1 |
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
YACLC: A Chinese Learner Corpus with Multidimensional Annotation ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Alternated Training with Synthetic and Authentic Data for Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
CPM-2: Large-scale Cost-effective Pre-trained Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Automatic Construction of Sememe Knowledge Bases via Dictionaries ...
|
|
|
|
Abstract:
A sememe is defined as the minimum semantic unit in linguistics. Sememe knowledge bases (SKBs), which comprise words annotated with sememes, enable sememes to be applied to natural language processing. So far a large body of research has showcased the unique advantages and effectiveness of SKBs in various tasks. However, most languages have no SKBs, and manual construction of SKBs is time-consuming and labor-intensive. To tackle this challenge, we propose a simple and fully automatic method of building an SKB via an existing dictionary. We use this method to build an English SKB and a French SKB, and conduct comprehensive evaluations from both intrinsic and extrinsic perspectives. Experimental results demonstrate that the automatically built English SKB is even superior to HowNet, the most widely used SKB that takes decades to build manually. And both the English and French SKBs can bring obvious performance enhancement in multiple downstream tasks. All the code and data of this paper (except the copyrighted ... : Accepted by Findings of ACL at ACL-IJCNLP 2021. Camera-ready version ...
|
|
Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://dx.doi.org/10.48550/arxiv.2105.12585 https://arxiv.org/abs/2105.12585
|
|
BASE
|
|
Hide details
|
|
6 |
Sub-Character Tokenization for Chinese Pretrained Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Transfer Learning for Sequence Generation: from Single-source to Multi-source ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
OpenAttack: An Open-source Textual Adversarial Attack Toolkit ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Lexical Sememe Prediction using Dictionary Definitions by Capturing Local Semantic Correspondence ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Improving Back-Translation with Uncertainty-based Confidence Estimation ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Modeling Semantic Compositionality with Sememe Knowledge ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
OpenHowNet: An Open Sememe-based Lexical Knowledge Base ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Neural Machine Translation with Explicit Phrase Alignment ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|