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['Ahmed Osman', 'Wojciech Samek']

1802.00209v1

We propose an architecture for VQA which utilizes recurrent layers to generate visual and textual attention. The memory characteristic of the proposed recurrent attention units offers a rich joint embedding of visual and textual features and enables the model to reason relations between several parts of the image and question. Our single model outperforms the first place winner on the VQA 1.0 dataset, performs within margin to the current state-of-the-art ensemble model. We also experiment with replacing attention mechanisms in other state-of-the-art models with our implementation and show increased accuracy. In both cases, our recurrent attention mechanism improves performance in tasks requiring sequential or relational reasoning on the VQA dataset.

Dual Recurrent Attention Units for Visual Question Answering

http://arxiv.org/pdf/1802.00209v1

Title Dual Recurrent Attention Units Visual Question Answering Summary propose architecture VQA utilizes recurrent layer generate visual textual attention memory characteristic proposed recurrent attention unit offer rich joint embedding visual textual feature enables model reason relation several part image question single model outperforms first place winner VQA 10 dataset performs within margin current stateoftheart ensemble model also experiment replacing attention mechanism stateoftheart model implementation show increased accuracy case recurrent attention mechanism improves performance task requiring sequential relational reasoning VQA dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ji Young Lee', 'Franck Dernoncourt']

1603.03827v1

Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction.

Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

http://arxiv.org/pdf/1603.03827v1

Title Sequential ShortText Classification Recurrent Convolutional Neural Networks Summary Recent approach based artificial neural network ANNs shown promising result shorttext classification However many short text occur sequence eg sentence document utterance dialog existing ANNbased system leverage preceding short text classifying subsequent one work present model based recurrent neural network convolutional neural network incorporates preceding short text model achieves stateoftheart result three different datasets dialog act prediction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian Vlad Serban', 'Tim Klinger', 'Gerald Tesauro', 'Kartik Talamadupula', 'Bowen Zhou', 'Yoshua Bengio', 'Aaron Courville']

1606.00776v2

We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens. There are many ways to estimate or learn the high-level coarse tokens, but we argue that a simple extraction procedure is sufficient to capture a wealth of high-level discourse semantics. Such procedure allows training the multiresolution recurrent neural network by maximizing the exact joint log-likelihood over both sequences. In contrast to the standard log- likelihood objective w.r.t. natural language tokens (word perplexity), optimizing the joint log-likelihood biases the model towards modeling high-level abstractions. We apply the proposed model to the task of dialogue response generation in two challenging domains: the Ubuntu technical support domain, and Twitter conversations. On Ubuntu, the model outperforms competing approaches by a substantial margin, achieving state-of-the-art results according to both automatic evaluation metrics and a human evaluation study. On Twitter, the model appears to generate more relevant and on-topic responses according to automatic evaluation metrics. Finally, our experiments demonstrate that the proposed model is more adept at overcoming the sparsity of natural language and is better able to capture long-term structure.

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation

http://arxiv.org/pdf/1606.00776v2

Title Multiresolution Recurrent Neural Networks Application Dialogue Response Generation Summary introduce multiresolution recurrent neural network extends sequencetosequence framework model natural language generation two parallel discrete stochastic process sequence highlevel coarse token sequence natural language token many way estimate learn highlevel coarse token argue simple extraction procedure sufficient capture wealth highlevel discourse semantics procedure allows training multiresolution recurrent neural network maximizing exact joint loglikelihood sequence contrast standard log likelihood objective wrt natural language token word perplexity optimizing joint loglikelihood bias model towards modeling highlevel abstraction apply proposed model task dialogue response generation two challenging domain Ubuntu technical support domain Twitter conversation Ubuntu model outperforms competing approach substantial margin achieving stateoftheart result according automatic evaluation metric human evaluation study Twitter model appears generate relevant ontopic response according automatic evaluation metric Finally experiment demonstrate proposed model adept overcoming sparsity natural language better able capture longterm structure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sebastian Ruder', 'Joachim Bingel', 'Isabelle Augenstein', 'Anders Søgaard']

