110 citations · 867 across the 51 of their papers we have counts for
30 papers · 1 filter
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…
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models
Lei Li, Yankai Lin, Xuancheng Ren +4
Investigating better ways to reuse the released pre-trained language models (PLMs) can significantly reduce the computational cost and the potential environmental side-effects. Thi…
Hierarchical Inductive Transfer for Continual Dialogue Learning
Shaoxiong Feng, Xuancheng Ren, Kan Li +1
Pre-trained models have achieved excellent performance on the dialogue task. However, for the continual increase of online chit-chat scenarios, directly fine-tuning these models fo…
Model Uncertainty-Aware Knowledge Amalgamation for Pre-Trained Language Models
Lei Li, Yankai Lin, Xuancheng Ren +4
As many fine-tuned pre-trained language models~(PLMs) with promising performance are generously released, investigating better ways to reuse these models is vital as it can greatly…
O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning
Fenglin Liu, Xuancheng Ren, Xian Wu +4
Video captioning combines video understanding and language generation. Different from image captioning that describes a static image with details of almost every object, video capt…
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models
Wenkai Yang, Lei Li, Zhiyuan Zhang +3
Recent studies have revealed a security threat to natural language processing (NLP) models, called the Backdoor Attack. Victim models can maintain competitive performance on clean…
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation
Shaoxiong Feng, Xuancheng Ren, Kan Li +1
Neural dialogue models suffer from low-quality responses when interacted in practice, demonstrating difficulty in generalization beyond training data. Recently, knowledge distillat…
CAPT: Contrastive Pre-Training for Learning Denoised Sequence Representations
Fuli Luo, Pengcheng Yang, Shicheng Li +2
Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing. These models typica…
Regularizing Dialogue Generation by Imitating Implicit Scenarios
Shaoxiong Feng, Xuancheng Ren, Hongshen Chen +3
Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario. To enable responses that are more m…
Rethinking and Improving Natural Language Generation with Layer-Wise Multi-View Decoding
Fenglin Liu, Xuancheng Ren, Guangxiang Zhao +4
In sequence-to-sequence learning, e.g., natural language generation, the decoder relies on the attention mechanism to efficiently extract information from the encoder. While it is…
Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
Guangxiang Zhao, Junyang Lin, Zhiyuan Zhang +3
Self-attention based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks. Self-attention is able to model long-term depe…
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer
Chen Wu, Xuancheng Ren, Fuli Luo +1
Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision. Existing seq2seq methods face three challenges: 1) th…
PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation
Ruixuan Luo, Jingjing Xu, Yi Zhang +3
Chinese word segmentation (CWS) is a fundamental step of Chinese natural language processing. In this paper, we build a new toolkit, named PKUSEG, for multi-domain word segmentatio…
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
Fenglin Liu, Yuanxin Liu, Xuancheng Ren +2
In vision-and-language grounding problems, fine-grained representations of the image are considered to be of paramount importance. Most of the current systems incorporate visual fe…
Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet
Shu Liu, Jingjing Xu, Xuancheng Ren +1
Automatic evaluation of semantic rationality is an important yet challenging task, and current automatic techniques cannot well identify whether a sentence is semantically rational…
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions
Fenglin Liu, Xuancheng Ren, Yuanxin Liu +2
The encode-decoder framework has shown recent success in image captioning. Visual attention, which is good at detailedness, and semantic attention, which is good at comprehensivene…
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation
Jingjing Xu, Xuancheng Ren, Yi Zhang +3
Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing gen…
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation
Junyang Lin, Xu Sun, Xuancheng Ren +2
Most of the Neural Machine Translation (NMT) models are based on the sequence-to-sequence (Seq2Seq) model with an encoder-decoder framework equipped with the attention mechanism. H…
Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations
Guangxiang Zhao, Jingjing Xu, Qi Zeng +1
This paper explores a new natural language processing task, review-driven multi-label music style classification. This task requires the system to identify multiple styles of music…
Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
Wei Li, Xuancheng Ren, Damai Dai +3
Huge numbers of new words emerge every day, leading to a great need for representing them with semantic meaning that is understandable to NLP systems. Sememes are defined as the mi…
Deconvolution-Based Global Decoding for Neural Machine Translation
Junyang Lin, Xu Sun, Xuancheng Ren +3
A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following…
A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification
Shuming Ma, Xu Sun, Junyang Lin +1
Text summarization and sentiment classification both aim to capture the main ideas of the text but at different levels. Text summarization is to describe the text within a few sent…
Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency
Bingzhen Wei, Xuancheng Ren, Xu Sun +3
Abstractive text summarization is a highly difficult problem, and the sequence-to-sequence model has shown success in improving the performance on the task. However, the generated…
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
Jingjing Xu, Xu Sun, Qi Zeng +4
The goal of sentiment-to-sentiment "translation" is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To…