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Found 27 papers, 14 papers with code
Unsupervised Preference-Aware Language Identification
Recognizing the language of ambiguous texts has become a main challenge in language identification (LID).
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation
Diverse NMT aims at generating multiple diverse yet faithful translations given a source sentence.
Challenges of Neural Machine Translation for Short Texts
After empirically investigating the rationale behind this, we summarize two challenges in NMT for STs associated with translation error types above, respectively: (1) the imbalanced length distribution in training set intensifies model inference calibration over STs, leading to more over-translation cases on STs; and (2) the lack of contextual information forces NMT to have higher data uncertainty on short sentences, and thus NMT model is troubled by considerable mistranslation errors.
Self-supervised Product Title Rewrite for Product Listing Ads
Product Listing Ads (PLAs) are primary online advertisements merchants pay to attract more customers.
Large Language Models are Contrastive Reasoners
Prompting methods play a crucial role in enhancing the capabilities of pre-trained large language models (LLMs).
Exploring Large Language Models for Knowledge Graph Completion
Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness.
Friend Ranking in Online Games via Pre-training Edge Transformers
Friend recall is an important way to improve Daily Active Users (DAU) in online games.
AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury
However, few have explored BERT on disease-specific medical domain tasks such as AKI early prediction.
CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation
Recently, there is growing attention on one-stage panoptic segmentation methods which aim to segment instances and stuff jointly within a fully convolutional pipeline efficiently.
Towards User-Driven Neural Machine Translation
Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus.
Domain Transfer based Data Augmentation for Neural Query Translation
Query translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR).
Constraint Translation Candidates: A Bridge between Neural Query Translation and Cross-lingual Information Retrieval
Query translation (QT) is a key component in cross-lingual information retrieval system (CLIR).
Exploiting Neural Query Translation into Cross Lingual Information Retrieval
As a crucial role in cross-language information retrieval (CLIR), query translation has three main challenges: 1) the adequacy of translation; 2) the lack of in-domain parallel training data; and 3) the requisite of low latency.
Towards Expressive Graph Representation
Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning.
KG-BERT: BERT for Knowledge Graph Completion
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.
Ranked #5 on
Link Prediction
on UMLS
Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study
While the NLP system showed high precision and recall measurements for four target concepts (aortic valve regurgitation, left atrium size at end systole, mitral valve regurgitation, tricuspid valve regurgitation) across all sites, we found moderate or poor results for the remaining concepts and the NLP system performance varied between individual sites.
ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays
However, most of the existing approaches for image representation ignore the relations between images and consider each input image independently.
MedGCN: Medication recommendation and lab test imputation via graph convolutional networks
In this study, we construct a graph to associate 4 types of medical entities, i. e., patients, encounters, lab tests, and medications, and applied a graph neural network to learn node embeddings for medication recommendation and lab test imputation.
Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes
We developed data-driven prediction models to estimate the risk of new AKI onset.
Alibaba's Neural Machine Translation Systems for WMT18
We participated in 5 translation directions including English ↔ Russian, English ↔ Turkish in both directions and English → Chinese.
Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray Images
However, most of the previous deep neural network classifiers were based on deterministic architectures which are usually very noise-sensitive and are likely to aggravate the overfitting issue.
Distribution Networks for Open Set Learning
In this paper, we recognize that novel classes should be different from each other, and propose distribution networks for open set learning that can model different novel classes based on probability distributions.
Graph Convolutional Networks for Text Classification
We build a single text graph for a corpus based on word co-occurrence and document word relations, then learn a Text Graph Convolutional Network (Text GCN) for the corpus.
Ranked #6 on
Text Classification
on Ohsumed
Developing a Portable Natural Language Processing Based Phenotyping System
Our system facilitates portable phenotyping of obesity and its 15 comorbidities based on the unstructured patient discharge summaries, while achieving a performance that often ranked among the top 10 of the challenge participants.
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks
Clinical text classification is an important problem in medical natural language processing.
Ranked #2 on
Clinical Note Phenotyping
on I2B2 2008: Obesity
Image-Image Search for Comparable Corpora Construction
We present a novel method of comparable corpora construction.
