18 citations · 48 across the 14 of their papers we have counts for
15 papers
On the Practice of Scaling Search Conversion Rate Prediction
James Pak, Jyun-Yu Jiang, Fan Zhang +13
Scaling a Search Conversion Rate (CVR) prediction model, especially in high-traffic environments, presents a challenge: superior model quality needs to be balanced with strict cons…
Beyond Independent Passages: Adaptive Passage Combination Retrieval for Retrieval Augmented Open-Domain Question Answering
Ting-Wen Ko, Jyun-Yu Jiang, Pu-Jen Cheng
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external documents at inference time, enabling up-to-date knowledge access without costl…
Retrieval-augmented Encoders for Extreme Multi-label Text Classification
Yau-Shian Wang, Wei-Cheng Chang, Jyun-Yu Jiang +3
Extreme multi-label classification (XMC) seeks to find relevant labels from an extremely large label collection for a given text input. To tackle such a vast label space, current s…
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models
Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang +5
Embedding-based Retrieval Models (ERMs) have emerged as a promising framework for large-scale text retrieval problems due to powerful large language models. Nevertheless, fine-tuni…
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering
Xiusi Chen, Jyun-Yu Jiang, Wei-Cheng Chang +3
Recent advances in few-shot question answering (QA) mostly rely on the power of pre-trained large language models (LLMs) and fine-tuning in specific settings. Although the pre-trai…
printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning
Hao-Lun Lin, Jyun-Yu Jiang, Ming-Hao Juan +1
Nowadays, modern recommender systems usually leverage textual and visual contents as auxiliary information to predict user preference. For textual information, review texts are one…
Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation
Xiusi Chen, Yu Zhang, Jinliang Deng +2
Few-shot question answering (QA) aims at precisely discovering answers to a set of questions from context passages while only a few training samples are available. Although existin…
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Eli Chien, Jiong Zhang, Cho-Jui Hsieh +4
The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extractio…
InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks
Seungbae Kim, Jyun-Yu Jiang, Jinyoung Han +1
As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influenc…
Uncertainty in Extreme Multi-label Classification
Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhong +2
Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain ha…
#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs
Yichao Zhou, Jyun-yu Jiang, Xiusi Chen +1
COVID-19 has caused lasting damage to almost every domain in public health, society, and economy. To monitor the pandemic trend, existing studies rely on the aggregation of traditi…
Drug-Target Interaction Prediction with Graph Attention networks
Haiyang Wang, Guangyu Zhou, Siqi Liu +2
Motivation: Predicting Drug-Target Interaction (DTI) is a well-studied topic in bioinformatics due to its relevance in the fields of proteomics and pharmaceutical research. Althoug…
Long Document Ranking with Query-Directed Sparse Transformer
Jyun-Yu Jiang, Chenyan Xiong, Chia-Jung Lee +1
The computing cost of transformer self-attention often necessitates breaking long documents to fit in pretrained models in document ranking tasks. In this paper, we design Query-Di…
"The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition
Yichao Zhou, Jyun-Yu Jiang, Jieyu Zhao +2
Humor plays an important role in human languages and it is essential to model humor when building intelligence systems. Among different forms of humor, puns perform wordplay for hu…
Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification
Yichao Zhou, Jyun-Yu Jiang, Kai-Wei Chang +1
Adversarial attacks against machine learning models have threatened various real-world applications such as spam filtering and sentiment analysis. In this paper, we propose a novel…