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Praveen Paritosh

Other affiliations: Northwestern University
Bio: Praveen Paritosh is an academic researcher from Google. The author has contributed to research in topics: Crowdsourcing & Commonsense reasoning. The author has an hindex of 14, co-authored 39 publications receiving 4497 citations. Previous affiliations of Praveen Paritosh include Northwestern University.

Papers
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Proceedings ArticleDOI
09 Jun 2008
TL;DR: MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.
Abstract: Freebase is a practical, scalable tuple database used to structure general human knowledge. The data in Freebase is collaboratively created, structured, and maintained. Freebase currently contains more than 125,000,000 tuples, more than 4000 types, and more than 7000 properties. Public read/write access to Freebase is allowed through an HTTP-based graph-query API using the Metaweb Query Language (MQL) as a data query and manipulation language. MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.

4,813 citations

Proceedings ArticleDOI
06 May 2021
TL;DR: In this paper, the authors report on data practices in high-stakes AI, from interviews with 53 AI practitioners in India, East and West African countries, and USA, and define, identify, and present empirical evidence on Data Cascades, compounding events causing negative, downstream effects from data issues.
Abstract: AI models are increasingly applied in high-stakes domains like health and conservation. Data quality carries an elevated significance in high-stakes AI due to its heightened downstream impact, impacting predictions like cancer detection, wildlife poaching, and loan allocations. Paradoxically, data is the most under-valued and de-glamorised aspect of AI. In this paper, we report on data practices in high-stakes AI, from interviews with 53 AI practitioners in India, East and West African countries, and USA. We define, identify, and present empirical evidence on Data Cascades—compounding events causing negative, downstream effects from data issues—triggered by conventional AI/ML practices that undervalue data quality. Data cascades are pervasive (92% prevalence), invisible, delayed, but often avoidable. We discuss HCI opportunities in designing and incentivizing data excellence as a first-class citizen of AI, resulting in safer and more robust systems for all.

349 citations

Proceedings ArticleDOI
25 Jul 2010
TL;DR: Rabj, an engine designed to simplify collecting human input, is described and how the architecture and design decisions of Rabj are affected by the constraints of content agnosticity, data freshness, latency and visibility is described.
Abstract: In this paper we describe Rabj, an engine designed to simplify collecting human input. We have used Rabj to collect over 2.3 million human judgments to augment data mining, data entry, and curation tasks at Freebase over the course of a year. We illustrate several successful applications that have used Rabj to collect human judgment. We describe how the architecture and design decisions of Rabj are affected by the constraints of content agnosticity, data freshness, latency and visibility. We present work aimed at increasing the yield and reliability of human computation efforts. Finally, we discuss empirical observations and lessons learned in the course of a year of operating the service.

87 citations

Proceedings Article
03 May 2017
TL;DR: A categorization of coarse discourse acts designed to encompass general online discussion and allow for easy annotation by crowd workers is devised, and the broadening of discourse acts from simply question and answer to a richer set of categories can improve the recall performance of Q&A extraction.
Abstract: In this work, we present a novel method for classifying comments in online discussions into a set of coarse discourse acts towards the goal of better understanding discussions at scale. To facilitate this study, we devise a categorization of coarse discourse acts designed to encompass general online discussion and allow for easy annotation by crowd workers. We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit. Using our corpus, we demonstrate how the analysis of discourse acts can characterize different types of discussions, including discourse sequences such as Q&A pairs and chains of disagreement, as well as different communities. Finally, we conduct experiments to predict discourse acts using our corpus, finding that structured prediction models such as conditional random fields can achieve an F1 score of 75%. We also demonstrate how the broadening of discourse acts from simply question and answer to a richer set of categories can improve the recall performance of Q&A extraction.

84 citations

Proceedings ArticleDOI
28 Feb 2015
TL;DR: This work proposes an investigation into how to use diversions containing small amounts of entertainment to improve crowd workers' experiences and finds that micro-diversions can significantly improve worker retention rate while retaining the same work quality.
Abstract: Crowdsourcing has become a popular and indispensable component of many problem-solving pipelines in the research literature, with crowd workers often treated as computational resources that can reliably solve problems that computers have trouble with, such as image labeling/classification, natural language processing, or document writing. Yet, obviously crowd workers are human, and long sequences of the same monotonous tasks might intuitively reduce the amount of good quality work done by the workers. Here we propose an investigation into how we can use diversions containing small amounts of entertainment to improve crowd workers' experiences. We call these small period of entertainment ``micro-diversions", which we hypothesize to provide timely relief to workers during long sequences of micro-tasks. We hope to improve productivity by retaining workers to work on our tasks longer and to either improve or retain the quality of work. We experimentally test micro-diversions on Amazon's Mechanical Turk, a large paid-crowdsourcing platform. We find that micro-diversions can significantly improve worker retention rate while retaining the same work quality.

82 citations


Cited by
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01 Jan 2009

7,241 citations

Proceedings Article
05 Dec 2013
TL;DR: TransE is proposed, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities, which proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases.
Abstract: We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.

5,109 citations

Proceedings ArticleDOI
07 Dec 2015
TL;DR: The task of free-form and open-ended Visual Question Answering (VQA) is proposed, given an image and a natural language question about the image, the task is to provide an accurate natural language answer.
Abstract: We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines for VQA are provided and compared with human performance.

3,513 citations

Proceedings ArticleDOI
02 Aug 2009
TL;DR: This work investigates an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corpora of any size.
Abstract: Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corpora of any size. Our experiments use Freebase, a large semantic database of several thousand relations, to provide distant supervision. For each pair of entities that appears in some Freebase relation, we find all sentences containing those entities in a large unlabeled corpus and extract textual features to train a relation classifier. Our algorithm combines the advantages of supervised IE (combining 400,000 noisy pattern features in a probabilistic classifier) and unsupervised IE (extracting large numbers of relations from large corpora of any domain). Our model is able to extract 10,000 instances of 102 relations at a precision of 67.6%. We also analyze feature performance, showing that syntactic parse features are particularly helpful for relations that are ambiguous or lexically distant in their expression.

2,965 citations

Proceedings Article
27 Jul 2014
TL;DR: This paper proposes TransH which models a relation as a hyperplane together with a translation operation on it and can well preserve the above mapping properties of relations with almost the same model complexity of TransE.
Abstract: We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance. We discuss some mapping properties of relations which should be considered in embedding, such as reflexive, one-to-many, many-to-one, and many-to-many. We note that TransE does not do well in dealing with these properties. Some complex models are capable of preserving these mapping properties but sacrifice efficiency in the process. To make a good trade-off between model capacity and efficiency, in this paper we propose TransH which models a relation as a hyperplane together with a translation operation on it. In this way, we can well preserve the above mapping properties of relations with almost the same model complexity of TransE. Additionally, as a practical knowledge graph is often far from completed, how to construct negative examples to reduce false negative labels in training is very important. Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling. We conduct extensive experiments on link prediction, triplet classification and fact extraction on benchmark datasets like WordNet and Freebase. Experiments show TransH delivers significant improvements over TransE on predictive accuracy with comparable capability to scale up.

2,835 citations