616 citations · 1.8k across the 64 of their papers we have counts for
21 papers · 1 filter
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation
Han Zhang, Weichong Yin, Yewei Fang +7
Conventional methods for the image-text generation tasks mainly tackle the naturally bidirectional generation tasks separately, focusing on designing task-specific frameworks to im…
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
Shuohuan Wang, Yu Sun, Yang Xiang +26
Pre-trained language models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. GPT-3 has shown that scaling up pre-trained language models c…
TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations
Xin Tian, Xinxian Huang, Dongfeng He +11
Task-oriented dialogue systems have been plagued by the difficulties of obtaining large-scale and high-quality annotated conversations. Furthermore, most of the publicly available…
Docking-based Virtual Screening with Multi-Task Learning
Zijing Liu, Xianbin Ye, Xiaomin Fang +3
Machine learning shows great potential in virtual screening for drug discovery. Current efforts on accelerating docking-based virtual screening do not consider using existing data…
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models
Hongyu Zhu, Yan Chen, Jing Yan +5
In this paper, we focus on studying robustness evaluation of Chinese question matching. Most of the previous work on analyzing robustness issue focus on just one or a few types of…
SgSum: Transforming Multi-document Summarization into Sub-graph Selection
Moye Chen, Wei Li, Jiachen Liu +3
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 tw…
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
Ruiyang Ren, Yingqi Qu, Jing Liu +5
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 proced…
Building Chinese Biomedical Language Models via Multi-Level Text Discrimination
Quan Wang, Songtai Dai, Benfeng Xu +4
Pre-trained language models (PLMs), such as BERT and GPT, have revolutionized the field of NLP, not only in the general domain but also in the biomedical domain. Most prior efforts…
DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation
Zeming Liu, Haifeng Wang, Zheng-Yu Niu +2
In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and…
Mixup Decoding for Diverse Machine Translation
Jicheng Li, Pengzhi Gao, Xuanfu Wu +4
Diverse machine translation aims at generating various target language translations for a given source language sentence. Leveraging the linear relationship in the sentence latent…
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation
Siqi Bao, Huang He, Fan Wang +11
To explore the limit of dialogue generation pre-training, we present the models of PLATO-XL with up to 11 billion parameters, trained on both Chinese and English social media conve…
PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval
Ruiyang Ren, Shangwen Lv, Yingqi Qu +6
Recently, dense passage retrieval has become a mainstream approach to finding relevant information in various natural language processing tasks. A number of studies have been devot…
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
Yu Sun, Shuohuan Wang, Shikun Feng +19
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-train…
ERNIE-Tiny : A Progressive Distillation Framework for Pretrained Transformer Compression
Weiyue Su, Xuyi Chen, Shikun Feng +6
Pretrained language models (PLMs) such as BERT adopt a training paradigm which first pretrain the model in general data and then finetune the model on task-specific data, and have…
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction
Xiaomin Fang, Lihang Liu, Jieqiong Lei +6
Effective molecular representation learning is of great importance to facilitate molecular property prediction, which is a fundamental task for the drug and material industry. Rece…
A Unified Pre-training Framework for Conversational AI
Siqi Bao, Bingjin Chen, Huang He +7
In this work, we explore the application of PLATO-2 on various dialogue systems, including open-domain conversation, knowledge grounded dialogue, and task-oriented conversation. PL…
BASS: Boosting Abstractive Summarization with Unified Semantic Graph
Wenhao Wu, Wei Li, Xinyan Xiao +5
Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.…
Link Prediction on N-ary Relational Facts: A Graph-based Approach
Quan Wang, Haifeng Wang, Yajuan Lyu +1
Link prediction on knowledge graphs (KGs) is a key research topic. Previous work mainly focused on binary relations, paying less attention to higher-arity relations although they a…
BSTC: A Large-Scale Chinese-English Speech Translation Dataset
Ruiqing Zhang, Xiyang Wang, Chuanqiang Zhang +6
This paper presents BSTC (Baidu Speech Translation Corpus), a large-scale Chinese-English speech translation dataset. This dataset is constructed based on a collection of licensed…
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing
Kun Wu, Lijie Wang, Zhenghua Li +5
Data augmentation has attracted a lot of research attention in the deep learning era for its ability in alleviating data sparseness. The lack of labeled data for unseen evaluation…
Learning to Select External Knowledge with Multi-Scale Negative Sampling
Huang He, Hua Lu, Siqi Bao +4
The Track-1 of DSTC9 aims to effectively answer user requests or questions during task-oriented dialogues, which are out of the scope of APIs/DB. By leveraging external knowledge r…