Search Results for author: Kai Hui

Found 11 papers, 7 papers with code

ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning

2 code implementations22 Nov 2021 Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Q. Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, Donald Metzler

Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training.

Denoising Multi-Task Learning

Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing

no code implementations18 Apr 2021 Kai Hui, Klaus Berberich

In this work, we collect judgments from multiple judges using a crowdsourcing platform and aggregate them to compare the two kinds of preference judgments in terms of transitivity, time consumption, and quality.

Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context

no code implementations17 Apr 2021 Xiaoyang Chen, Kai Hui, Ben He, Xianpei Han, Le Sun, Zheng Ye

BERT-based text ranking models have dramatically advanced the state-of-the-art in ad-hoc retrieval, wherein most models tend to consider individual query-document pairs independently.

Learning-To-Rank Re-Ranking

Simplified TinyBERT: Knowledge Distillation for Document Retrieval

1 code implementation16 Sep 2020 Xuanang Chen, Ben He, Kai Hui, Le Sun, Yingfei Sun

Despite the effectiveness of utilizing the BERT model for document ranking, the high computational cost of such approaches limits their uses.

Document Ranking Knowledge Distillation

NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval

1 code implementation EMNLP 2018 Canjia Li, Yingfei Sun, Ben He, Le Wang, Kai Hui, Andrew Yates, Le Sun, Jungang Xu

Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches.

Ad-Hoc Information Retrieval Information Retrieval

Content-Based Weak Supervision for Ad-Hoc Re-Ranking

1 code implementation1 Jul 2017 Sean MacAvaney, Andrew Yates, Kai Hui, Ophir Frieder

One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training.

Information Retrieval Re-Ranking

Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval

3 code implementations30 Jun 2017 Kai Hui, Andrew Yates, Klaus Berberich, Gerard de Melo

Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals.

Ad-Hoc Information Retrieval

DE-PACRR: Exploring Layers Inside the PACRR Model

no code implementations27 Jun 2017 Andrew Yates, Kai Hui

Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval.

Ad-Hoc Information Retrieval Information Retrieval

PACRR: A Position-Aware Neural IR Model for Relevance Matching

3 code implementations EMNLP 2017 Kai Hui, Andrew Yates, Klaus Berberich, Gerard de Melo

In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query.

Ad-Hoc Information Retrieval Information Retrieval

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