1705.08142v2

Multi-task learning is motivated by the observation that humans bring to bear what they know about related problems when solving new ones. Similarly, deep neural networks can profit from related tasks by sharing parameters with other networks. However, humans do not consciously decide to transfer knowledge between tasks. In Natural Language Processing (NLP), it is hard to predict if sharing will lead to improvements, particularly if tasks are only loosely related. To overcome this, we introduce Sluice Networks, a general framework for multi-task learning where trainable parameters control the amount of sharing. Our framework generalizes previous proposals in enabling sharing of all combinations of subspaces, layers, and skip connections. We perform experiments on three task pairs, and across seven different domains, using data from OntoNotes 5.0, and achieve up to 15% average error reductions over common approaches to multi-task learning. We show that a) label entropy is predictive of gains in sluice networks, confirming findings for hard parameter sharing and b) while sluice networks easily fit noise, they are robust across domains in practice.

Learning what to share between loosely related tasks

http://arxiv.org/pdf/1705.08142v2

Title Learning share loosely related task Summary Multitask learning motivated observation human bring bear know related problem solving new one Similarly deep neural network profit related task sharing parameter network However human consciously decide transfer knowledge task Natural Language Processing NLP hard predict sharing lead improvement particularly task loosely related overcome introduce Sluice Networks general framework multitask learning trainable parameter control amount sharing framework generalizes previous proposal enabling sharing combination subspace layer skip connection perform experiment three task pair across seven different domain using data OntoNotes 50 achieve 15 average error reduction common approach multitask learning show label entropy predictive gain sluice network confirming finding hard parameter sharing b sluice network easily fit noise robust across domain practice Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian V. Serban', 'Chinnadhurai Sankar', 'Mathieu Germain', 'Saizheng Zhang', 'Zhouhan Lin', 'Sandeep Subramanian', 'Taesup Kim', 'Michael Pieper', 'Sarath Chandar', 'Nan Rosemary Ke', 'Sai Rajeshwar', 'Alexandre de Brebisson', 'Jose M. R. Sotelo', 'Dendi Suhubdy', 'Vincent Michalski', 'Alexandre Nguyen', 'Joelle Pineau', 'Yoshua Bengio']

1709.02349v2

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including template-based models, bag-of-words models, sequence-to-sequence neural network and latent variable neural network models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than many competing systems. Due to its machine learning architecture, the system is likely to improve with additional data.

A Deep Reinforcement Learning Chatbot

http://arxiv.org/pdf/1709.02349v2

Title Deep Reinforcement Learning Chatbot Summary present MILABOT deep reinforcement learning chatbot developed Montreal Institute Learning Algorithms MILA Amazon Alexa Prize competition MILABOT capable conversing human popular small talk topic speech text system consists ensemble natural language generation retrieval model including templatebased model bagofwords model sequencetosequence neural network latent variable neural network model applying reinforcement learning crowdsourced data realworld user interaction system trained select appropriate response model ensemble system evaluated AB testing realworld user performed significantly better many competing system Due machine learning architecture system likely improve additional data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kelvin Guu', 'Tatsunori B. Hashimoto', 'Yonatan Oren', 'Percy Liang']

1709.08878v1

We propose a new generative model of sentences that first samples a prototype sentence from the training corpus and then edits it into a new sentence. Compared to traditional models that generate from scratch either left-to-right or by first sampling a latent sentence vector, our prototype-then-edit model improves perplexity on language modeling and generates higher quality outputs according to human evaluation. Furthermore, the model gives rise to a latent edit vector that captures interpretable semantics such as sentence similarity and sentence-level analogies.

Generating Sentences by Editing Prototypes

http://arxiv.org/pdf/1709.08878v1

Title Generating Sentences Editing Prototypes Summary propose new generative model sentence first sample prototype sentence training corpus edits new sentence Compared traditional model generate scratch either lefttoright first sampling latent sentence vector prototypethenedit model improves perplexity language modeling generates higher quality output according human evaluation Furthermore model give rise latent edit vector capture interpretable semantics sentence similarity sentencelevel analogy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iulian V. Serban', 'Chinnadhurai Sankar', 'Mathieu Germain', 'Saizheng Zhang', 'Zhouhan Lin', 'Sandeep Subramanian', 'Taesup Kim', 'Michael Pieper', 'Sarath Chandar', 'Nan Rosemary Ke', 'Sai Rajeswar', 'Alexandre de Brebisson', 'Jose M. R. Sotelo', 'Dendi Suhubdy', 'Vincent Michalski', 'Alexandre Nguyen', 'Joelle Pineau', 'Yoshua Bengio']

1801.06700v1

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including neural network and template-based models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than other systems. The results highlight the potential of coupling ensemble systems with deep reinforcement learning as a fruitful path for developing real-world, open-domain conversational agents.

