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Found 30 papers, 8 papers with code
Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks
Natural language generation (NLG) is a critical component in a spoken dialogue system.
Semantic Refinement GRU-based Neural Language Generation for Spoken Dialogue Systems
Natural language generation (NLG) plays a critical role in spoken dialogue systems.
Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation
Natural language generation (NLG) is an important component in spoken dialogue systems.
Combining Advanced Methods in Japanese-Vietnamese Neural Machine Translation
Neural machine translation (NMT) systems have recently obtained state-of-the art in many machine translation systems between popular language pairs because of the availability of data.
Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems
In this procedure, a model is first trained on a source domain data and then fine-tuned on a small set of target domain utterances under the guidance of two proposed critics.
Dual Latent Variable Model for Low-Resource Natural Language Generation in Dialogue Systems
Recent deep learning models have shown improving results to natural language generation (NLG) irrespective of providing sufficient annotated data.
How Transformer Revitalizes Character-based Neural Machine Translation: An Investigation on Japanese-Vietnamese Translation Systems
While translating between East Asian languages, many works have discovered clear advantages of using characters as the translation unit.
Overcoming the Rare Word Problem for Low-Resource Language Pairs in Neural Machine Translation
Among the six challenges of neural machine translation (NMT) coined by (Koehn and Knowles, 2017), rare-word problem is considered the most severe one, especially in translation of low-resource languages.
Improving Multilingual Neural Machine Translation For Low-Resource Languages: French,English - Vietnamese
Prior works have demonstrated that a low-resource language pair can benefit from multilingual machine translation (MT) systems, which rely on many language pairs' joint training.
SCNN: Swarm Characteristic Neural Network
Deep learning is a powerful approach with good performance on many different tasks.
Sublanguage: A Serious Issue Affects Pretrained Models in Legal Domain
Legal English is a sublanguage that is important for everyone but not for everyone to understand.
HYDRA -- Hyper Dependency Representation Attentions
Attention is all we need as long as we have enough data.
Transformer-based Approaches for Legal Text Processing
In this paper, we introduce our approaches using Transformer-based models for different problems of the COLIEE 2021 automatic legal text processing competition.
Attentive Deep Neural Networks for Legal Document Retrieval
The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents.
Law to Binary Tree -- An Formal Interpretation of Legal Natural Language
First, legal reasoning can be performed on the basis of the binary tree representation of the regulations.
Improving Vietnamese Legal Question--Answering System based on Automatic Data Enrichment
Question answering (QA) in law is a challenging problem because legal documents are much more complicated than normal texts in terms of terminology, structure, and temporal and logical relationships.
DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation
Unlike earlier transformer-based U-net models, DA-TransUNet utilizes Transformers and DA-Block to integrate not only global and local features, but also image-specific positional and channel features, improving the performance of medical image segmentation.
CAPTAIN at COLIEE 2023: Efficient Methods for Legal Information Retrieval and Entailment Tasks
The Competition on Legal Information Extraction/Entailment (COLIEE) is held annually to encourage advancements in the automatic processing of legal texts.
Employing Label Models on ChatGPT Answers Improves Legal Text Entailment Performance
ChatGPT, a large language model, is robust in many natural language processing tasks, including legal text entailment: when we set the temperature = 0 (the ChatGPT answers are deterministic) and prompt the model, it achieves 70. 64% accuracy on COLIEE 2022 dataset, which outperforms the previous SOTA of 67. 89%.
VLSP 2023 -- LTER: A Summary of the Challenge on Legal Textual Entailment Recognition
In this new era of rapid AI development, especially in language processing, the demand for AI in the legal domain is increasingly critical.
A Mutual Inclusion Mechanism for Precise Boundary Segmentation in Medical Images
Our approach, inspired by radiologists' working patterns, features two distinct modules: (i) \textbf{Mutual Inclusion of Position and Channel Attention (MIPC) module}: To enhance the precision of boundary segmentation in medical images, we introduce the MIPC module, which enhances the focus on channel information when extracting position features and vice versa; (ii) \textbf{GL-MIPC-Residue}: To improve the restoration of medical images, we propose the GL-MIPC-Residue, a global residual connection that enhances the integration of the encoder and decoder by filtering out invalid information and restoring the most effective information lost during the feature extraction process.
Multi Graph Neural Network for Extractive Long Document Summarization
Heterogeneous Graph Neural Networks (HeterGNN) have been recently introduced as an emergent approach for extracting document summarization (EDS) by exploiting the cross-relations between words and sentences.
Improving Multilingual Neural Machine Translation For Low-Resource Languages: French, English - Vietnamese
Prior works have demonstrated that a low-resource language pair can benefit from multilingual machine translation (MT) systems, which rely on many language pairs’ joint training.
