Search Results for author: W. Bruce Croft

Found 41 papers, 19 papers with code

Evaluating Fairness in Argument Retrieval

2 code implementations23 Aug 2021 Sachin Pathiyan Cherumanal, Damiano Spina, Falk Scholer, W. Bruce Croft

In this work, we analyze a range of non-stochastic fairness-aware ranking and diversity metrics to evaluate the extent to which argument stances are fairly exposed in argument retrieval systems.

Argument Retrieval Fairness

Asking Clarifying Questions Based on Negative Feedback in Conversational Search

no code implementations12 Jul 2021 Keping Bi, Qingyao Ai, W. Bruce Croft

To quickly identify user intent and reduce effort during interactions, we propose an intent clarification task based on yes/no questions where the system needs to ask the correct question about intents within the fewest conversation turns.

Conversational Search

Passage Retrieval for Outside-Knowledge Visual Question Answering

1 code implementation9 May 2021 Chen Qu, Hamed Zamani, Liu Yang, W. Bruce Croft, Erik Learned-Miller

We first conduct sparse retrieval with BM25 and study expanding the question with object names and image captions.

Image Captioning Passage Retrieval +2

A Neural Passage Model for Ad-hoc Document Retrieval

no code implementations16 Mar 2021 Qingyao Ai, Brendan O Connor, W. Bruce Croft

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

Document Ranking

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

1 code implementation9 Jan 2021 Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, W. Bruce Croft

Here we focus on context-aware models to leverage the rich contextual information available to mobile devices.

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

no code implementations13 Jun 2020 Helia Hashemi, Hamed Zamani, W. Bruce Croft

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.

Conversational Search Information Retrieval +1

Open-Retrieval Conversational Question Answering

1 code implementation22 May 2020 Chen Qu, Liu Yang, Cen Chen, Minghui Qiu, W. Bruce Croft, Mohit Iyyer

We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers.

Conversational Question Answering Conversational Search +1

A Transformer-based Embedding Model for Personalized Product Search

no code implementations18 May 2020 Keping Bi, Qingyao Ai, W. Bruce Croft

Aware of these limitations, we propose a transformer-based embedding model (TEM) for personalized product search, which could dynamically control the influence of personalization by encoding the sequence of query and user's purchase history with a transformer architecture.

Learning a Fine-Grained Review-based Transformer Model for Personalized Product Search

no code implementations20 Apr 2020 Keping Bi, Qingyao Ai, W. Bruce Croft

RTM conducts review-level matching between the user and item, where each review has a dynamic effect according to the context in the sequence.

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

3 code implementations EACL 2021 Keping Bi, Rahul Jha, W. Bruce Croft, Asli Celikyilmaz

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.

Document Summarization Extractive Document Summarization +2

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

1 code implementation3 Feb 2020 Liu Yang, Minghui Qiu, Chen Qu, Cen Chen, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Haiqing Chen

We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.

Representation Learning

Explainable Product Search with a Dynamic Relation Embedding Model

no code implementations16 Sep 2019 Qingyao Ai, Yongfeng Zhang, Keping Bi, W. Bruce Croft

Specifically, we propose to model the "search and purchase" behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session.

A Study of Context Dependencies in Multi-page Product Search

no code implementations9 Sep 2019 Keping Bi, Choon Hui Teo, Yesh Dattatreya, Vijai Mohan, W. Bruce Croft

In this paper, we study RF techniques based on both long-term and short-term context dependencies in multi-page product search.

Conversational Product Search Based on Negative Feedback

no code implementations4 Sep 2019 Keping Bi, Qingyao Ai, Yongfeng Zhang, W. Bruce Croft

So in this paper, we propose a conversational paradigm for product search driven by non-relevant items, based on which fine-grained feedback is collected and utilized to show better results in the next iteration.

Conversational Search

Leverage Implicit Feedback for Context-aware Product Search

no code implementations4 Sep 2019 Keping Bi, Choon Hui Teo, Yesh Dattatreya, Vijai Mohan, W. Bruce Croft

However, customers with little or no purchase history do not benefit from personalized product search.

Re-Ranking

A Zero Attention Model for Personalized Product Search

no code implementations29 Aug 2019 Qingyao Ai, Daniel N. Hill, S. V. N. Vishwanathan, W. Bruce Croft

In this paper, we formulate the problem of personalized product search and conduct large-scale experiments with search logs sampled from a commercial e-commerce search engine.