A Deep Reinforcement Learning Chatbot (Short Version)

http://arxiv.org/pdf/1801.06700v1

Title Deep Reinforcement Learning Chatbot Short Version Summary present MILABOT deep reinforcement learning chatbot developed Montreal Institute Learning Algorithms MILA Amazon Alexa Prize competition MILABOT capable conversing human popular small talk topic speech text system consists ensemble natural language generation retrieval model including neural network templatebased model applying reinforcement learning crowdsourced data realworld user interaction system trained select appropriate response model ensemble system evaluated AB testing realworld user performed significantly better system result highlight potential coupling ensemble system deep reinforcement learning fruitful path developing realworld opendomain conversational agent Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Darko Brodic', 'Alessia Amelio', 'Zoran N. Milivojevic', 'Milena Jevtic']

1609.06492v1

The paper introduces a new method for discrimination of documents given in different scripts. The document is mapped into a uniformly coded text of numerical values. It is derived from the position of the letters in the text line, based on their typographical characteristics. Each code is considered as a gray level. Accordingly, the coded text determines a 1-D image, on which texture analysis by run-length statistics and local binary pattern is performed. It defines feature vectors representing the script content of the document. A modified clustering approach employed on document feature vector groups documents written in the same script. Experimentation performed on two custom oriented databases of historical documents in old Cyrillic, angular and round Glagolitic as well as Antiqua and Fraktur scripts demonstrates the superiority of the proposed method with respect to well-known methods in the state-of-the-art.

Document Image Coding and Clustering for Script Discrimination

http://arxiv.org/pdf/1609.06492v1

Title Document Image Coding Clustering Script Discrimination Summary paper introduces new method discrimination document given different script document mapped uniformly coded text numerical value derived position letter text line based typographical characteristic code considered gray level Accordingly coded text determines 1D image texture analysis runlength statistic local binary pattern performed defines feature vector representing script content document modified clustering approach employed document feature vector group document written script Experimentation performed two custom oriented database historical document old Cyrillic angular round Glagolitic well Antiqua Fraktur script demonstrates superiority proposed method respect wellknown method stateoftheart Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mateusz Malinowski', 'Mario Fritz']

1610.01076v1

Together with the development of more accurate methods in Computer Vision and Natural Language Understanding, holistic architectures that answer on questions about the content of real-world images have emerged. In this tutorial, we build a neural-based approach to answer questions about images. We base our tutorial on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the models that we present here can achieve a competitive performance on both datasets, in fact, they are among the best methods that use a combination of LSTM with a global, full frame CNN representation of an image. We hope that after reading this tutorial, the reader will be able to use Deep Learning frameworks, such as Keras and introduced Kraino, to build various architectures that will lead to a further performance improvement on this challenging task.

Tutorial on Answering Questions about Images with Deep Learning

http://arxiv.org/pdf/1610.01076v1

Title Tutorial Answering Questions Images Deep Learning Summary Together development accurate method Computer Vision Natural Language Understanding holistic architecture answer question content realworld image emerged tutorial build neuralbased approach answer question image base tutorial two datasets mostly DAQUAR bit VQA small tweak model present achieve competitive performance datasets fact among best method use combination LSTM global full frame CNN representation image hope reading tutorial reader able use Deep Learning framework Keras introduced Kraino build various architecture lead performance improvement challenging task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tony Beltramelli']

1705.07962v2

Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies).

pix2code: Generating Code from a Graphical User Interface Screenshot

http://arxiv.org/pdf/1705.07962v2

Title pix2code Generating Code Graphical User Interface Screenshot Summary Transforming graphical user interface screenshot created designer computer code typical task conducted developer order build customized software website mobile application paper show deep learning method leveraged train model endtoend automatically generate code single input image 77 accuracy three different platform ie iOS Android webbased technology Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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