Daya Guo's repositories
Tencent2020_Rank1st
The code for 2020 Tencent College Algorithm Contest, and the online result ranks 1st.
Tencent2019_Preliminary_Rank1st
The code for 2019 Tencent College Algorithm Contest, and the online result ranks 1st in the preliminary.
CCF-BDCI-Sentiment-Analysis-Baseline
The code for CCF-BDCI-Sentiment-Analysis-Baseline
ICME2019-CTR
The Code for ICME2019 Grand Challenge: Short Video Understanding (Single Model Ranks 6th)
Dialog-to-Action
The code for the 2018 NeurIPS paper "Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base"
Question-Generation-VAE
The code for the EMNLP2018 paper "Question Generation from SQL Queries Improves Neural Semantic Parsing"
transformers
🤗 Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.
comet-commonsense
Code for ACL 2019 Paper: "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction" https://arxiv.org/abs/1906.05317
EA-VQ-VAE-1
This repo provides the code for the ACL 2020 paper "Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder"
ember-paper
The Ember approach to Material Design.
EMNLP2019-Split-And-Recombine
The code of EMNLP 2019 paper "A Split-and-Recombine Approach for Follow-up Query Analysis"
javascript
PubNub JavaScript SDK. https://www.pubnub.com/docs/javascript/pubnub-javascript-sdk-v4
LearningToUpdateNLComments
Learning to Update Natural Language Comments Based on Code Changes: Artifact
MSMARCO-Passage-Ranking
MS MARCO(Microsoft Machine Reading Comprehension) is a large scale dataset focused on machine reading comprehension, question answering, and passage ranking. A variant of this task will be the part of TREC and AFIRM 2019. For Updates about TREC 2019 please follow This Repository Passage Reranking task Task Given a query q and a the 1000 most relevant passages P = p1, p2, p3,... p1000, as retrieved by BM25 a succeful system is expected to rerank the most relevant passage as high as possible. For this task not all 1000 relevant items have a human labeled relevant passage. Evaluation will be done using MRR
ProphetNet
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training https://arxiv.org/pdf/2001.04063.pdf
py-faster-rcnn
Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
pytorch-vq-vae
PyTorch implementation of VQ-VAE by Aäron van den Oord et al.
roberta_zh
RoBERTa for Chinese
VideoFeatureExtractor
Video Feature Extractor for S3D-HowTo100M