Daxin Jiang

Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks

2019 · arXiv (Cornell University)

Haoyang Huang, Yaobo Liang, Nan Duan, Ming Gong, Linjun Shou, Daxin Jiang, Ming Zhou. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language …

Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base

2019 · arXiv (Cornell University)

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 semantic parsing based approaches usually decompose the task into several …

DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding

2020

Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT. State-of-the-art approaches typically follow the "retrieve and read" pipeline and employ BERT-based reranker to filter retrieved documents …

No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension

2020

The Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but also of richer types (including no-answer, yes/no, single-span …

Disentangled Retrieval and Reasoning for Implicit Question Answering

2022 · IEEE Transactions on Neural Networks and Learning Systems

To date, most of the existing open-domain question answering (QA) methods focus on explicit questions where the reasoning steps are mentioned explicitly in the question. In this article, we study implicit QA where the reasoning …

Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

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 evidence from external knowledge and make predictions based on …

CodeBERT: A Pre-Trained Model for Programming and Natural Languages

2020

Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou. Findings of the Association for Computational Linguistics: EMNLP 2020. 2020.

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation

2020

Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung …

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters

2021

We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, when multiple kinds of knowledge are …

Text Embeddings by Weakly-Supervised Contrastive Pre-training

2022 · arXiv (Cornell University)

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. The model is trained in a contrastive manner with weak supervision signals from our curated large-scale …