BestHub
Sign in
Tagged articles
7 articles
Page 1 of 1
Baidu Geek Talk
Baidu Geek Talk
Nov 16, 2022 · Artificial Intelligence

How Baidu’s Ernie‑SimCSE Uses Contrastive Learning to Crush Spam Promotion

This article explains how Baidu's anti‑spam team tackled large‑scale promotional spam on Baidu Zhidao by combining the Ernie pretrained model with SimCSE contrastive learning, detailing the problem background, traditional methods, text‑representation stages, the SimCSE approach, training pipeline, optimizations, and experimental results.

ErnieNLPSimCSE
0 likes · 15 min read
How Baidu’s Ernie‑SimCSE Uses Contrastive Learning to Crush Spam Promotion
DaTaobao Tech
DaTaobao Tech
Apr 12, 2022 · Artificial Intelligence

ArcCSE: Angular Margin Contrastive Learning for Self‑Supervised Text Representation

ArcCSE introduces an angular‑margin contrastive loss and both pairwise (dropout‑augmented) and triple‑wise (span‑masked) relationship modeling to self‑supervise text embeddings, yielding tighter decision boundaries, higher alignment and uniformity, and superior performance on unsupervised STS, SentEval, and Alibaba’s retrieval and recommendation systems.

NLPangular margincontrastive learning
0 likes · 8 min read
ArcCSE: Angular Margin Contrastive Learning for Self‑Supervised Text Representation
DataFunSummit
DataFunSummit
Feb 12, 2022 · Artificial Intelligence

Advances and Challenges in Post‑BERT Semantic Matching: Negative Sampling, Data Augmentation, and Applications

After the BERT era, this article reviews the limitations of pre‑trained language models for semantic matching, discusses negative‑sample sampling, data‑augmentation techniques, contrastive learning methods such as ConSERT and SimCSE, and practical deployment considerations in vector‑based retrieval systems.

contrastive learningdata augmentationpretrained language models
0 likes · 20 min read
Advances and Challenges in Post‑BERT Semantic Matching: Negative Sampling, Data Augmentation, and Applications
HaoDF Tech Team
HaoDF Tech Team
Sep 15, 2021 · Artificial Intelligence

Optimizing Question‑Answer Search Similarity in Haodf Online: A Semantic Similarity Model Case Study

This article describes how Haodf Online improved its medical question‑answer search by analyzing search challenges, adopting semantic similarity models based on pre‑trained language embeddings, designing contrastive training tasks, and evaluating the resulting increase in click‑through rate and user engagement.

Model Optimizationmedical-ainatural language processing
0 likes · 12 min read
Optimizing Question‑Answer Search Similarity in Haodf Online: A Semantic Similarity Model Case Study
DataFunTalk
DataFunTalk
Jun 6, 2021 · Artificial Intelligence

ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

ConSERT introduces a contrastive self‑supervised framework that enhances BERT‑derived sentence embeddings by applying efficient embedding‑level data augmentations, achieving significant improvements on semantic textual similarity tasks, especially in low‑resource settings, and outperforming previous state‑of‑the‑art methods.

BERTcontrastive learningself-supervised
0 likes · 20 min read
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 18, 2019 · Artificial Intelligence

From Word2Vec to Quick-Thought: A Complete Guide to Modern Embeddings

This article reviews the evolution of word and sentence embeddings, covering foundational theories like vector semantics and distributional hypothesis, practical models such as Word2Vec, GloVe, fastText, Skip‑Thought, Quick‑Thought, and evaluation techniques, while offering implementation tips and real‑world use cases.

GloVeNLPWord2Vec
0 likes · 21 min read
From Word2Vec to Quick-Thought: A Complete Guide to Modern Embeddings