October 16, 2019

1334 words 7 mins read

Paper Group NANR 32

Creating Lithuanian and Latvian Speech Corpora from Inaccurately Annotated Web Data. Automating Bayesian optimization with Bayesian optimization. High-Order Tensor Regularization With Application to Attribute Ranking. Integrating Question Classification and Deep Learning for improved Answer Selection. An Unsupervised System for Parallel Corpus Filt …

Creating Lithuanian and Latvian Speech Corpora from Inaccurately Annotated Web Data


Title	Creating Lithuanian and Latvian Speech Corpora from Inaccurately Annotated Web Data
Authors	Askars Salimbajevs
Abstract
Tasks	Language Modelling, Speech Recognition
Published	2018-05-01
URL	https://www.aclweb.org/anthology/L18-1454/
PDF	https://www.aclweb.org/anthology/L18-1454
PWC	https://paperswithcode.com/paper/creating-lithuanian-and-latvian-speech
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Automating Bayesian optimization with Bayesian optimization


Title	Automating Bayesian optimization with Bayesian optimization
Authors	Gustavo Malkomes, Roman Garnett
Abstract	Bayesian optimization is a powerful tool for global optimization of expensive functions. One of its key components is the underlying probabilistic model used for the objective function f. In practice, however, it is often unclear how one should appropriately choose a model, especially when gathering data is expensive. In this work, we introduce a novel automated Bayesian optimization approach that dynamically selects promising models for explaining the observed data using Bayesian Optimization in the model space. Crucially, we account for the uncertainty in the choice of model; our method is capable of using multiple models to represent its current belief about f and subsequently using this information for decision making. We argue, and demonstrate empirically, that our approach automatically finds suitable models for the objective function, which ultimately results in more-efficient optimization.
Tasks	Decision Making
Published	2018-12-01
URL	http://papers.nips.cc/paper/7838-automating-bayesian-optimization-with-bayesian-optimization
PDF	http://papers.nips.cc/paper/7838-automating-bayesian-optimization-with-bayesian-optimization.pdf
PWC	https://paperswithcode.com/paper/automating-bayesian-optimization-with
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High-Order Tensor Regularization With Application to Attribute Ranking


Title	High-Order Tensor Regularization With Application to Attribute Ranking
Authors	Kwang In Kim, Juhyun Park, James Tompkin
Abstract	When learning functions on manifolds, we can improve performance by regularizing with respect to the intrinsic manifold geometry rather than the ambient space. However, when regularizing tensor learning, calculating the derivatives along this intrinsic geometry is not possible, and so existing approaches are limited to regularizing in Euclidean space. Our new method for intrinsically regularizing and learning tensors on Riemannian manifolds introduces a surrogate object to encapsulate the geometric characteristic of the tensor. Regularizing this instead allows us to learn non-symmetric and high-order tensors. We apply our approach to the relative attributes problem, and we demonstrate that explicitly regularizing high-order relationships between pairs of data points improves performance.
Tasks
Published	2018-06-01
URL	http://openaccess.thecvf.com/content_cvpr_2018/html/Kim_High-Order_Tensor_Regularization_CVPR_2018_paper.html
PDF	http://openaccess.thecvf.com/content_cvpr_2018/papers/Kim_High-Order_Tensor_Regularization_CVPR_2018_paper.pdf
PWC	https://paperswithcode.com/paper/high-order-tensor-regularization-with
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Framework

Integrating Question Classification and Deep Learning for improved Answer Selection


