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Found 27 papers, 10 papers with code
GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection
For Subtask B, framed as a multi-label classification problem, we utilize various improved multi-label cross-entropy loss functions and analyze the performance of our method.
Astrea: A MOE-based Visual Understanding Model with Progressive Alignment
Vision-Language Models (VLMs) based on Mixture-of-Experts (MoE) architectures have emerged as a pivotal paradigm in multimodal understanding, offering a powerful framework for integrating visual and linguistic information.
Unveiling the Capabilities of Large Language Models in Detecting Offensive Language with Annotation Disagreement
Large Language Models (LLMs) have become essential for offensive language detection, yet their ability to handle annotation disagreement remains underexplored.
Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition
Humor recognition aims to identify whether a specific speaker's text is humorous.
STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detection
Furthermore, the lack of research on Chinese hateful slang poses a significant challenge.
Towards Comprehensive Detection of Chinese Harmful Memes
Harmful memes have proliferated on the Chinese Internet, while research on detecting Chinese harmful memes significantly lags behind due to the absence of reliable datasets and effective detectors.
PclGPT: A Large Language Model for Patronizing and Condescending Language Detection
Disclaimer: Samples in this paper may be harmful and cause discomfort!
Towards Patronizing and Condescending Language in Chinese Videos: A Multimodal Dataset and Detector
Patronizing and Condescending Language (PCL) is a form of discriminatory toxic speech targeting vulnerable groups, threatening both online and offline safety.
Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detection
Experimental results on two widely-used personality detection datasets demonstrate the effectiveness of the MvP model and the benefits of automatically analyzing user posts from diverse perspectives for textual personality detection.
Take its Essence, Discard its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect
Current methods of toxic language detection (TLD) typically rely on specific tokens to conduct decisions, which makes them suffer from lexical bias, leading to inferior performance and generalization.
Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives
This limitation hinders a comprehensive understanding of individuals' personalities, as both stable traits and dynamic states are vital.
Taiyi-Diffusion-XL: Advancing Bilingual Text-to-Image Generation with Large Vision-Language Model Support
Recent advancements in text-to-image models have significantly enhanced image generation capabilities, yet a notable gap of open-source models persists in bilingual or Chinese language support.
Unified Lattice Graph Fusion for Chinese Named Entity Recognition
To solve this issue, we propose a Unified Lattice Graph Fusion (ULGF) approach for Chinese NER.
Chinese Named Entity Recognition
named-entity-recognition
+2
Unlocking the Potential of Large Language Models for Explainable Recommendations
In this study, we propose LLMXRec, a simple yet effective two-stage explainable recommendation framework aimed at further boosting the explanation quality by employing LLMs.
Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects
During the instruction fine-tuning stage, we introduce semantic-aware visual feature extraction, a crucial method that enables the model to extract informative features from concrete visual objects.
Ranked #1 on
Image Captioning
on nocaps entire
iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design
However, there are few specialized models in certain domains, such as interior design, which is attributed to the complex textual descriptions and detailed visual elements inherent in design, alongside the necessity for adaptable resolution.
Ziya2: Data-centric Learning is All LLMs Need
Although many such issues are addressed along the line of research on LLMs, an important yet practical limitation is that many studies overly pursue enlarging model sizes without comprehensively analyzing and optimizing the use of pre-training data in their learning process, as well as appropriate organization and leveraging of such data in training LLMs under cost-effective settings.
Ziya-Visual: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning
Recent advancements enlarge the capabilities of large language models (LLMs) in zero-shot image-to-text generation and understanding by integrating multi-modal inputs.
Hate Speech Detection via Dual Contrastive Learning
Our framework jointly optimizes the self-supervised and the supervised contrastive learning loss for capturing span-level information beyond the token-level emotional semantics used in existing models, particularly detecting speech containing abusive and insulting words.
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective
We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis.
Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks
In addition, it is crucial to introduce lexical knowledge to detect the toxicity of posts, which has been a challenge for researchers.
Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence
We hope that this project will be the foundation of Chinese cognitive intelligence.
Flat Multi-modal Interaction Transformer for Named Entity Recognition
Then, we transform the fine-grained semantic representation of the vision and text into a unified lattice structure and design a novel relative position encoding to match different modalities in Transformer.
Unified BERT for Few-shot Natural Language Understanding
Even as pre-trained language models share a semantic encoder, natural language understanding suffers from a diversity of output schemas.
SiNER: A Large Dataset for Sindhi Named Entity Recognition
We introduce the SiNER: a named entity recognition (NER) dataset for low-resourced Sindhi language with quality baselines.
Word Embedding based New Corpus for Low-resourced Language: Sindhi
Our intrinsic evaluation results demonstrate the high quality of our generated Sindhi word embeddings using SG, CBoW, and GloVe as compare to SdfastText word representations.
Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection
Response selection plays an important role in fully automated dialogue systems.
