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Found 12 papers, 7 papers with code
Language Resource Efficient Learning for Captioning
Our analysis of caption models with SC loss shows that the performance degradation is caused by the increasingly noisy estimation of reward and baseline with fewer language resources.
Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs
The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention.
Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering
To this end, we propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements most informative for KGQA.
Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction
We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens.
From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment
Our approach captures the unified correlation pattern of two kinds of information between entities, and explicitly models the fine-grained interaction between original entity information.
An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering
Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP).
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation
By fine-tuning the pre-trained language model with these template orders, our approach improves the performance of quad prediction, and outperforms state-of-the-art methods significantly in low-resource settings.
Ranked #3 on
Aspect-Based Sentiment Analysis (ABSA)
on ACOS
MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion
Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically.
Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances
In this work, we define the training instances with the same question type but different answers as \textit{superficially similar instances}, and attribute the language priors to the confusion of VQA model on such instances.
Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification
The goal of few-shot fine-grained image classification is to recognize rarely seen fine-grained objects in the query set, given only a few samples of this class in the support set.
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification
Cross-domain sentiment classification has drawn much attention in recent years.
Improving Captioning for Low-Resource Languages by Cycle Consistency
Improving the captioning performance on low-resource languages by leveraging English caption datasets has received increasing research interest in recent years.