Title	Integrating Question Classification and Deep Learning for improved Answer Selection
Authors	Harish Tayyar Madabushi, Mark Lee, John Barnden
Abstract	We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection. We detail the necessary changes to the Question Classification taxonomy and system, the creation of a new Entity Identification system and methods of highlighting entities to achieve this objective. Our experiments show that Question Classes are a strong signal to Deep Learning models for Answer Selection, and enable us to outperform the current state of the art in all variations of our experiments except one. In the best configuration, our MRR and MAP scores outperform the current state of the art by between 3 and 5 points on both versions of the TREC Answer Selection test set, a standard dataset for this task.
Tasks	Answer Selection, Question Answering
Published	2018-08-01
URL	https://www.aclweb.org/anthology/C18-1278/
PDF	https://www.aclweb.org/anthology/C18-1278
PWC	https://paperswithcode.com/paper/integrating-question-classification-and-deep
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Framework

An Unsupervised System for Parallel Corpus Filtering


Title	An Unsupervised System for Parallel Corpus Filtering
Authors	Viktor Hangya, Alex Fraser, er
Abstract	In this paper we describe LMU Munich{'}s submission for the \textit{WMT 2018 Parallel Corpus Filtering} shared task which addresses the problem of cleaning noisy parallel corpora. The task of mining and cleaning parallel sentences is important for improving the quality of machine translation systems, especially for low-resource languages. We tackle this problem in a fully unsupervised fashion relying on bilingual word embeddings created without any bilingual signal. After pre-filtering noisy data we rank sentence pairs by calculating bilingual sentence-level similarities and then remove redundant data by employing monolingual similarity as well. Our unsupervised system achieved good performance during the official evaluation of the shared task, scoring only a few BLEU points behind the best systems, while not requiring any parallel training data.
Tasks	Domain Adaptation, Language Modelling, Machine Translation, Sentence Embedding, Text Simplification, Word Embeddings
Published	2018-10-01
URL	https://www.aclweb.org/anthology/W18-6477/
PDF	https://www.aclweb.org/anthology/W18-6477
PWC	https://paperswithcode.com/paper/an-unsupervised-system-for-parallel-corpus
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Weather Forecast Voice System


Title	Weather Forecast Voice System
Authors	Dinh Thanh Do
Abstract
Tasks
Published	2018-10-01
URL	https://www.aclweb.org/anthology/O18-1013/
PDF	https://www.aclweb.org/anthology/O18-1013
PWC	https://paperswithcode.com/paper/weather-forecast-voice-system
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Annotated Corpus of Scientific Conference’s Homepages for Information Extraction


Title	Annotated Corpus of Scientific Conference’s Homepages for Information Extraction
Authors	Piotr Andruszkiewicz, Rafa{\l} Hazan
Abstract
Tasks
Published	2018-05-01
URL	https://www.aclweb.org/anthology/L18-1587/
PDF	https://www.aclweb.org/anthology/L18-1587
PWC	https://paperswithcode.com/paper/annotated-corpus-of-scientific-conferences
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Framework

Building a Constraint Grammar Parser for Plains Cree Verbs and Arguments


Title	Building a Constraint Grammar Parser for Plains Cree Verbs and Arguments
Authors	Katherine Schmirler, Antti Arppe, Trond Trosterud, Lene Antonsen
Abstract
Tasks
Published	2018-05-01
URL	https://www.aclweb.org/anthology/L18-1472/
PDF	https://www.aclweb.org/anthology/L18-1472
PWC	https://paperswithcode.com/paper/building-a-constraint-grammar-parser-for
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Framework

QUEST: A Natural Language Interface to Relational Databases


Title	QUEST: A Natural Language Interface to Relational Databases
Authors	Vadim Sheinin, Elahe Khorashani, Hangu Yeo, Kun Xu, Ngoc Phuoc An Vo, Octavian Popescu
Abstract
Tasks	Relation Extraction, Semantic Parsing
Published	2018-05-01
URL	https://www.aclweb.org/anthology/L18-1469/
PDF	https://www.aclweb.org/anthology/L18-1469
PWC	https://paperswithcode.com/paper/quest-a-natural-language-interface-to
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Framework

Reference production in human-computer interaction: Issues for Corpus-based Referring Expression Generation


