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Found 24 papers, 11 papers with code
Refining Data for Text Generation
To solve this problem, we propose a two-step approach which first selects and orders the important data records and then generates text from the noise-reduced data.
Adversarial Speech Generation and Natural Speech Recovery for Speech Content Protection
Moreover, based on the speech collection, we proposed a neural network-based frame-by-frame mapping method to recover the speech content by converting from the adversarial speech to the human speech.
GLFNET: Global-Local (frequency) Filter Networks for efficient medical image segmentation
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance.
Shall We Talk: Exploring Spontaneous Collaborations of Competing LLM Agents
Recent advancements have shown that agents powered by large language models (LLMs) possess capabilities to simulate human behaviors and societal dynamics.
Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation
Semi-supervised learning has demonstrated great potential in medical image segmentation by utilizing knowledge from unlabeled data.
Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation
This paper studies the impact of layer normalization (LayerNorm) on zero-shot translation (ZST).
Comprehensive Solution Program Centric Pretraining for Table-and-Text Hybrid Numerical Reasoning
Numerical reasoning over table-and-text hybrid passages, such as financial reports, poses significant challenges and has numerous potential applications.
GPT-RE: In-context Learning for Relation Extraction using Large Language Models
In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e. g., GPT-3), they still lag significantly behind fully-supervised baselines (e. g., fine-tuned BERT) in relation extraction (RE).
Textual Enhanced Contrastive Learning for Solving Math Word Problems
Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information.
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction
Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models.
Optimizing Vision Transformers for Medical Image Segmentation
For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations.
ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision
To address this problem, we propose a novel search algorithm with combinatorial strategy \textbf{ComSearch}, which can compress the search space by excluding mathematically equivalent equations.
Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems
To solve Math Word Problems, human students leverage diverse reasoning logic that reaches different possible equation solutions.
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision
Contrastive pre-training on distant supervision has shown remarkable effectiveness in improving supervised relation extraction tasks.
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition
Low-resource speech recognition has been long-suffering from insufficient training data.
Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding
Meta learning with auxiliary languages has demonstrated promising improvements for cross-lingual natural language processing.
Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network
In this paper, the Convolutional Neutral Network (CNN) has been adapted to predict and classify lymph node metastasis in breast cancer.
Reverse Operation based Data Augmentation for Solving Math Word Problems
Automatically solving math word problems is a critical task in the field of natural language processing.
LiveQA: A Question Answering Dataset over Sports Live
In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast.
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction
We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets.
A System for Worldwide COVID-19 Information Aggregation
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education.
CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning
The model is extremely weak at differing the head and tail entity, resulting in inaccurate entity extraction.
Ranked #12 on
Relation Extraction
on WebNLG
Tree-structured Decoding for Solving Math Word Problems
To address this problem, we propose a tree-structured decoding method that generates the abstract syntax tree of the equation in a top-down manner.
An Improved Coarse-to-Fine Method for Solving Generation Tasks
The methods first generate a rough sketch in the coarse stage and then use the sketch to get the final result in the fine stage.
