Search Results for author:
Found 9 papers, 4 papers with code
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for language understanding.
Self-Explaining Structures Improve NLP Models
The proposed model comes with the following merits: (1) span weights make the model self-explainable and do not require an additional probing model for interpretation; (2) the proposed model is general and can be adapted to any existing deep learning structures in NLP; (3) the weight associated with each text span provides direct importance scores for higher-level text units such as phrases and sentences.
Neural Semi-supervised Learning for Text Classification Under Large-Scale Pretraining
The goal of semi-supervised learning is to utilize the unlabeled, in-domain dataset U to improve models trained on the labeled dataset D. Under the context of large-scale language-model (LM) pretraining, how we can make the best use of U is poorly understood: is semi-supervised learning still beneficial with the presence of large-scale pretraining?
Ranked #1000000000 on
Text Classification
on IMDb
Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries
The difficulty of generating coherent long texts lies in the fact that existing models overwhelmingly focus on predicting local words, and cannot make high level plans on what to generate or capture the high-level discourse dependencies between chunks of texts.
Pair the Dots: Jointly Examining Training History and Test Stimuli for Model Interpretability
Any prediction from a model is made by a combination of learning history and test stimuli.
Large-scale Pretraining for Neural Machine Translation with Tens of Billions of Sentence Pairs
In this paper, we investigate the problem of training neural machine translation (NMT) systems with a dataset of more than 40 billion bilingual sentence pairs, which is larger than the largest dataset to date by orders of magnitude.
Query-Based Named Entity Recognition
In this paper, we propose a new strategy for the task of named entity recognition (NER).
Entity Extraction using GAN
Machine Reading Comprehension
+3
Entity-Relation Extraction as Multi-Turn Question Answering
In this paper, we propose a new paradigm for the task of entity-relation extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
IcoRating: A Deep-Learning System for Scam ICO Identification
For the best setting, the proposed system is able to identify scam ICO projects with 0. 83 precision.
