Search Results for author:
Found 11 papers, 5 papers with code
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis leverages multimodal signals to detect the sentiment of a speaker.
LA3: Efficient Label-Aware AutoAugment
Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural network training.
Search-Map-Search: A Frame Selection Paradigm for Action Recognition
To overcome the limitations of existing methods, we propose a Search-Map-Search learning paradigm which combines the advantages of heuristic search and supervised learning to select the best combination of frames from a video as one entity.
CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering
To address this issue, we propose CEIL, a novel Classification-Enhanced Iterative Learning framework for short text clustering, which aims at generally promoting the clustering performance by introducing a classification objective to iteratively improve feature representations.
GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation
Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user instructions. Although the synthesis performance is fascinating, one significant limitation of current image generation models is their insufficient ability to generate text coherently within images, particularly for complex glyph structures like Chinese characters.
Optical Character Recognition (OCR)
Text-to-Image Generation
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
The learned GUR captures the overall preferences and characteristics of a user and thus can be used to augment the behavior data and improve recommendations in any single domain in which the user is involved.
LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization
Language model pre-training based on large corpora has achieved tremendous success in terms of constructing enriched contextual representations and has led to significant performance gains on a diverse range of Natural Language Understanding (NLU) tasks.
Verdi: Quality Estimation and Error Detection for Bilingual Corpora
Verdi adopts two word predictors to enable diverse features to be extracted from a pair of sentences for subsequent quality estimation, including a transformer-based neural machine translation (NMT) model and a pre-trained cross-lingual language model (XLM).
QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications
In this paper, we present QBSUM, a high-quality large-scale dataset consisting of 49, 000+ data samples for the task of Chinese query-based document summarization.
Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models
Specifically, we propose a data selection framework based on Deterministic Actor-Critic, in which a critic network predicts the expected change of model performance due to a certain sample, while an actor network learns to select the best sample out of a random batch of samples presented to it.
Learning to Generate Questions by Learning What not to Generate
In CGC-QG, we design a multi-task labeling strategy to identify whether a question word should be copied from the input passage or be generated instead, guiding the model to learn the accurate boundaries between copying and generation.
