26th International Conference on Speech and Computer

作者： Idamkina, Mary Corradini, Andrea Univ Liverpool Liverpool Merseyside England MCI Innsbruck Austria

ISBN: (纸本)9783031779602;9783031779619

AI has recently started to gain popularity in most spheres of our daily life, including healthcare. While most AI-based solutions have been proposed for physical-related medical conditions, only few of them target mental health. AI-based solutions can support the diagnosis of mental disorders from clues that a human doctor may not be able to recognize, for example the biomarkers of depression. Previous studies in the area of automatic detection of depression from different media, like audio-visual data, ECG, or transcribed speech exists. In this research, we focus on the detection of depression from audio data. We use only low-level audio features and their physical characteristics, while we do not include any semantic information carried by the speech. We performed a qualitative analysis on the selected set of features that proved to be important for recognizing depression. Such an analysis revealed the dependence between gender and the set of relevant features for depression detection. We discovered differences and similarities between sets of strong predictors between gender. Formants have shown to be important to describe the articulation level for both male and female voices. Control over the voice is a better predictor for male voices, while monotony is better described with formants for male voices and with MFCC and energy for female ones. This highlights the importance of gender inclusivity in mental healthcare and depression diagnostics in particular. Experiments carried out while running this study achieved a value of F1 of 65% for depression detection for female voices and 67% for male voices.

关键词： Machine Learning Speech Analysis Depression Detection

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On the Influence of CNN-Based Feature Learning Modules in Neural Speaker Verification Framework 26th

On the Influence of CNN-Based Feature Learning Modules in Ne...

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26th International Conference on Speech and Computer

作者： Alam, Jahangir Islam, Md Shahidul Comp Res Inst Montreal CRIM Montreal PQ Canada

ISBN: (纸本)9783031780134;9783031780141

The advent of Convolutional Neural Networks (CNNs) has revolutionized various fields of machine learning, including automatic speaker verification (ASV). In this paper, we explore the influence of CNN-based feature learning modules on the performance and robustness of neural ASV systems. To this end, we employ a neural speaker embedding framework consisting of CNN-based features extraction module, which is connected in cascade with a frame-level network, consisting of a Time Delay Neural Network (TDNN)-Long Short Term Memory (LSTM) hybrid network and a fully TDNN network in a cascade arrangement. The CNN-based feature extraction modules considered are: (i) 1D CNN, (ii) vanilla 2D-CNN, and (iii) Selective Kernel Attention (SKA) integrated 2D-CNN. In order to aggregate speaker information within an utterance-level context, we use Multi-Level Attentive Statistics Pooling, which captures local statistics as context and leverages the complementarity of different network modules. Experimental results on the Voxceleb dataset show that the frequency- and channel-wise SKA integrated 2D-CNN-based feature extraction module significantly outperforms the other considered feature extraction modules.

关键词： Speaker verification CNN Hybrid network TDNN-LSTM Cosine scoring VoxCeleb

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Speech Encryption Scheme Based on Chaotic Memristor Neural Network and S-Box 13th

Speech Encryption Scheme Based on Chaotic Memristor Neural N...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhao, Dawei Yu, Hui Chen, Chuan Li, Lixiang Mi, Ling Qilu Univ Technol Shandong Comp Sci Ctr Key Lab Comp Power Network & Informat Secur Minist EducShandong Acad Sci Jinan 250353 Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Ind Network & Informat Syst Jinan 250014 Peoples R China Quan Cheng Lab Jinan 250013 Peoples R China Beijing Univ Posts & Teleconununicat Informat Secur Ctr State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Qilu Univ Technol Sch Math & Stat Shandong Acad Sci Jinan 250353 Peoples R China

ISBN: (纸本)9789819794393;9789819794409

In this study, we propose a novel speech encryption scheme based on a chaotic memristor neural network (MNN) and S-box. Firstly, we consider a magnetic-controlled memristor model and a new MNN model, and explore the chaotic behaviors of the new MNN under different memristor strengths. By iterating the new chaotic MNN, we generate an S-box through bit AND and modulo operations. Subsequently, the original speech is converted into a one-dimensional array, which is split into an integer part array and a fractional part array. The integer part array of the above one-dimensional array is converted into a binary matrix, which is separated into two binary matrices. These two binary matrices undergo XOR operations with two chaotic sequences and a sequence generated by the S-box. We convert the obtained matrix into a new array, and perform scrambling operation on the new array by using another chaotic sequence. Then, the encrypted speech can be obtained. Finally, the proposed encryption scheme is evaluated through experimental simulations and security analyses, demonstrating its high level of security.

关键词： speech encryption memristor neural network S-box chaotic sequences

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