Title	Reference production in human-computer interaction: Issues for Corpus-based Referring Expression Generation
Authors	Danillo Rocha, Iv Paraboni, r{'e}
Abstract
Tasks	Text Generation
Published	2018-05-01
URL	https://www.aclweb.org/anthology/L18-1474/
PDF	https://www.aclweb.org/anthology/L18-1474
PWC	https://paperswithcode.com/paper/reference-production-in-human-computer
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Framework

Definite Description Lexical Choice: taking Speaker’s Personality into account


Title	Definite Description Lexical Choice: taking Speaker’s Personality into account
Authors	Alex Lan, Iv Paraboni, r{'e}
Abstract
Tasks	Text Generation
Published	2018-05-01
URL	https://www.aclweb.org/anthology/L18-1475/
PDF	https://www.aclweb.org/anthology/L18-1475
PWC	https://paperswithcode.com/paper/definite-description-lexical-choice-taking
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Framework

Modeling Temporality of Human Intentions by Domain Adaptation


Title	Modeling Temporality of Human Intentions by Domain Adaptation
Authors	Xiaolei Huang, Lixing Liu, Kate Carey, Joshua Woolley, Stefan Scherer, Brian Borsari
Abstract	Categorizing patient{'}s intentions in conversational assessment can help decision making in clinical treatments. Many conversation corpora span broaden a series of time stages. However, it is not clear that how the themes shift in the conversation impact on the performance of human intention categorization (eg., patients might show different behaviors during the beginning versus the end). This paper proposes a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI). We deploy Bi-LSTM and topic model jointly to learn language usage change across different time sessions. We conduct experiments on the MI corpora to show the promising improvement after considering temporality in the classification task.
Tasks	Decision Making, Domain Adaptation, Intent Classification, Topic Models
Published	2018-10-01
URL	https://www.aclweb.org/anthology/D18-1074/
PDF	https://www.aclweb.org/anthology/D18-1074
PWC	https://paperswithcode.com/paper/modeling-temporality-of-human-intentions-by
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Framework

Referring Expression Generation in time-constrained communication


Title	Referring Expression Generation in time-constrained communication
Authors	Andr{'e} Mariotti, Iv Paraboni, r{'e}
Abstract
Tasks	Text Generation
Published	2018-05-01
URL	https://www.aclweb.org/anthology/L18-1476/
PDF	https://www.aclweb.org/anthology/L18-1476
PWC	https://paperswithcode.com/paper/referring-expression-generation-in-time
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Framework

Optimization for Approximate Submodularity


Title	Optimization for Approximate Submodularity
Authors	Yaron Singer, Avinatan Hassidim
Abstract	We consider the problem of maximizing a submodular function when given access to its approximate version. Submodular functions are heavily studied in a wide variety of disciplines, since they are used to model many real world phenomena, and are amenable to optimization. However, there are many cases in which the phenomena we observe is only approximately submodular and the approximation guarantees cease to hold. We describe a technique which we call the sampled mean approximation that yields strong guarantees for maximization of submodular functions from approximate surrogates under cardinality and intersection of matroid constraints. In particular, we show tight guarantees for maximization under a cardinality constraint and 1/(1+P) approximation under intersection of P matroids.
Tasks
Published	2018-12-01
URL	http://papers.nips.cc/paper/7322-optimization-for-approximate-submodularity
PDF	http://papers.nips.cc/paper/7322-optimization-for-approximate-submodularity.pdf
PWC	https://paperswithcode.com/paper/optimization-for-approximate-submodularity
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Framework

Universal Dependencies for Amharic


Title	Universal Dependencies for Amharic
Authors	Binyam Ephrem Seyoum, Yusuke Miyao, Baye Yimam Mekonnen
Abstract
Tasks	Machine Translation, Question Answering
Published	2018-05-01
URL	https://www.aclweb.org/anthology/L18-1350/
PDF	https://www.aclweb.org/anthology/L18-1350
PWC	https://paperswithcode.com/paper/universal-dependencies-for-amharic
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Framework