Search Results for author: Bhaskar Mitra

Found 37 papers, 9 papers with code

Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness

no code implementations15 Oct 2021 Nicola Neophytou, Bhaskar Mitra, Catherine Stinson

We find statistically significant differences in recommender performance by both age and gender.

Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking

1 code implementation20 May 2021 Sebastian Hofstätter, Bhaskar Mitra, Hamed Zamani, Nick Craswell, Allan Hanbury

An emerging recipe for achieving state-of-the-art effectiveness in neural document re-ranking involves utilizing large pre-trained language models - e. g., BERT - to evaluate all individual passages in the document and then aggregating the outputs by pooling or additional Transformer layers.

Document Ranking Knowledge Distillation +1

Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models

no code implementations10 May 2021 Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz, Carsten Eickhoff

In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query.

MS MARCO: Benchmarking Ranking Models in the Large-Data Regime

no code implementations9 May 2021 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Jimmy Lin

Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big questions in our field.

Multi-FR: A Multi-Objective Optimization Method for Achieving Two-sided Fairness in E-commerce Recommendation

no code implementations6 May 2021 Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, Xue Liu

Two-sided marketplaces are an important component of many existing Internet services like Airbnb and Amazon, which have both consumers (e. g. users) and producers (e. g. retailers).


Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence

no code implementations19 Apr 2021 Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani, Nick Craswell

The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark -- and can be considered to be an efficient (but slightly less effective) alternative to other Transformer-based architectures that employ (i) large-scale pretraining (high training cost), (ii) joint encoding of query and document (high inference cost), and (iii) larger number of Transformer layers (both high training and high inference costs).

Document Ranking

TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime

no code implementations19 Apr 2021 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Ellen M. Voorhees, Ian Soboroff

The TREC Deep Learning (DL) Track studies ad hoc search in the large data regime, meaning that a large set of human-labeled training data is available.

Selection bias

Overview of the TREC 2020 deep learning track

1 code implementation15 Feb 2021 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos

This is the second year of the TREC Deep Learning Track, with the goal of studying ad hoc ranking in the large training data regime.

Passage Retrieval

Tip of the Tongue Known-Item Retrieval: A Case Study in Movie Identification

no code implementations18 Jan 2021 Jaime Arguello, Adam Ferguson, Emery Fine, Bhaskar Mitra, Hamed Zamani, Fernando Diaz

Using movie search as a case study, we explore the characteristics of questions posed by searchers in TOT states in a community question answering website.

Community Question Answering Information Retrieval

Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval

no code implementations21 Dec 2020 Bhaskar Mitra

In many real-life IR tasks, the retrieval involves extremely large collections--such as the document index of a commercial Web search engine--containing billions of documents.

Information Retrieval Speech Recognition

Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track

no code implementations14 Nov 2020 Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani, Nick Craswell

We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track.

Semantic Product Search for Matching Structured Product Catalogs in E-Commerce

no code implementations18 Aug 2020 Jason Ingyu Choi, Surya Kallumadi, Bhaskar Mitra, Eugene Agichtein, Faizan Javed

Retrieving all semantically relevant products from the product catalog is an important problem in E-commerce.

Conformer-Kernel with Query Term Independence for Document Retrieval

1 code implementation20 Jul 2020 Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani, Nick Craswell

In this work, we extend the TK architecture to the full retrieval setting by incorporating the query term independence assumption.

ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search

no code implementations9 Jun 2020 Nick Craswell, Daniel Campos, Bhaskar Mitra, Emine Yilmaz, Bodo Billerbeck

Users of Web search engines reveal their information needs through queries and clicks, making click logs a useful asset for information retrieval.

Information Retrieval

Analyzing and Learning from User Interactions for Search Clarification

no code implementations30 May 2020 Hamed Zamani, Bhaskar Mitra, Everest Chen, Gord Lueck, Fernando Diaz, Paul N. Bennett, Nick Craswell, Susan T. Dumais

We also propose a model for learning representation for clarifying questions based on the user interaction data as implicit feedback.


Local Self-Attention over Long Text for Efficient Document Retrieval

1 code implementation11 May 2020 Sebastian Hofstätter, Hamed Zamani, Bhaskar Mitra, Nick Craswell, Allan Hanbury

In this work, we propose a local self-attention which considers a moving window over the document terms and for each term attends only to other terms in the same window.

