Search Results for author:
Found 50 papers, 16 papers with code
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization
In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization.
A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method.
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation
ACA learns to control the attention by keeping track of the decoding history and the current information with a memory vector, so that the model can take the translated contents and the current information into consideration.
Automatic Translating between Ancient Chinese and Contemporary Chinese with Limited Aligned Corpora
In this paper, we propose to build an end-to-end neural model to automatically translate between ancient and contemporary Chinese.
Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
In this paper, we propose the task of relation classification for Chinese literature text.
Global Encoding for Abstractive Summarization
To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context.
Ranked #29 on
Text Summarization
on GigaWord
Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency
Especially, the proposed approach improves the semantic consistency by 4\% in terms of human evaluation.
Deconvolution-Based Global Decoding for Neural Machine Translation
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 sequential order.
Ranked #9 on
Machine Translation
on IWSLT2015 English-Vietnamese
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation
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.
Ranked #7 on
Machine Translation
on IWSLT2015 English-Vietnamese
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification
We propose a novel model for multi-label text classification, which is based on sequence-to-sequence learning.
A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification
However, the Seq2Seq model is not suitable for the MLTC task in essence.
Memorized Sparse Backpropagation
Neural network learning is usually time-consuming since backpropagation needs to compute full gradients and backpropagate them across multiple layers.
Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction
In this work, we propose a BERT-based Hierarchical Aggregation Model to summarize a large amount of finance news to predict forex movement.
Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification
The task of unsupervised sentiment modification aims to reverse the sentiment polarity of the input text while preserving its semantic content without any parallel data.
HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network
To address this issue, we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions: (1) Implicitly modelling by learning to predict node pair relations (2) Explicitly modelling by adding edges between nodes that potentially have the same label.
Pretrain-KGEs: Learning Knowledge Representation from Pretrained Models for Knowledge Graph Embeddings
Learning knowledge graph embeddings (KGEs) is an efficient approach to knowledge graph completion.
Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
Self-attention based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks.
Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks
Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect extraction) and the sentiment polarities (sentiment detection) towards them.
Using Conceptual Norms for Metaphor Detection
This paper reports a linguistically-enriched method of detecting token-level metaphors for the second shared task on Metaphor Detection.
Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models
Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem.
metaCAT: A Metadata-based Task-oriented Chatbot Annotation Tool
Creating high-quality annotated dialogue corpora is challenging.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Sina Mandarin Alphabetical Words:A Web-driven Code-mixing Lexical Resource
Mandarin Alphabetical Word (MAW) is one indispensable component of Modern Chinese that demonstrates unique code-mixing idiosyncrasies influenced by language exchanges.
Evolution of cooperation with asymmetric social interactions
How cooperation emerges in human societies is both an evolutionary enigma, and a practical problem with tangible implications for societal health.
Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge
Knowledge facts are typically represented by relational triples, while we observe that some commonsense facts are represented by the triples whose forms are inconsistent with the expression of language.
Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects
Motivated by neuroscientific evidence and theoretical results, we demonstrate that side effects can be controlled by the number of changed parameters and thus, we propose to conduct \textit{neural network surgery} by only modifying a limited number of parameters.
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models
We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions.
Adversarial Parameter Defense by Multi-Step Risk Minimization
To enhance neural networks, we propose the adversarial parameter defense algorithm that minimizes the average risk of multiple adversarial parameter corruptions.
One-shot Weakly-Supervised Segmentation in Medical Images
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.
Chinese Word Segmentation with Heterogeneous Graph Neural Network
In recent years, deep learning has achieved significant success in the Chinese word segmentation (CWS) task.
The arrow of evolution when the offspring variance is large
The concept of fitness is central to evolution, but it quantifies only the expected number of offspring an individual will produce.
That Slepen Al the Nyght with Open Ye! Cross-era Sequence Segmentation with Switch-memory
The evolution of language follows the rule of gradual change.
GA-SAM: Gradient-Strength based Adaptive Sharpness-Aware Minimization for Improved Generalization
It demonstrates that flat minima tend to imply better generalization abilities.
Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation
NLP attacks tend to have small relative backdoor strengths, which may result in the failure of robust federated aggregation methods for NLP attacks.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive Summarization
Most Transformer based abstractive summarization systems have a severe mismatch between training and inference, i. e., exposure bias.
Prompt What You Need: Enhancing Segmentation in Rainy Scenes with Anchor-based Prompting
Semantic segmentation in rainy scenes is a challenging task due to the complex environment, class distribution imbalance, and limited annotated data.
Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN
Recently, it is quite common to integrate Chinese sequence labeling results to enhance syntactic and semantic parsing.
Automatic lobe segmentation using attentive cross entropy and end-to-end fissure generation
Therefore, we propose a new automatic lung lobe segmentation framework, in which we urge the model to pay attention to the area around the pulmonary fissure during the training process, which is realized by a task-specific loss function.
Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning
The key to our data sampling technique lies in the enhancement of diversity in the chosen subsets, as the model selects new data points most distinct from any existing ones according to its current embedding space.
DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models
Recent advances in image and video creation, especially AI-based image synthesis, have led to the production of numerous visual scenes that exhibit a high level of abstractness and diversity.
Knowledge-Aware Artifact Image Synthesis with LLM-Enhanced Prompting and Multi-Source Supervision
Ancient artifacts are an important medium for cultural preservation and restoration.
An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling
To address this challenge, we propose a two-stage curriculum learning (TCL) framework specifically designed for sequence labeling tasks.
Small Language Model Is a Good Guide for Large Language Model in Chinese Entity Relation Extraction
Recently, large language models (LLMs) have been successful in relational extraction (RE) tasks, especially in the few-shot learning.
Restoring Ancient Ideograph: A Multimodal Multitask Neural Network Approach
Cultural heritage serves as the enduring record of human thought and history.
CHisIEC: An Information Extraction Corpus for Ancient Chinese History
Additionally, we have evaluated the capabilities of Large Language Models (LLMs) in the context of tasks related to ancient Chinese history.
Unsupervised Chinese Word Segmentation with BERT Oriented Probing and Transformation
Word Segmentation is a fundamental step for understanding Chinese language.
汉语竞争类多人游戏语言中疑问句的形式与功能(The Form and Function of Interrogatives in Multi-party Chinese Competitive Game Conversation)
本文基于自建的竞争类多人游戏对话语料库对汉语疑问句的形式与功能进行了考察。文章首先在前人研究的基础上将疑问句的类型分为五大类, 然后考察不同类型的疑问句在对话中出现的位置与功能。研究显示, 是非问(包括反复问)与特指问是最常见的类型, 选择问使用频率最低。大部分疑问句会引起话轮转换, 具有询问功能, 此外, 否定与指出事实也是疑问句的主要功能。特指问的否定功能与附加问指出事实的 功能比较突出。
