416 citations · 1.6k across the 85 of their papers we have counts for
8 papers · 1 filter
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System
Ze Yang, Linjun Shou, Ming Gong +2
Deep pre-training and fine-tuning models (such as BERT and OpenAI GPT) have demonstrated excellent results in question answering areas. However, due to the sheer amount of model pa…
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base
Tao Shen, Xiubo Geng, Tao Qin +5
We consider the problem of conversational question answering over a large-scale knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent neural sema…
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning
Yibo Sun, Duyu Tang, Nan Duan +4
Neural semantic parsing has achieved impressive results in recent years, yet its success relies on the availability of large amounts of supervised data. Our goal is to learn a neur…
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks
Haoyang Huang, Yaobo Liang, Nan Duan +4
We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data…
Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering
Shangwen Lv, Daya Guo, Jingjing Xu +7
Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evid…
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training
Gen Li, Nan Duan, Yuejian Fang +3
We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner. Borrow ideas from cross-lingual pre-trained mo…