Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

"Topic": models, code, and papers

Inverse Reinforcement Learning for Marketing

Dec 13, 2017
Igor Halperin

Learning customer preferences from an observed behaviour is an important topic in the marketing literature. Structural models typically model forward-looking customers or firms as utility-maximizing agents whose utility is estimated using methods of Stochastic Optimal Control. We suggest an alternative approach to study dynamic consumer demand, based on Inverse Reinforcement Learning (IRL). We develop a version of the Maximum Entropy IRL that leads to a highly tractable model formulation that amounts to low-dimensional convex optimization in the search for optimal model parameters. Using simulations of consumer demand, we show that observational noise for identical customers can be easily confused with an apparent consumer heterogeneity.

* 18 pages, 5 figures 

  Access Paper or Ask Questions

SelQA: A New Benchmark for Selection-based Question Answering

Oct 28, 2016
Tomasz Jurczyk, Michael Zhai, Jinho D. Choi

This paper presents a new selection-based question answering dataset, SelQA. The dataset consists of questions generated through crowdsourcing and sentence length answers that are drawn from the ten most prevalent topics in the English Wikipedia. We introduce a corpus annotation scheme that enhances the generation of large, diverse, and challenging datasets by explicitly aiming to reduce word co-occurrences between the question and answers. Our annotation scheme is composed of a series of crowdsourcing tasks with a view to more effectively utilize crowdsourcing in the creation of question answering datasets in various domains. Several systems are compared on the tasks of answer sentence selection and answer triggering, providing strong baseline results for future work to improve upon.

  Access Paper or Ask Questions

Distilling Word Embeddings: An Encoding Approach

Jul 24, 2016
Lili Mou, Ran Jia, Yan Xu, Ge Li, Lu Zhang, Zhi Jin

Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems. This paper addresses the problem of distilling word embeddings for NLP tasks. We propose an encoding approach to distill task-specific knowledge from a set of high-dimensional embeddings, which can reduce model complexity by a large margin as well as retain high accuracy, showing a good compromise between efficiency and performance. Experiments in two tasks reveal the phenomenon that distilling knowledge from cumbersome embeddings is better than directly training neural networks with small embeddings.

* Accepted by CIKM-16 as a short paper, and by the Representation Learning for Natural Language Processing (RL4NLP) Workshop @ACL-16 for presentation 

  Access Paper or Ask Questions

Query-Focused Opinion Summarization for User-Generated Content

Jun 17, 2016
Lu Wang, Hema Raghavan, Claire Cardie, Vittorio Castelli

We present a submodular function-based framework for query-focused opinion summarization. Within our framework, relevance ordering produced by a statistical ranker, and information coverage with respect to topic distribution and diverse viewpoints are both encoded as submodular functions. Dispersion functions are utilized to minimize the redundancy. We are the first to evaluate different metrics of text similarity for submodularity-based summarization methods. By experimenting on community QA and blog summarization, we show that our system outperforms state-of-the-art approaches in both automatic evaluation and human evaluation. A human evaluation task is conducted on Amazon Mechanical Turk with scale, and shows that our systems are able to generate summaries of high overall quality and information diversity.

* COLING 2014 

  Access Paper or Ask Questions

Multi-Task Policy Search

Feb 12, 2014
Marc Peter Deisenroth, Peter Englert, Jan Peters, Dieter Fox

Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled approaches to share and transfer knowledge among similar tasks. We present a novel approach for learning a nonlinear feedback policy that generalizes across multiple tasks. The key idea is to define a parametrized policy as a function of both the state and the task, which allows learning a single policy that generalizes across multiple known and unknown tasks. Applications of our novel approach to reinforcement and imitation learning in real-robot experiments are shown.

* 8 pages, double column. IEEE International Conference on Robotics and Automation, 2014 

  Access Paper or Ask Questions

When majority voting fails: Comparing quality assurance methods for noisy human computation environment

Apr 16, 2012
Yu-An Sun, Christopher Dance

Quality assurance remains a key topic in human computation research. Prior work indicates that majority voting is effective for low difficulty tasks, but has limitations for harder tasks. This paper explores two methods of addressing this problem: tournament selection and elimination selection, which exploit 2-, 3- and 4-way comparisons between different answers to human computation tasks. Our experimental results and statistical analyses show that both methods produce the correct answer in noisy human computation environment more often than majority voting. Furthermore, we find that the use of 4-way comparisons can significantly reduce the cost of quality assurance relative to the use of 2-way comparisons.

* Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991) 

  Access Paper or Ask Questions

Semantic-ontological combination of Business Rules and Business Processes in IT Service Management

Jul 11, 2011
Alexander Sellner, Christopher Schwarz, Erwin Zinser

IT Service Management deals with managing a broad range of items related to complex system environments. As there is both, a close connection to business interests and IT infrastructure, the application of semantic expressions which are seamlessly integrated within applications for managing ITSM environments, can help to improve transparency and profitability. This paper focuses on the challenges regarding the integration of semantics and ontologies within ITSM environments. It will describe the paradigm of relationships and inheritance within complex service trees and will present an approach of ontologically expressing them. Furthermore, the application of SBVR-based rules as executable SQL triggers will be discussed. Finally, the broad range of topics for further research, derived from the findings, will be presented.

* Proceedings of the Doctoral Consortium and Poster Session of the 5th International Symposium on Rules (RuleML [email protected]), pages 33-40 (arXiv:1107.1686) 

  Access Paper or Ask Questions

Inferring Strategies for Sentence Ordering in Multidocument News Summarization

Jun 09, 2011
R. Barzilay, N. Elhadad

The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies.

* Journal Of Artificial Intelligence Research, Volume 17, pages 35-55, 2002 

  Access Paper or Ask Questions

Unsupervised Ranking and Aggregation of Label Descriptions for Zero-Shot Classifiers

Apr 20, 2022
Angelo Basile, Marc Franco-Salvador, Paolo Rosso

Zero-shot text classifiers based on label descriptions embed an input text and a set of labels into the same space: measures such as cosine similarity can then be used to select the most similar label description to the input text as the predicted label. In a true zero-shot setup, designing good label descriptions is challenging because no development set is available. Inspired by the literature on Learning with Disagreements, we look at how probabilistic models of repeated rating analysis can be used for selecting the best label descriptions in an unsupervised fashion. We evaluate our method on a set of diverse datasets and tasks (sentiment, topic and stance). Furthermore, we show that multiple, noisy label descriptions can be aggregated to boost the performance.

* 6 pages, 2 figures 

  Access Paper or Ask Questions

Random Information Sharing over Social Networks

Mar 04, 2022
Mert Kayaalp, Virginia Bordignon, Ali H. Sayed

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning, agents exchange full information with each other while trying to infer the true state of nature. We study the case where agents share information about only one hypothesis, i.e., the trending topic, which can be randomly changing at every iteration. We show that agents can learn the true hypothesis even if they do not discuss it, at rates comparable to traditional social learning. We also show that using one's own belief as a prior for estimating the neighbors' non-transmitted components might create opinion clusters that prevent learning with full confidence. This practice however avoids the complete rejection of the truth.

  Access Paper or Ask Questions