110 citations · 405 across the 25 of their papers we have counts for
22 papers · 1 filter
CC-OCR V2: Fine-Grained Attribution of LMM Failures in Real-World Visual Document Understanding
Chunyi Peng, Zhipeng Xu, Yuqi Xiong +12
Recent Large Multimodal Models (LMMs) have achieved remarkable progress on OCR-centric document understanding and processing tasks. Existing benchmarks primarily evaluate LMMs acro…
Noisy Pair Corrector for Dense Retrieval
Hang Zhang, Yeyun Gong, Xingwei He +4
Most dense retrieval models contain an implicit assumption: the training query-document pairs are exactly matched. Since it is expensive to annotate the corpus manually, training p…
Qwen Technical Report
Jinze Bai, Shuai Bai, Yunfei Chu +45
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to hu…
Interactive Natural Language Processing
Zekun Wang, Ge Zhang, Kexin Yang +19
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with t…
Draft, Command, and Edit: Controllable Text Editing in E-Commerce
Kexin Yang, Dayiheng Liu, Wenqiang Lei +3
Product description generation is a challenging and under-explored task. Most such work takes a set of product attributes as inputs then generates a description from scratch in a s…
Mask Attention Networks: Rethinking and Strengthen Transformer
Zhihao Fan, Yeyun Gong, Dayiheng Liu +6
Transformer is an attention-based neural network, which consists of two sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN). Existing research explores t…
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
Weizhen Qi, Yeyun Gong, Jian Jiao +9
In this paper, we propose BANG, a new pretraining model to Bridge the gap between Autoregressive (AR) and Non-autoregressive (NAR) Generation. AR and NAR generation can be uniforml…
GLGE: A New General Language Generation Evaluation Benchmark
Dayiheng Liu, Yu Yan, Yeyun Gong +15
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus…
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
Dayiheng Liu, Yeyun Gong, Jie Fu +5
In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC),…
AnchiBERT: A Pre-Trained Model for Ancient ChineseLanguage Understanding and Generation
Huishuang Tian, Kexin Yang, Dayiheng Liu +1
Ancient Chinese is the essence of Chinese culture. There are several natural language processing tasks of ancient Chinese domain, such as ancient-modern Chinese translation, poem g…
RikiNet: Reading Wikipedia Pages for Natural Question Answering
Dayiheng Liu, Yeyun Gong, Jie Fu +5
Reading long documents to answer open-domain questions remains challenging in natural language understanding. In this paper, we introduce a new model, called RikiNet, which reads W…
Let's be Humorous: Knowledge Enhanced Humor Generation
Hang Zhang, Dayiheng Liu, Jiancheng Lv +1
The generation of humor is an under-explored and challenging problem. Previous works mainly utilize templates or replace phrases to generate humor. However, few works focus on free…
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation
Dayiheng Liu, Yeyun Gong, Jie Fu +6
News headline generation aims to produce a short sentence to attract readers to read the news. One news article often contains multiple keyphrases that are of interest to different…
Generating Chinese Poetry from Images via Concrete and Abstract Information
Yusen Liu, Dayiheng Liu, Jiancheng Lv +1
In recent years, the automatic generation of classical Chinese poetry has made great progress. Besides focusing on improving the quality of the generated poetry, there is a new top…
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
Weizhen Qi, Yu Yan, Yeyun Gong +5
This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the propo…
Deep Poetry: A Chinese Classical Poetry Generation System
Yusen Liu, Dayiheng Liu, Jiancheng Lv
In this work, we demonstrate a Chinese classical poetry generation system called Deep Poetry. Existing systems for Chinese classical poetry generation are mostly template-based and…
TIGS: An Inference Algorithm for Text Infilling with Gradient Search
Dayiheng Liu, Jie Fu, Pengfei Liu +1
Text infilling is defined as a task for filling in the missing part of a sentence or paragraph, which is suitable for many real-world natural language generation scenarios. However…
mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation
Dayiheng Liu, Xu Yang, Feng He +2
It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problem. The model w…
Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning
Dayiheng Liu, Jie Fu, Yidan Zhang +2
Typical methods for unsupervised text style transfer often rely on two key ingredients: 1) seeking the explicit disentanglement of the content and the attributes, and 2) troublesom…
Ancient-Modern Chinese Translation with a Large Training Dataset
Dayiheng Liu, Jiancheng Lv, Kexin Yang +1
Ancient Chinese brings the wisdom and spirit culture of the Chinese nation. Automatic translation from ancient Chinese to modern Chinese helps to inherit and carry forward the quin…
A Multi-Modal Chinese Poetry Generation Model
Dayiheng Liu, Quan Guo, Wubo Li +1
Recent studies in sequence-to-sequence learning demonstrate that RNN encoder-decoder structure can successfully generate Chinese poetry. However, existing methods can only generate…
BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation
Dayiheng Liu, Jie Fu, Qian Qu +1
Incorporating prior knowledge like lexical constraints into the model's output to generate meaningful and coherent sentences has many applications in dialogue system, machine trans…