773 citations · 2.9k across the 80 of their papers we have counts for
14 papers · 1 filter
Discovering Dialog Structure Graph for Open-Domain Dialog Generation
Jun Xu, Zeyang Lei, Haifeng Wang +4
Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dial…
ERNIE-Doc: A Retrospective Long-Document Modeling Transformer
Siyu Ding, Junyuan Shang, Shuohuan Wang +4
Transformers are not suited for processing long documents, due to their quadratically increasing memory and time consumption. Simply truncating a long document or applying the spar…
ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora
Xuan Ouyang, Shuohuan Wang, Chao Pang +4
Recent studies have demonstrated that pre-trained cross-lingual models achieve impressive performance in downstream cross-lingual tasks. This improvement benefits from learning a l…
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
Wei Li, Can Gao, Guocheng Niu +5
Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other. They can only utilize single-modal data (i.e. text…
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding
Dongling Xiao, Yu-Kun Li, Han Zhang +4
Coarse-grained linguistic information, such as named entities or phrases, facilitates adequately representation learning in pre-training. Previous works mainly focus on extending t…
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering
Yingqi Qu, Yuchen Ding, Jing Liu +6
In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers. Typically, the dual-encoder architecture is…
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph
Fei Yu, Jiji Tang, Weichong Yin +4
We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language. ERNIE-ViL…
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
Siqi Bao, Huang He, Fan Wang +6
To build a high-quality open-domain chatbot, we introduce the effective training process of PLATO-2 via curriculum learning. There are two stages involved in the learning process.…
Towards Conversational Recommendation over Multi-Type Dialogs
Zeming Liu, Haifeng Wang, Zheng-Yu Niu +3
We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e…
Leveraging Graph to Improve Abstractive Multi-Document Summarization
Wei Li, Xinyan Xiao, Jiachen Liu +3
Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries. In this…
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
Hao Tian, Can Gao, Xinyan Xiao +5
Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge, such as sentiment words and aspect-sentiment pairs,…
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer
Chulun Zhou, Liangyu Chen, Jiachen Liu +4
Unsupervised style transfer aims to change the style of an input sentence while preserving its original content without using parallel training data. In current dominant approaches…
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications
Hongxuan Tang, Hongyu Li, Jing Liu +3
Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements. However, most of the neural MRC models are still far…
ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
Dongling Xiao, Han Zhang, Yukun Li +4
Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks. To address this issue, we propose an enhanced mu…