Document Ranking

On the Reliability of Test Collections for Evaluating Systems of Different Types

no code implementations28 Apr 2020 Emine Yilmaz, Nick Craswell, Bhaskar Mitra, Daniel Campos

As deep learning based models are increasingly being used for information retrieval (IR), a major challenge is to ensure the availability of test collections for measuring their quality.

Fairness Information Retrieval +1

Evaluating Stochastic Rankings with Expected Exposure

no code implementations27 Apr 2020 Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette

We introduce the concept of \emph{expected exposure} as the average attention ranked items receive from users over repeated samples of the same query.

Information Retrieval

Fault Location Using the Natural Frequency of Oscillation of Current Discharge in MTdc Networks

no code implementations13 Apr 2020 Bhaskar Mitra, Suman Debanth, Badrul Chowdhury

A relationship between the damped natural frequency of oscillation of the transmission line current and fault location is established in this paper.

Overview of the TREC 2019 deep learning track

2 code implementations17 Mar 2020 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Ellen M. Voorhees

The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime.

Passage Retrieval Transfer Learning

Duet at TREC 2019 Deep Learning Track

1 code implementation10 Dec 2019 Bhaskar Mitra, Nick Craswell

This report discusses three submissions based on the Duet architecture to the Deep Learning track at TREC 2019.

Learning-To-Rank Passage Retrieval

Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks

no code implementations8 Jul 2019 Bhaskar Mitra, Corby Rosset, David Hawking, Nick Craswell, Fernando Diaz, Emine Yilmaz

Deep neural IR models, in contrast, compare the whole query to the document and are, therefore, typically employed only for late stage re-ranking.

Information Retrieval Re-Ranking

An Axiomatic Approach to Regularizing Neural Ranking Models

no code implementations15 Apr 2019 Corby Rosset, Bhaskar Mitra, Chenyan Xiong, Nick Craswell, Xia Song, Saurabh Tiwary

The training of these models involve a search for appropriate parameter values based on large quantities of labeled examples.

Information Retrieval

An Updated Duet Model for Passage Re-ranking

1 code implementation18 Mar 2019 Bhaskar Mitra, Nick Craswell

We propose several small modifications to Duet---a deep neural ranking model---and evaluate the updated model on the MS MARCO passage ranking task.

Passage Re-Ranking Re-Ranking

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

Optimizing Query Evaluations using Reinforcement Learning for Web Search

no code implementations12 Apr 2018 Corby Rosset, Damien Jose, Gargi Ghosh, Bhaskar Mitra, Saurabh Tiwary

In web search, typically a candidate generation step selects a small set of documents---from collections containing as many as billions of web pages---that are subsequently ranked and pruned before being presented to the user.

Reply With: Proactive Recommendation of Email Attachments

no code implementations17 Oct 2017 Christophe Van Gysel, Bhaskar Mitra, Matteo Venanzi, Roy Rosemarin, Grzegorz Kukla, Piotr Grudzien, Nicola Cancedda

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message.

Toward Incorporation of Relevant Documents in word2vec

no code implementations20 Jul 2017 Navid Rekabsaz, Bhaskar Mitra, Mihai Lupu, Allan Hanbury

As an alternative, explicit word representations propose vectors whose dimensions are easily interpretable, and recent methods show competitive performance to the dense vectors.

Information Retrieval Word Embeddings

Neural Networks for Information Retrieval

no code implementations13 Jul 2017 Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra

Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them.

Information Retrieval

Neural Models for Information Retrieval

no code implementations3 May 2017 Bhaskar Mitra, Nick Craswell

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to rank search results in response to a query.

Information Retrieval Learning-To-Rank

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

11 code implementations28 Nov 2016 Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang

The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering.

Machine Reading Comprehension Question Answering

Learning to Match Using Local and Distributed Representations of Text for Web Search

1 code implementation Proceedings of the 26th International Conference on World Wide Web, WWW '17 2017 Bhaskar Mitra, Fernando Diaz, Nick Craswell

Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space.

Document Ranking Information Retrieval

Query Expansion with Locally-Trained Word Embeddings

no code implementations ACL 2016 Fernando Diaz, Bhaskar Mitra, Nick Craswell

Continuous space word embeddings have received a great deal of attention in the natural language processing and machine learning communities for their ability to model term similarity and other relationships.

Ad-Hoc Information Retrieval Information Retrieval +1

A Dual Embedding Space Model for Document Ranking

no code implementations2 Feb 2016 Bhaskar Mitra, Eric Nalisnick, Nick Craswell, Rich Caruana

A fundamental goal of search engines is to identify, given a query, documents that have relevant text.

Document Ranking Word Embeddings

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