Attentive History Selection for Conversational Question Answering

2 code implementations26 Aug 2019 Chen Qu, Liu Yang, Minghui Qiu, Yongfeng Zhang, Cen Chen, W. Bruce Croft, Mohit Iyyer

First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.

Conversational Question Answering Conversational Search +1

Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

1 code implementation15 Jul 2019 Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, W. Bruce Croft

In this paper, we formulate the task of asking clarifying questions in open-domain information-seeking conversational systems.

ANTIQUE: A Non-Factoid Question Answering Benchmark

1 code implementation22 May 2019 Helia Hashemi, Mohammad Aliannejadi, Hamed Zamani, W. Bruce Croft

Despite the importance of the task, the community still feels the significant lack of large-scale non-factoid question answering collections with real questions and comprehensive relevance judgments.

Community Question Answering Passage Retrieval

BERT with History Answer Embedding for Conversational Question Answering

1 code implementation14 May 2019 Chen Qu, Liu Yang, Minghui Qiu, W. Bruce Croft, Yongfeng Zhang, Mohit Iyyer

One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question.

Conversational Question Answering Conversational Search +1

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

no code implementations5 May 2019 Harshith Padigela, Hamed Zamani, W. Bruce Croft

The bidirectional encoder representations from transformers (BERT) model has recently advanced the state-of-the-art in passage re-ranking.

Passage Re-Ranking Re-Ranking

A Hybrid Retrieval-Generation Neural Conversation Model

1 code implementation19 Apr 2019 Liu Yang, Junjie Hu, Minghui Qiu, Chen Qu, Jianfeng Gao, W. Bruce Croft, Xiaodong Liu, Yelong Shen, Jingjing Liu

In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods.

Text Generation

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

no code implementations30 Dec 2018 Chen Qu, Feng Ji, Minghui Qiu, Liu Yang, Zhiyu Min, Haiqing Chen, Jun Huang, W. Bruce Croft

Specifically, the data selector "acts" on the source domain data to find a subset for optimization of the TL model, and the performance of the TL model can provide "rewards" in turn to update the selector.

Information Retrieval Natural Language Inference +4

Revisiting Iterative Relevance Feedback for Document and Passage Retrieval

no code implementations13 Dec 2018 Keping Bi, Qingyao Ai, W. Bruce Croft

We conduct extensive experiments to analyze and compare IRF with the standard top-k RF framework on document and passage retrieval.

Passage Retrieval

Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning

no code implementations9 May 2018 Daniel Cohen, Bhaskar Mitra, Katja Hofmann, W. Bruce Croft

We use an adversarial discriminator and train our neural ranking model on a small set of domains.

Information Retrieval

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

1 code implementation1 May 2018 Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Jun Huang, Haiqing Chen

Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.

Knowledge Distillation Text Matching

Analyzing and Characterizing User Intent in Information-seeking Conversations

no code implementations23 Apr 2018 Chen Qu, Liu Yang, W. Bruce Croft, Johanne R. Trippas, Yongfeng Zhang, Minghui Qiu

Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems.

Conversational Search Question Answering

Unbiased Learning to Rank with Unbiased Propensity Estimation

1 code implementation16 Apr 2018 Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft

We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank.

Learning-To-Rank online learning

Learning a Deep Listwise Context Model for Ranking Refinement

1 code implementation16 Apr 2018 Qingyao Ai, Keping Bi, Jiafeng Guo, W. Bruce Croft

Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results.

Information Retrieval Learning-To-Rank

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

1 code implementation5 Jan 2018 Liu Yang, Qingyao Ai, Jiafeng Guo, W. Bruce Croft

As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers.

Feature Engineering Question Answering

Relevance-based Word Embedding

no code implementations9 May 2017 Hamed Zamani, W. Bruce Croft

This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information.

General Classification Information Retrieval +2

Neural Ranking Models with Weak Supervision

1 code implementation28 Apr 2017 Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn, Jaap Kamps, W. Bruce Croft

Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections.

Ad-Hoc Information Retrieval Information Retrieval

Adaptability of Neural Networks on Varying Granularity IR Tasks

no code implementations24 Jun 2016 Daniel Cohen, Qingyao Ai, W. Bruce Croft

Recent work in Information Retrieval (IR) using Deep Learning models has yielded state of the art results on a variety of IR tasks.

Information Retrieval

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