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Found 21 papers, 7 papers with code
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex
Semantic parsing is a technique aimed at constructing a structured representation of the meaning of a natural-language question.
Active Learning for Multilingual Semantic Parser
Current multilingual semantic parsing (MSP) datasets are almost all collected by translating the utterances in the existing datasets from the resource-rich language to the target language.
Let's Negotiate! A Survey of Negotiation Dialogue Systems
Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements.
Strong Instance Segmentation Pipeline for MMSports Challenge
The goal of ACM MMSports2022 DeepSportRadar Instance Segmentation Challenge is to tackle the segmentation of individual humans including players, coaches and referees on a basketball court.
Real-time End-to-End Video Text Spotter with Contrastive Representation Learning
Our contributions are three-fold: 1) CoText simultaneously address the three tasks (e. g., text detection, tracking, recognition) in a real-time end-to-end trainable framework.
The Third Place Solution for CVPR2022 AVA Accessibility Vision and Autonomy Challenge
Finally, our approach achieves 63. 008\%AP@0. 50:0. 95 on the test set of CVPR2022 AVA Challenge.
The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation
The referring video object segmentation task (RVOS) aims to segment object instances in a given video referred by a language expression in all video frames.
A Zipf's Law-Driven Method for Extracting Entities from Documents
Entity extraction is critical to the intelligent development of various domains and the construction of knowledge agents.
Paraphrasing Techniques for Maritime QA system
In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain.
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for task-specific natural language generation with none or a handful of task-specific labeled examples.
Contrastive Learning of Semantic and Visual Representations for Text Tracking
Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video.
A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer
Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data.
Pretrained Language Model in Continual Learning: A Comparative Study
In this paper, we thoroughly compare the continual learning performance over the combination of 5 PLMs and 4 veins of CL methods on 3 benchmarks in 2 typical incremental settings.
Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers
We conduct extensive experiments to study the research problems involved in continual semantic parsing and demonstrate that a neural semantic parser trained with TotalRecall achieves superior performance than the one trained directly with the SOTA continual learning algorithms and achieve a 3-6 times speedup compared to re-training from scratch.
AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation
In our evaluations, the method outperforms all the state-of-the-art image retrieval algorithms on some out-of-domain image datasets.
Representation Learning for Weakly Supervised Relation Extraction
The experiments have demonstrated that this type of feature, combine with the traditional hand-crafted features, could improve the performance of the logistic classification model for relation extraction, especially on the classification of relations with only minor training instances.
On Robustness of Neural Semantic Parsers
In this paper, we provide the empirical study on the robustness of semantic parsers in the presence of adversarial attacks.
Few-Shot Semantic Parsing for New Predicates
In this work, we investigate the problems of semantic parsing in a few-shot learning setting.
Context Dependent Semantic Parsing: A Survey
Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations.
Asymmetric Correntropy for Robust Adaptive Filtering
Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables.
