W. Bruce Croft
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View article: Generalized Weak Supervision for Neural Information Retrieval
Generalized Weak Supervision for Neural Information Retrieval Open
Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtain. To address this …
View article: Dense Retrieval Adaptation using Target Domain Description
Dense Retrieval Adaptation using Target Domain Description Open
In information retrieval (IR), domain adaptation is the process of adapting a retrieval model to a new domain whose data distribution is different from the source domain. Existing methods in this area focus on unsupervised domain adaptatio…
View article: Predicting the Citation Count and CiteScore of Journals One Year in Advance
Predicting the Citation Count and CiteScore of Journals One Year in Advance Open
Prediction of the future performance of academic journals is a task that can benefit a variety of stakeholders including editorial staff, publishers, indexing services, researchers, university administrators and granting agencies. Using hi…
View article: Evaluating Fairness in Argument Retrieval
Evaluating Fairness in Argument Retrieval Open
Existing commercial search engines often struggle to represent different perspectives of a search query. Argument retrieval systems address this limitation of search engines and provide both positive (PRO) and negative (CON) perspectives a…
View article: Context-aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants
Context-aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants Open
Users install many apps on their smartphones, raising issues related to information overload for users and resource management for devices. Moreover, the recent increase in the use of personal assistants has made mobile devices even more p…
View article: Weakly-Supervised Open-Retrieval Conversational Question Answering
Weakly-Supervised Open-Retrieval Conversational Question Answering Open
Recent studies on Question Answering (QA) and Conversational QA (ConvQA) emphasize the role of retrieval: a system first retrieves evidence from a large collection and then extracts answers. This open-retrieval ConvQA setting typically ass…
View article: A Transformer-based Embedding Model for Personalized Product Search
A Transformer-based Embedding Model for Personalized Product Search Open
Product search is an important way for people to browse and purchase items on E-commerce platforms. While customers tend to make choices based on their personal tastes and preferences, analysis of commercial product search logs has shown t…
View article: Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search
Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search Open
Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, especially conversational search systems with limited bandwidth interfaces. Analyz…
View article: Collaborative Tagging of Phenotypic Data for Clinical and Translational Sciences
Collaborative Tagging of Phenotypic Data for Clinical and Translational Sciences Open
To fully understand results derived from genetic research, a patient’s genotype data must be integrated with other information about the individual (vital signs, height/weight, lab values, disease history – the phenotype of the patient) th…
View article: IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems Open
Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarification questions, potential answers and user feedback in in…
View article: A Review-based Transformer Model for Personalized Product Search
A Review-based Transformer Model for Personalized Product Search Open
In product search, customers make purchase decisions based on not only the product relevance but also their personal preferences. Despite its great potential, recent analysis on commercial logs shows that personalization does not always im…
View article: AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization Open
Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step. Previous work shows th…
View article: IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems Open
Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarification questions, potential answers and user feedback in in…
View article: Design and Applications of Differentially Private Mechanisms: Adherence to Query Range Constraints and Obfuscation of Facial Images
Design and Applications of Differentially Private Mechanisms: Adherence to Query Range Constraints and Obfuscation of Facial Images Open
Collection and dissemination of data are common tasks motivated by numerous benefits attained through the analysis of rich datasets. Yet many datasets contain sensitive information about individuals which must be duly protected if the data…
View article: Conversational Product Search Based on Negative Feedback
Conversational Product Search Based on Negative Feedback Open
Intelligent assistants change the way people interact with computers and make it possible for people to search for products through conversations when they have purchase needs. During the interactions, the system could ask questions on cer…
View article: A Hybrid Retrieval-Generation Neural Conversation Model
A Hybrid Retrieval-Generation Neural Conversation Model Open
Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on using either retrieval-based or generation-based methods t…
View article: Explainable Product Search with a Dynamic Relation Embedding Model
Explainable Product Search with a Dynamic Relation Embedding Model Open
Product search is one of the most popular methods for customers to discover products online. Most existing studies on product search focus on developing effective retrieval models that rank items by their likelihood to be purchased. They, …
View article: Leverage Implicit Feedback for Context-aware Product Search
Leverage Implicit Feedback for Context-aware Product Search Open
Product search serves as an important entry point for online shopping. In contrast to web search, the retrieved results in product search not only need to be relevant but also should satisfy customers' preferences in order to elicit purcha…
View article: Investigating the Successes and Failures of BERT for Passage Re-Ranking
Investigating the Successes and Failures of BERT for Passage Re-Ranking Open
The bidirectional encoder representations from transformers (BERT) model has recently advanced the state-of-the-art in passage re-ranking. In this paper, we analyze the results produced by a fine-tuned BERT model to better understand the r…
View article: Retrieval and Evaluation Techniquesfor Personal Information
Retrieval and Evaluation Techniquesfor Personal Information Open
Providing an effective mechanism for personal information retrieval is important for many applications, and requires different techniques than have been developed for general web search. This thesis focuses on developing retrieval models a…
View article: A Hybrid Retrieval-Generation Neural Conversation Model
A Hybrid Retrieval-Generation Neural Conversation Model Open
Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on using either retrieval-based or generation-based methods t…
View article: A Deep Look into Neural Ranking Models for Information Retrieval
A Deep Look into Neural Ranking Models for Information Retrieval Open
Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to mode…
View article: How do computer scientists use google scholar?: A survey of user interest in elements on SERPs and author profile pages
How do computer scientists use google scholar?: A survey of user interest in elements on SERPs and author profile pages Open
In this paper, we explore user interest in elements on Google Scholar’s search engine result pages (SERPs) and author profile pages (APPs) through a survey in order to predict search behavior. We investigate the effects of different query …
View article: The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval
The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval Open
Constantine Lignos, Daniel Cohen, Yen-Chieh Lien, Pratik Mehta, W. Bruce Croft, Scott Miller. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Lang…
View article: Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching Open
Deep text matching approaches have been widely studied for many applications including question answering and information retrieval systems. To deal with a domain that has insufficient labeled data, these approaches can be used in a Transf…
View article: Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match
Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match Open
Relevance feedback techniques assume that users provide relevance judgments for the top k (usually 10) documents and then re-rank using a new query model based on those judgments. Even though this is effective, there has been little resear…
View article: Revisiting Iterative Relevance Feedback for Document and Passage Retrieval
Revisiting Iterative Relevance Feedback for Document and Passage Retrieval Open
As more and more search traffic comes from mobile phones, intelligent assistants, and smart-home devices, new challenges (e.g., limited presentation space) and opportunities come up in information retrieval. Previously, an effective techni…
View article: Towards Conversational Search and Recommendation
Towards Conversational Search and Recommendation Open
Conversational search and recommendation based on user-system dialogs exhibit major differences from conventional search and recommendation tasks in that 1) the user and system can interact for multiple semantically coherent rounds on a ta…
View article: From Neural Re-Ranking to Neural Ranking
From Neural Re-Ranking to Neural Ranking Open
The availability of massive data and computing power allowing for effective data driven neural approaches is having a major impact on machine learning and information retrieval research, but these models have a basic problem with efficienc…