241 citations · 686 across the 25 of their papers we have counts for
17 papers · 1 filter
Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain
Andrew Yates, Michael Unterkalmsteiner
Domain-specific synonyms occur in many specialized search tasks, such as when searching medical documents, legal documents, and software engineering artifacts. We replicate prior w…
Zero-shot Query Contextualization for Conversational Search
Antonios Minas Krasakis, Andrew Yates, Evangelos Kanoulas
Current conversational passage retrieval systems cast conversational search into ad-hoc search by using an intermediate query resolution step that places the user's question in con…
You Get What You Chat: Using Conversations to Personalize Search-based Recommendations
Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum
Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes, and ratings. This paper investigates tapping into a…
Personalized Entity Search by Sparse and Scrutable User Profiles
Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum
Prior work on personalizing web search results has focused on considering query-and-click logs to capture users individual interests. For product search, extensive user histories a…
How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset
Iain Mackie, Jeffery Dalton, Andrew Yates
Deep Learning Hard (DL-HARD) is a new annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) topics b…
Simplified Data Wrangling with ir_datasets
Sean MacAvaney, Andrew Yates, Sergey Feldman +3
Managing the data for Information Retrieval (IR) experiments can be challenging. Dataset documentation is scattered across the Internet and once one obtains a copy of the data, the…
Pretrained Transformers for Text Ranking: BERT and Beyond
Jimmy Lin, Rodrigo Nogueira, Andrew Yates
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, ins…
BERT-QE: Contextualized Query Expansion for Document Re-ranking
Zhi Zheng, Kai Hui, Ben He +3
Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant info…
PARADE: Passage Representation Aggregation for Document Reranking
Canjia Li, Andrew Yates, Sean MacAvaney +2
Pretrained transformer models, such as BERT and T5, have shown to be highly effective at ad-hoc passage and document ranking. Due to inherent sequence length limits of these models…
Investigating Retrieval Method Selection with Axiomatic Features
Siddhant Arora, Andrew Yates
We consider algorithm selection in the context of ad-hoc information retrieval. Given a query and a pair of retrieval methods, we propose a meta-learner that predicts how to combin…
CEDR: Contextualized Embeddings for Document Ranking
Sean MacAvaney, Andrew Yates, Arman Cohan +1
Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to thes…
Overcoming low-utility facets for complex answer retrieval
Sean MacAvaney, Andrew Yates, Arman Cohan +4
Many questions cannot be answered simply; their answers must include numerous nuanced details and additional context. Complex Answer Retrieval (CAR) is the retrieval of answers to…
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Canjia Li, Yingfei Sun, Ben He +5
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…
Characterizing Question Facets for Complex Answer Retrieval
Sean MacAvaney, Andrew Yates, Arman Cohan +4
Complex answer retrieval (CAR) is the process of retrieving answers to questions that have multifaceted or nuanced answers. In this work, we present two novel approaches for CAR ba…
DE-PACRR: Exploring Layers Inside the PACRR Model
Andrew Yates, Kai Hui
Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval. However, deep models have a reputation for being black boxes, and the roles of a…
Content-Based Weak Supervision for Ad-Hoc Re-Ranking
Sean MacAvaney, Andrew Yates, Kai Hui +1
One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak super…
PACRR: A Position-Aware Neural IR Model for Relevance Matching
Kai Hui, Andrew Yates, Klaus Berberich +1
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 use…