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- Neural Document Summarization by Jointly Learning to Score and Select Sentences348
This paper presents a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences, which significantly outperforms the state-of-the-art extractive summarization models.
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- Knowledge-Based Question Answering as Machine Translation136
A translation-based approach to solve knowledge-based question answering tasks in one unified framework based on CYK parsing, which achieves better results than previous methods.
- Document-Level Relation Extraction with Reconstruction135
A novel encoder-classifier-reconstructor model that manages to reconstruct the ground-truth path dependencies from the graph representation to ensure that the proposed DocRE model pays more attention to encode entity pairs with relationships in the training.
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- Table-to-Text: Describing Table Region With Natural Language116
A generative model to generate a natural language sentence describing a table region, e.g., a row, and a flexible copying mechanism that selectively replicates contents from the table in the output sequence is presented.
- Automated image quality evaluation of T2‐weighted liver MRI utilizing deep learning architecture101
To develop and test a deep learning approach named Convolutional Neural Network for automated screening of T2‐weighted (T2WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists.
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- Syntax-Directed Attention for Neural Machine Translation96
This paper extends the local attention with syntax-distance constraint, which focuses on syntactically related source words with the predicted target word to learning a more effective context vector for predicting translation, to provide more translation performance for NMT from source representation.
- Chinese Named Entity Recognition with a Sequence Labeling Approach: Based on Characters, or Based on Words?87
The results show that without the global features the person names and the location names have good result based on characters, but the organization names are more suitable based on words when global features are added, and the performance ofbased on words improved significantly.
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- Discriminative Reasoning for Document-level Relation Extraction76
A novel discriminative reasoning framework is proposed to explicitly model the paths of these reasoning skills between each entity pair in this document to outperforms the previous state-of-the-art performance on the large-scale DocRE dataset.
- Deep Attention Neural Tensor Network for Visual Question Answering70
A novel deep attention neural tensor network (DA-NTN) for visual question answering, which can discover the joint correlations over images, questions and answers with tensor-based representations and integrates into the state-of-the-art VQA models.
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- Iterative GRAPPA (iGRAPPA) for improved parallel imaging reconstruction61
Both phantom and in vivo MRI experiments demonstrated that the iterative approach reduces parallel imaging artifacts and permits high‐quality image reconstruction with a relatively small number of calibration lines and slight changes of GRAPPA weights.
- Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation53
A simple method to translate between thirteen languages using a single encoder and a single decoder is introduced, making use of multilingual data to improve UNMT for all language pairs and proposes two knowledge distillation methods to further enhance multilingual UNMT performance.
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- Exploring the sensitivity of magnetic resonance fingerprinting to motion50
Different kinds of motion have distinct effects on the precision and effective resolution of the parametric maps measured with MRF, showing that MRF-based acquisitions can be relatively robust to motion effects occurring at the beginning or end of the sequence.
- High‐resolution sodium imaging of human brain at 7 T50
The feasibility of high‐resolution sodium magnetic resonance imaging on human brain at 7 T was demonstrated in this study and the quantification of sodium concentration on the phantom and brain images enabled better accuracy at the high nominal resolutions than at the low nominal resolution.
- Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training48
CTEG is proposed, a model equipped with two novel mechanisms to learn to decouple these easily-confused relations, and the results show that the proposed model achieves comparable and even much better results to strong baselines in terms of accuracy.
- Attention-Fused Deep Matching Network for Natural Language Inference47
An attention-fused deep matching network (AF-DMN) for natural language inference that takes two sentences as input and iteratively learns the attention-aware representations for each side by multi-level interactions and adds a self-attention mechanism to fully exploit local context information within each sentence.
- Issues with Entailment-based Zero-shot Text Classification46
It is found that pre-trained BERT without any fine-tuning can yield competitive performance against BERT fine- tuned for NLI, and with the concern that these models heavily rely on spurious lexical patterns for prediction, preliminary approaches for more robust NLI are experiments, but the results are in general negative.
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- Sodium imaging of human brain at 7 T with 15‐channel array coil46
The 15‐channel array coil is an efficient tool to substantially improve SNR in sodium imaging on human brain by performing self‐calibrated sensitivity encoding parallel image reconstruction and uniform image intensity across entire field of view.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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