W. Bruce Croft

research

∙ 07/06/2023

Dense Retrieval Adaptation using Target Domain Description

In information retrieval (IR), domain adaptation is the process of adapt...

0 Helia Hashemi, et al. ∙

research

∙ 04/18/2023

Generalized Weak Supervision for Neural Information Retrieval

Neural ranking models (NRMs) have demonstrated effective performance in ...

0 Yen-Chieh Lien, et al. ∙

research

∙ 08/23/2021

Evaluating Fairness in Argument Retrieval

Existing commercial search engines often struggle to represent different...

0 Sachin Pathiyan Cherumanal, et al. ∙

research

∙ 07/12/2021

Asking Clarifying Questions Based on Negative Feedback in Conversational Search

Users often need to look through multiple search result pages or reformu...

0 Keping Bi, et al. ∙

research

∙ 05/09/2021

Passage Retrieval for Outside-Knowledge Visual Question Answering

In this work, we address multi-modal information needs that contain text...

0 Chen Qu, et al. ∙

research

∙ 03/16/2021

A Neural Passage Model for Ad-hoc Document Retrieval

Traditional statistical retrieval models often treat each document as a ...

0 Qingyao Ai, et al. ∙

research

∙ 03/03/2021

Weakly-Supervised Open-Retrieval Conversational Question Answering

Recent studies on Question Answering (QA) and Conversational QA (ConvQA)...

1 Chen Qu, et al. ∙

research

∙ 01/09/2021

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

Users install many apps on their smartphones, raising issues related to ...

4 Mohammad Aliannejadi, et al. ∙

research

∙ 06/13/2020

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

Asking clarifying questions in response to ambiguous or faceted queries ...

0 Helia Hashemi, et al. ∙

research

∙ 05/22/2020

Open-Retrieval Conversational Question Answering

Conversational search is one of the ultimate goals of information retrie...

0 Chen Qu, et al. ∙

research

∙ 05/18/2020

A Transformer-based Embedding Model for Personalized Product Search

Product search is an important way for people to browse and purchase ite...

0 Keping Bi, et al. ∙

research

∙ 04/20/2020

A Review-based Transformer Model for Personalized Product Search

In product search, customers make purchase decisions based on not only t...

0 Keping Bi, et al. ∙

research

∙ 04/13/2020

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

Redundancy-aware extractive summarization systems score the redundancy o...

0 Keping Bi, et al. ∙

research

∙ 02/03/2020

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

Personal assistant systems, such as Apple Siri, Google Assistant, Amazon...

0 Liu Yang, et al. ∙

research

∙ 09/16/2019

Explainable Product Search with a Dynamic Relation Embedding Model

Product search is one of the most popular methods for customers to disco...

0 Qingyao Ai, et al. ∙

research

∙ 09/09/2019

A Study of Context Dependencies in Multi-page Product Search

In product search, users tend to browse results on multiple search resul...

0 Keping Bi, et al. ∙

research

∙ 09/04/2019

Conversational Product Search Based on Negative Feedback

Intelligent assistants change the way people interact with computers and...

0 Keping Bi, et al. ∙

research

∙ 09/04/2019

Leverage Implicit Feedback for Context-aware Product Search

Product search serves as an important entry point for online shopping. I...

0 Keping Bi, et al. ∙

research

∙ 08/29/2019

A Zero Attention Model for Personalized Product Search

Product search is one of the most popular methods for people to discover...

0 Qingyao Ai, et al. ∙

research

∙ 08/26/2019

Attentive History Selection for Conversational Question Answering

Conversational question answering (ConvQA) is a simplified but concrete ...

0 Chen Qu, et al. ∙

research

∙ 07/15/2019

Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

Users often fail to formulate their complex information needs in a singl...

3 Mohammad Aliannejadi, et al. ∙

research

∙ 05/22/2019

ANTIQUE: A Non-Factoid Question Answering Benchmark

Considering the widespread use of mobile and voice search, answer passag...

