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"Recommendation": models, code, and papers

A system for information extraction from scientific texts in Russian

Sep 14, 2021
Elena Bruches, Anastasia Mezentseva, Tatiana Batura

In this paper, we present a system for information extraction from scientific texts in the Russian language. The system performs several tasks in an end-to-end manner: term recognition, extraction of relations between terms, and term linking with entities from the knowledge base. These tasks are extremely important for information retrieval, recommendation systems, and classification. The advantage of the implemented methods is that the system does not require a large amount of labeled data, which saves time and effort for data labeling and therefore can be applied in low- and mid-resource settings. The source code is publicly available and can be used for different research purposes.


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Do Transformer Modifications Transfer Across Implementations and Applications?

Feb 23, 2021
Sharan Narang, Hyung Won Chung, Yi Tay, William Fedus, Thibault Fevry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel

The research community has proposed copious modifications to the Transformer architecture since it was introduced over three years ago, relatively few of which have seen widespread adoption. In this paper, we comprehensively evaluate many of these modifications in a shared experimental setting that covers most of the common uses of the Transformer in natural language processing. Surprisingly, we find that most modifications do not meaningfully improve performance. Furthermore, most of the Transformer variants we found beneficial were either developed in the same codebase that we used or are relatively minor changes. We conjecture that performance improvements may strongly depend on implementation details and correspondingly make some recommendations for improving the generality of experimental results.


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MICROS: Mixed-Initiative ConveRsatiOnal Systems Workshop

Jan 25, 2021
Ida Mele, Cristina Ioana Muntean, Mohammad Aliannejadi, Nikos Voskarides

The 1st edition of the workshop on Mixed-Initiative ConveRsatiOnal Systems ([email protected]) aims at investigating and collecting novel ideas and contributions in the field of conversational systems. Oftentimes, the users fulfill their information need using smartphones and home assistants. This has revolutionized the way users access online information, thus posing new challenges compared to traditional search and recommendation. The first edition of MICROS will have a particular focus on mixed-initiative conversational systems. Indeed, conversational systems need to be proactive, proposing not only answers but also possible interpretations for ambiguous or vague requests.

* ECIR 2021 workshop 

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Artificial Intelligence in Clinical Health Care Applications: Viewpoint

Jun 26, 2019
Michael van Hartskamp, Sergio Consoli, Wim Verhaegh, Milan Petković, Anja van de Stolpe

The idea of Artificial Intelligence (AI) has a long history. It turned out, however, that reaching intelligence at human levels is more complicated than originally anticipated. Currently we are experiencing a renewed interest in AI, fueled by an enormous increase in computing power and an even larger increase in data, in combination with improved AI technologies like deep learning. Healthcare is considered the next domain to be revolutionized by Artificial Intelligence. While AI approaches are excellently suited to develop certain algorithms, for biomedical applications there are specific challenges. We propose recommendations to improve AI projects in the biomedical space and especially clinical healthcare.

* Journal of Medical Internet Research (2019), 21(4):e12100 

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On Evaluation of Embodied Navigation Agents

Jul 18, 2018
Peter Anderson, Angel Chang, Devendra Singh Chaplot, Alexey Dosovitskiy, Saurabh Gupta, Vladlen Koltun, Jana Kosecka, Jitendra Malik, Roozbeh Mottaghi, Manolis Savva, Amir R. Zamir

Skillful mobile operation in three-dimensional environments is a primary topic of study in Artificial Intelligence. The past two years have seen a surge of creative work on navigation. This creative output has produced a plethora of sometimes incompatible task definitions and evaluation protocols. To coordinate ongoing and future research in this area, we have convened a working group to study empirical methodology in navigation research. The present document summarizes the consensus recommendations of this working group. We discuss different problem statements and the role of generalization, present evaluation measures, and provide standard scenarios that can be used for benchmarking.

* Report of a working group on empirical methodology in navigation research. Authors are listed in alphabetical order 

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Regularization for Deep Learning: A Taxonomy

Oct 29, 2017
Jan Kukačka, Vladimir Golkov, Daniel Cremers

Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.


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Sequential ranking under random semi-bandit feedback

May 26, 2016
Hossein Vahabi, Paul Lagrée, Claire Vernade, Olivier Cappé

In many web applications, a recommendation is not a single item suggested to a user but a list of possibly interesting contents that may be ranked in some contexts. The combinatorial bandit problem has been studied quite extensively these last two years and many theoretical results now exist : lower bounds on the regret or asymptotically optimal algorithms. However, because of the variety of situations that can be considered, results are designed to solve the problem for a specific reward structure such as the Cascade Model. The present work focuses on the problem of ranking items when the user is allowed to click on several items while scanning the list from top to bottom.

* This submission has been withdrawn by arXiv administrators due to irreconcilable authorship dispute 

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Budget-Constrained Reinforcement of Ranked Objects

Mar 27, 2022
Amir Ban, Moshe Tennenholtz

Commercial entries, such as hotels, are ranked according to score by a search engine or recommendation system, and the score of each can be improved upon by making a targeted investment, e.g., advertising. We study the problem of how a principal, who owns or supports a set of entries, can optimally allocate a budget to maximize their ranking. Representing the set of ranked scores as a probability distribution over scores, we treat this question as a game between distributions. We show that, in the general case, the best ranking is achieved by equalizing the scores of several disjoint score ranges. We show that there is a unique optimal reinforcement strategy, and provide an efficient algorithm implementing it.


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Compliance checking in reified IO logic via SHACL

Oct 13, 2021
Livio Robaldo, Kolawole J. Adebayo

Reified Input/Output (I/O) logic[21] has been recently proposed to model real-world norms in terms of the logic in [11]. This is massively grounded on the notion of reification, and it has specifically designed to model meaning of natural language sentences, such as the ones occurring in existing legislation. This paper presents a methodology to carry out compliance checking on reified I/O logic formulae. These are translated in SHACL (Shapes Constraint Language) shapes, a recent W3C recommendation to validate and reason with RDF triplestores. Compliance checking is then enforced by validating RDF graphs describing states of affairs with respect to these SHACL shapes.


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