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Found 42 papers, 15 papers with code
Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically
Integrating knowledge into text is a promising way to enrich text representation, especially in the medical field.
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text
Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models.
FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion Vision-Language Pre-training
We propose a method for fine-grained fashion vision-language pre-training based on fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained multi-modalities fashion attributes and characteristics.
Refined Vision-Language Modeling for Fine-grained Multi-modal Pre-training
In this paper, we propose a fine-grained VLP scheme without object annotations from the linguistic perspective.
A Neural Span-Based Continual Named Entity Recognition Model
Named Entity Recognition (NER) models capable of Continual Learning (CL) are realistically valuable in areas where entity types continuously increase (e. g., personal assistants).
Continual Named Entity Recognition
Knowledge Distillation
+3
iEnhancer-ELM: Improve Enhancer Identification by Extracting Multi-scale Contextual Information based on Enhancer Language Models
Results: In this paper, we propose a multi-scale enhancer identification method (iEnhancer-ELM) based on enhancer language models, which treat enhancer sequences as natural language sentences that are composed of k-mer nucleotides.
SeDR: Segment Representation Learning for Long Documents Dense Retrieval
In SeDR, Segment-Interaction Transformer is proposed to encode long documents into document-aware and segment-sensitive representations, while it holds the complexity of splitting-and-pooling and outperforms other segment-interaction patterns on DR.
Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates
We conduct extensive experiments on six datasets with two popular pre-trained language models in the in-domain and out-of-domain settings.
Prompt-based Text Entailment for Low-Resource Named Entity Recognition
To address these challenges, we propose Prompt-based Text Entailment (PTE) for low-resource named entity recognition, which better leverages knowledge in the PLMs.
Low Resource Named Entity Recognition
named-entity-recognition
+2
A Survey on Table Question Answering: Recent Advances
Table Question Answering (Table QA) refers to providing precise answers from tables to answer a user's question.
Medical Dialogue Response Generation with Pivotal Information Recalling
To mitigate this problem, we propose a medical response generation model with Pivotal Information Recalling (MedPIR), which is built on two components, i. e., knowledge-aware dialogue graph encoder and recall-enhanced generator.
Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction
Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classification.
Diaformer: Automatic Diagnosis via Symptoms Sequence Generation
Detailed analysis on symptom inquiry prediction demonstrates that the potential of applying symptoms sequence generation for automatic diagnosis.
VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval
Cross-model retrieval has emerged as one of the most important upgrades for text-only search engines (SE).
Sentence-level Online Handwritten Chinese Character Recognition
Single online handwritten Chinese character recognition~(single OLHCCR) has achieved prominent performance.
GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyph
Previous works indicate that the glyph of Chinese characters contains rich semantic information and has the potential to enhance the representation of Chinese characters.
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on
Medical Relation Extraction
on CMeIE
Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning
In this study, we define the spectral graph convolutional network with the high-order dynamic Chebyshev approximation (HDGCN), which augments the multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutional layer.
You Can Do Better! If You Elaborate the Reason When Making Prediction
The experimental results show that the proposed approach can generate reasonable explanations for its predictions even with a small-scale training corpus.
A Hierarchical Reasoning Graph Neural Network for The Automatic Scoring of Answer Transcriptions in Video Job Interviews
In this work, we propose a Hierarchical Reasoning Graph Neural Network (HRGNN) for the automatic assessment of question-answer pairs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
MedWriter: Knowledge-Aware Medical Text Generation
To exploit the domain knowledge to guarantee the correctness of generated text has been a hot topic in recent years, especially for high professional domains such as medical.
Neural Data-to-Text Generation with Dynamic Content Planning
We further design a reconstruction mechanism with a novel objective function that can reconstruct the whole entry of the used data sequentially from the hidden states of the decoder, which aids the accuracy of the generated text.
Decomposing Word Embedding with the Capsule Network
We propose a Capsule network-based method to Decompose the unsupervised word Embedding of an ambiguous word into context specific Sense embedding, called CapsDecE2S.
Text-Guided Neural Image Inpainting
To fulfill such a task, we propose a novel inpainting model named Text-Guided Dual Attention Inpainting Network (TDANet).
Semi-supervised Visual Feature Integration for Pre-trained Language Models
In the framework, the visual features are obtained through a visualization and fusion mechanism.
Natural Language Inference
Natural Language Understanding
+2
Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning
In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019.
A Deep Learning-Based System for PharmaCoNER
The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical {\&} drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2).
Family history information extraction via deep joint learning
For this task, we propose a system based on deep joint learning methods to extract FH information.
BERTSel: Answer Selection with Pre-trained Models
we are the first to explore the performance of fine-tuning BERT for answer selection.
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification
As the largest manually annotated public Chinese SSEI corpus in the bank domain, the BQ corpus is not only useful for Chinese question semantic matching research, but also a significant resource for cross-lingual and cross-domain SSEI research.
LCQMC:A Large-scale Chinese Question Matching Corpus
In this paper, we first use a search engine to collect large-scale question pairs related to high-frequency words from various domains, then filter irrelevant pairs by the Wasserstein distance, and finally recruit three annotators to manually check the left pairs.
CNN based music emotion classification
Music emotion recognition (MER) is usually regarded as a multi-label tagging task, and each segment of music can inspire specific emotion tags.
Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF
In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers.
Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition
In this paper, we propose a novel model, named Stroke Sequence-dependent Deep Convolutional Neural Network (SSDCNN), using the stroke sequence information and eight-directional features for Online Handwritten Chinese Character Recognition (OLHCCR).
Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering
In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem.
LCSTS: A Large Scale Chinese Short Text Summarization Dataset
Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set.
Ranked #1 on
Text Summarization
on LCSTS
Convolutional Neural Network Architectures for Matching Natural Language Sentences
Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic, RetrievalQA}.
Ranked #3 on
Question Answering
on SemEvalCQA