0 Helia Hashemi, et al. ∙

research

∙ 05/14/2019

BERT with History Answer Embedding for Conversational Question Answering

Conversational search is an emerging topic in the information retrieval ...

0 Chen Qu, et al. ∙

research

∙ 05/05/2019

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

The bidirectional encoder representations from transformers (BERT) model...

0 Harshith Padigela, et al. ∙

research

∙ 04/19/2019

A Hybrid Retrieval-Generation Neural Conversation Model

Intelligent personal assistant systems, with either text-based or voice-...

0 Liu Yang, et al. ∙

research

∙ 03/16/2019

A Deep Look into Neural Ranking Models for Information Retrieval

Ranking models lie at the heart of research on information retrieval (IR...

0 Jiafeng Guo, et al. ∙

research

∙ 12/30/2018

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

Deep text matching approaches have been widely studied for many applicat...

0 Chen Qu, et al. ∙

research

∙ 12/20/2018

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

Relevance feedback techniques assume that users provide relevance judgme...

0 Keping Bi, et al. ∙

research

∙ 12/13/2018

Revisiting Iterative Relevance Feedback for Document and Passage Retrieval

As more and more search traffic comes from mobile phones, intelligent as...

0 Keping Bi, et al. ∙

research

∙ 07/15/2018

Joint Modeling and Optimization of Search and Recommendation

Despite the somewhat different techniques used in developing search engi...

0 Hamed Zamani, et al. ∙

research

∙ 06/14/2018

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

Building multi-turn information-seeking conversation systems is an impor...

0 Minghui Qiu, et al. ∙

research

∙ 06/13/2018

Towards Theoretical Understanding of Weak Supervision for Information Retrieval

Neural network approaches have recently shown to be effective in several...

0 Hamed Zamani, et al. ∙

research

∙ 06/11/2018

Distributed Evaluations: Ending Neural Point Metrics

With the rise of neural models across the field of information retrieval...

0 Daniel Cohen, et al. ∙

research

∙ 05/10/2018

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

With the rise in mobile and voice search, answer passage retrieval acts ...

0 Daniel Cohen, et al. ∙

research

∙ 05/09/2018

Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning

Unlike traditional learning to rank models that depend on hand-crafted f...

0 Daniel Cohen, et al. ∙

research

∙ 05/06/2018

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

With the recent growth of conversational systems and intelligent assista...

0 Mohammad Aliannejadi, et al. ∙

research

∙ 05/01/2018

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...

0 Liu Yang, et al. ∙

research

∙ 04/23/2018

Analyzing and Characterizing User Intent in Information-seeking Conversations

Understanding and characterizing how people interact in information-seek...

0 Chen Qu, et al. ∙

research

∙ 04/16/2018

Unbiased Learning to Rank with Unbiased Propensity Estimation

Learning to rank with biased click data is a well-known challenge. A var...

0 Qingyao Ai, et al. ∙

research

∙ 04/16/2018

Learning a Deep Listwise Context Model for Ranking Refinement

Learning to rank has been intensively studied and widely applied in info...

0 Qingyao Ai, et al. ∙

research

∙ 01/05/2018

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

As an alternative to question answering methods based on feature enginee...

0 Liu Yang, et al. ∙

research

∙ 11/23/2017

A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs...

0 Jiafeng Guo, et al. ∙

research

∙ 05/09/2017

Relevance-based Word Embedding

Learning a high-dimensional dense representation for vocabulary terms, a...

0 Hamed Zamani, et al. ∙

research

∙ 04/28/2017

Neural Ranking Models with Weak Supervision

Despite the impressive improvements achieved by unsupervised deep neural...

0 Mostafa Dehghani, et al. ∙

research

∙ 06/24/2016

Adaptability of Neural Networks on Varying Granularity IR Tasks

Recent work in Information Retrieval (IR) using Deep Learning models has...

0 Daniel Cohen, et al. ∙

W. Bruce Croft

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