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Found 20 papers, 4 papers with code
SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map
Sentence Compression (SC), which aims to shorten sentences while retaining important words that express the essential meanings, has been studied for many years in many languages, especially in English.
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network
Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations.
Ranked #1 on
Extractive Text Summarization
on CNN / Daily Mail
Enhancing Document Ranking with Task-adaptive Training and Segmented Token Recovery Mechanism
In this paper, we propose a new ranking model DR-BERT, which improves the Document Retrieval (DR) task by a task-adaptive training process and a Segmented Token Recovery Mechanism (STRM).
Quantum Adjoint Convolutional Layers for Effective Data Representation
Quantum Convolutional Layer (QCL) is considered as one of the core of Quantum Convolutional Neural Networks (QCNNs) due to its efficient data feature extraction capability.
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation
These models are able to generate creative and fluent metaphor sentences more frequently induced by selected samples from our dataset, demonstrating the value of our corpus for Chinese metaphor research.
Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models
We evaluate our method on both open and closed LLMs, and the experiments on the widely-used public dataset show that our method can generate more consistent responses in a long-context conversation.
MARBLE: Music Audio Representation Benchmark for Universal Evaluation
This is evident in the limited work on deep music representations, the scarcity of large-scale datasets, and the absence of a universal and community-driven benchmark.
Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking
Zero-shot transfer learning for Dialogue State Tracking (DST) helps to handle a variety of task-oriented dialogue domains without the cost of collecting in-domain data.
Interactive Natural Language Processing
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 the ultimate goals of artificial intelligence.
CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation
As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese.
HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding
Specifically, a personalized PageRank routine is developed to capture the co-relation of codes, a bidirectional hierarchy passage encoder to capture the codes' hierarchical representations, and a progressive predicting method is then proposed to narrow down the semantic searching space of prediction.
Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification
Recently, language representation techniques have achieved great performances in text classification.
CKG: Dynamic Representation Based on Context and Knowledge Graph
Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance.
HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models
In addition to verifying the existence of regional bias in LMs, we find that the biases on regional groups can be strongly influenced by the geographical clustering of the groups.
Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization
In zero-shot multilingual extractive text summarization, a model is typically trained on English summarization dataset and then applied on summarization datasets of other languages.
Photonics-Assisted Joint Communication-Radar System Based on a QPSK-Sliced Linearly Frequency-Modulated Signal
A photonics-assisted joint communication-radar system is proposed and experimentally demonstrated, by introducing a quadrature phase-shift keying (QPSK)-sliced linearly frequency-modulated (LFM) signal.
A review of Quantum Neural Networks: Methods, Models, Dilemma
The rapid development of quantum computer hardware has laid the hardware foundation for the realization of QNN.
Deep Differential Amplifier for Extractive Summarization
In this paper, we conceptualize the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework.
HIN: Hierarchical Inference Network for Document-Level Relation Extraction
In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level.
Ranked #51 on
Relation Extraction
on DocRED
Answer-focused and Position-aware Neural Question Generation
(2) The model copies the context words that are far from and irrelevant to the answer, instead of the words that are close and relevant to the answer.
