DE eng

Search in the Catalogues and Directories

Page: 1 2
Hits 1 – 20 of 36

1
Pairwise embedding for event coreference resolution
Hu, Yanda. - 2022
BASE
Show details
2
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training ...
BASE
Show details
3
Joint Detection and Coreference Resolution of Entities and Events with Document-level Context Aggregation ...
BASE
Show details
4
Stage-wise Fine-tuning for Graph-to-Text Generation ...
BASE
Show details
5
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction ...
BASE
Show details
6
Lifelong Event Detection with Knowledge Transfer ...
BASE
Show details
7
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection ...
BASE
Show details
8
Learning Shared Semantic Space for Speech-to-Text Translation ...
BASE
Show details
9
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference ...
BASE
Show details
10
Event-Centric Natural Language Processing ...
BASE
Show details
11
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction ...
BASE
Show details
12
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension ...
BASE
Show details
13
Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation ...
BASE
Show details
14
Coreference by Appearance: Visually Grounded Event Coreference Resolution ...
BASE
Show details
15
Learning speech embeddings for speaker adaptation and speech understanding
Sari, Leda. - 2021
BASE
Show details
16
Text Classification Using Label Names Only: A Language Model Self-Training Approach ...
Abstract: Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. Humans can perform classification without seeing any labeled examples but only based on a small set of words describing the categories to be classified. In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents. We use pre-trained neural language models both as general linguistic knowledge sources for category understanding and as representation learning models for document classification. Our method (1) associates semantically related words with the label names, (2) finds category-indicative words and trains the model to predict their implied categories, and (3) generalizes the model via self-training. We show that our model achieves around 90% accuracy on four benchmark datasets including topic and sentiment ... : EMNLP 2020. (Code: https://github.com/yumeng5/LOTClass) ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://dx.doi.org/10.48550/arxiv.2010.07245
https://arxiv.org/abs/2010.07245
BASE
Hide details
17
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation ...
Wang, Qingyun; Li, Manling; Wang, Xuan. - : arXiv, 2020
BASE
Show details
18
Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation ...
Mao, Yuning; Ren, Xiang; Ji, Heng. - : arXiv, 2020
BASE
Show details
19
Learning from Lexical Perturbations for Consistent Visual Question Answering ...
BASE
Show details
20
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding ...
Shen, Jiaming; Ji, Heng; Han, Jiawei. - : arXiv, 2020
BASE
Show details

Page: 1 2

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
36
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern