Search Results for author:
Found 22 papers, 10 papers with code
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing
Thanks to the strong representation learning capability of deep learning, especially pre-training techniques with language model loss, dependency parsing has achieved great performance boost in the in-domain scenario with abundant labeled training data for target domains.
A Coarse-to-Fine Labeling Framework for Joint Word Segmentation, POS Tagging, and Constituent Parsing
In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i. e., phrase, subphrase, word, subword).
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
In this paper, we propose a FLexible And Generalizable RL framework for VNE, named FlagVNE.
Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning
The proposed framework consists of a large language model (LLM), a diffusion-based image generator, and a series of visual rewards by design.
Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously.
Multi-modal Siamese Network for Entity Alignment
To deal with that problem, in this paper, we propose a novel Multi-modal Siamese Network for Entity Alignment (MSNEA) to align entities in different MMKGs, in which multi-modal knowledge could be comprehensively leveraged by the exploitation of inter-modal effect.
Ranked #7 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v1
(using extra training data)
Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition
Recent years have witnessed the improving performance of Chinese Named Entity Recognition (NER) from proposing new frameworks or incorporating word lexicons.
Chinese Named Entity Recognition
named-entity-recognition
+3
Multi-Modal Knowledge Graph Construction and Application: A Survey
In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques.
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference.
Ranked #3 on
Document-level Event Extraction
on ChFinAnn
Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model
Specifically, during neural network training, we naturally model the noise samples in each batch following a hypergeometric distribution parameterized by the noise-rate.
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models
This paper studies how to automatically generate a natural language text that describes the facts in knowledge graph (KG).
An In-depth Study on Internal Structure of Chinese Words
Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information.
Knowledge-based Review Generation by Coherence Enhanced Text Planning
For global coherence, we design a hierarchical self-attentive architecture with both subgraph- and node-level attention to enhance the correlations between subgraphs.
Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking
Entity linking (EL) for the rapidly growing short text (e. g. search queries and news titles) is critical to industrial applications.
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models
Complex node interactions are common in knowledge graphs (KGs), and these interactions can be considered as contextualized knowledge exists in the topological structure of KGs.
Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network
First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence.
FastLR: Non-Autoregressive Lipreading Model with Integrate-and-Fire
NAR lipreading is a challenging task that has many difficulties: 1) the discrepancy of sequence lengths between source and target makes it difficult to estimate the length of the output sequence; 2) the conditionally independent behavior of NAR generation lacks the correlation across time which leads to a poor approximation of target distribution; 3) the feature representation ability of encoder can be weak due to lack of effective alignment mechanism; and 4) the removal of AR language model exacerbates the inherent ambiguity problem of lipreading.
A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land?
Specifically, we erase name regularity, mention coverage and context diversity respectively from the benchmarks, in order to explore their impact on the generalization ability of models.
Integrating Graph Contextualized Knowledge into Pre-trained Language Models
Complex node interactions are common in knowledge graphs, and these interactions also contain rich knowledge information.
