W. Bruce Croft

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University of Massachusetts Amherst

Distinguished University Professor,,College of Information and Computer Sciences,and Director, Center for Intelligent Information Retrieval at University of Massachusetts Amherst

Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants

Users install many apps on their smartphones, raising issues related to ...
01/09/2021 ∙ by Mohammad Aliannejadi, et al. ∙ 4 ∙ share

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Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search

Asking clarifying questions in response to ambiguous or faceted queries ...
06/13/2020 ∙ by Helia Hashemi, et al. ∙ 0 ∙ share

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Open-Retrieval Conversational Question Answering

Conversational search is one of the ultimate goals of information retrie...
05/22/2020 ∙ by Chen Qu, et al. ∙ 0 ∙ share

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A Transformer-based Embedding Model for Personalized Product Search

Product search is an important way for people to browse and purchase ite...
05/18/2020 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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A Review-based Transformer Model for Personalized Product Search

In product search, customers make purchase decisions based on not only t...
04/20/2020 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization

Redundancy-aware extractive summarization systems score the redundancy o...
04/13/2020 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

Personal assistant systems, such as Apple Siri, Google Assistant, Amazon...
02/03/2020 ∙ by Liu Yang, et al. ∙ 0 ∙ share

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Explainable Product Search with a Dynamic Relation Embedding Model

Product search is one of the most popular methods for customers to disco...
09/16/2019 ∙ by Qingyao Ai, et al. ∙ 0 ∙ share

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A Study of Context Dependencies in Multi-page Product Search

In product search, users tend to browse results on multiple search resul...
09/09/2019 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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Conversational Product Search Based on Negative Feedback

Intelligent assistants change the way people interact with computers and...
09/04/2019 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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Leverage Implicit Feedback for Context-aware Product Search

Product search serves as an important entry point for online shopping. I...
09/04/2019 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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A Zero Attention Model for Personalized Product Search

Product search is one of the most popular methods for people to discover...
08/29/2019 ∙ by Qingyao Ai, et al. ∙ 0 ∙ share

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Attentive History Selection for Conversational Question Answering

Conversational question answering (ConvQA) is a simplified but concrete ...
08/26/2019 ∙ by Chen Qu, et al. ∙ 0 ∙ share

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Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

Users often fail to formulate their complex information needs in a singl...
07/15/2019 ∙ by Mohammad Aliannejadi, et al. ∙ 3 ∙ share

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ANTIQUE: A Non-Factoid Question Answering Benchmark

Considering the widespread use of mobile and voice search, answer passag...
05/22/2019 ∙ by Helia Hashemi, et al. ∙ 0 ∙ share

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BERT with History Answer Embedding for Conversational Question Answering

Conversational search is an emerging topic in the information retrieval ...
05/14/2019 ∙ by Chen Qu, et al. ∙ 0 ∙ share

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Investigating the Successes and Failures of BERT for Passage Re-Ranking

The bidirectional encoder representations from transformers (BERT) model...
05/05/2019 ∙ by Harshith Padigela, et al. ∙ 0 ∙ share

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A Hybrid Retrieval-Generation Neural Conversation Model

Intelligent personal assistant systems, with either text-based or voice-...
04/19/2019 ∙ by Liu Yang, et al. ∙ 0 ∙ share

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A Deep Look into Neural Ranking Models for Information Retrieval

Ranking models lie at the heart of research on information retrieval (IR...
03/16/2019 ∙ by Jiafeng Guo, et al. ∙ 0 ∙ share

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Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching

Deep text matching approaches have been widely studied for many applicat...
12/30/2018 ∙ by Chen Qu, et al. ∙ 0 ∙ share

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Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match

Relevance feedback techniques assume that users provide relevance judgme...
12/20/2018 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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Revisiting Iterative Relevance Feedback for Document and Passage Retrieval

As more and more search traffic comes from mobile phones, intelligent as...
12/13/2018 ∙ by Keping Bi, et al. ∙ 0 ∙ share

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Joint Modeling and Optimization of Search and Recommendation

Despite the somewhat different techniques used in developing search engi...
07/15/2018 ∙ by Hamed Zamani, et al. ∙ 0 ∙ share

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Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce

Building multi-turn information-seeking conversation systems is an impor...
06/14/2018 ∙ by Minghui Qiu, et al. ∙ 0 ∙ share

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Towards Theoretical Understanding of Weak Supervision for Information Retrieval

Neural network approaches have recently shown to be effective in several...
06/13/2018 ∙ by Hamed Zamani, et al. ∙ 0 ∙ share

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Distributed Evaluations: Ending Neural Point Metrics

With the rise of neural models across the field of information retrieval...
06/11/2018 ∙ by Daniel Cohen, et al. ∙ 0 ∙ share

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WikiPassageQA: A Benchmark Collection for Research on Non-factoid Answer Passage Retrieval

With the rise in mobile and voice search, answer passage retrieval acts ...
05/10/2018 ∙ by Daniel Cohen, et al. ∙ 0 ∙ share

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Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning

Unlike traditional learning to rank models that depend on hand-crafted f...
05/09/2018 ∙ by Daniel Cohen, et al. ∙ 0 ∙ share

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Target Apps Selection: Towards a Unified Search Framework for Mobile Devices

With the recent growth of conversational systems and intelligent assista...
05/06/2018 ∙ by Mohammad Aliannejadi, et al. ∙ 0 ∙ share

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Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

Intelligent personal assistant systems with either text-based or voice-b...
05/01/2018 ∙ by Liu Yang, et al. ∙ 0 ∙ share

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Analyzing and Characterizing User Intent in Information-seeking Conversations

Understanding and characterizing how people interact in information-seek...
04/23/2018 ∙ by Chen Qu, et al. ∙ 0 ∙ share

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Unbiased Learning to Rank with Unbiased Propensity Estimation

Learning to rank with biased click data is a well-known challenge. A var...
04/16/2018 ∙ by Qingyao Ai, et al. ∙ 0 ∙ share

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Learning a Deep Listwise Context Model for Ranking Refinement

Learning to rank has been intensively studied and widely applied in info...
04/16/2018 ∙ by Qingyao Ai, et al. ∙ 0 ∙ share

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aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model

As an alternative to question answering methods based on feature enginee...
01/05/2018 ∙ by Liu Yang, et al. ∙ 0 ∙ share

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A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs...
11/23/2017 ∙ by Jiafeng Guo, et al. ∙ 0 ∙ share

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Relevance-based Word Embedding

Learning a high-dimensional dense representation for vocabulary terms, a...
05/09/2017 ∙ by Hamed Zamani, et al. ∙ 0 ∙ share

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Neural Ranking Models with Weak Supervision

Despite the impressive improvements achieved by unsupervised deep neural...
04/28/2017 ∙ by Mostafa Dehghani, et al. ∙ 0 ∙ share

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Adaptability of Neural Networks on Varying Granularity IR Tasks

Recent work in Information Retrieval (IR) using Deep Learning models has...
06/24/2016 ∙ by Daniel Cohen, et al. ∙ 0 ∙ share

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W. Bruce Croft

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