Haifeng Wang

SgSum: Transforming Multi-document Summarization into Sub-graph Selection

2021 · arXiv (Cornell University)

Most of existing extractive multi-document summarization (MDS) methods score each sentence individually and extract salient sentences one by one to compose a summary, which have two main drawbacks: (1) neglecting both the intra and cross-document …

Federated Learning with Class Imbalance Reduction

2021 · 2021 29th European Signal Processing Conference (EUSIPCO)

Federated learning (FL) is a promising technique that enables a large amount of edge computing devices to collaboratively train a global learning model. Due to the communication limitation, only a subset of devices can be …

Cross-lingual Dependency Parsing Based on Distributed Representations

2015

Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, Ting Liu. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long …

Multi-Task Learning for Multiple Language Translation

2015

Daxiang Dong, Hua Wu, Wei He, Dianhai Yu, Haifeng Wang. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long …

A Representation Learning Framework for Multi-Source Transfer Parsing

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

Cross-lingual model transfer has been a promising approach for inducing dependency parsers for low-resource languages where annotated treebanks are not available. The major obstacles for the model transfer approach are two-fold: 1. Lexical features are …

Improved Neural Machine Translation with SMT Features

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

Neural machine translation (NMT) conducts end-to-end translation with a source language encoder and a target language decoder, making promising translation performance. However, as a newly emerged approach, the method has some limitations. An NMT system …

Proactive Human-Machine Conversation with Explicit Conversation Goal

2019

Though great progress has been made for human-machine conversation, current dialogue system is still in its infancy: it usually converses passively and utters words more as a matter of response, rather than on its own …

Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification

2018

Yizhong Wang, Kai Liu, Jing Liu, Wei He, Yajuan Lyu, Hua Wu, Sujian Li, Haifeng Wang. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications

2018

Wei He, Kai Liu, Jing Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yuan Liu, Yizhong Wang, Hua Wu, Qiaoqiao She, Xuan Liu, Tian Wu, Haifeng Wang. Proceedings of the Workshop on Machine Reading for Question …

ERNIE 2.0: A Continual Pre-training Framework for Language Understanding

2019 · arXiv (Cornell University)

Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing. Current pre-training procedures usually focus on training …

Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs

2019

Zhibin Liu, Zheng-Yu Niu, Hua Wu, Haifeng Wang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

End-to-End Speech Translation with Knowledge Distillation

2019

End-to-end speech translation (ST), which directly translates from source language speech into target language text, has attracted intensive attentions in recent years.Compared to conventional pipepine systems, end-to-end ST models have advantages of lower latency, smaller …

ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Recently pre-trained models have achieved state-of-the-art results in various language understanding tasks. Current pre-training procedures usually focus on training the model with several simple tasks to grasp the co-occurrence of words or sentences. However, besides …

ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora

2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recent studies have demonstrated that pretrained cross-lingual models achieve impressive performance in downstream cross-lingual tasks. This improvement benefits from learning a large amount of monolingual and parallel corpora. Although it is generally acknowledged that parallel …

RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

2021

Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, Haifeng Wang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: …

ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation

2021 · arXiv (Cornell University)

Pre-trained models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown that scaling up pre-trained language models can improve their generalization abilities. Particularly, the …

RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking

2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance, it is important to …