Paper Group NANR 203
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging. Ranking Distributions based on Noisy Sorting. Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction. An Italian Twitter Corpus of Hate Speech against Immigrants. From dictations to clinical reports using machine t …
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging
| Title | Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging |
| Authors | Nayeon Lee, Chien-Sheng Wu, Pascale Fung |
| Abstract | Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases. In this paper, we extend an existing pipeline approach to better tackle this problem. We propose a neural ranker using a decomposable attention model that dynamically selects sentences to achieve promising improvement in evidence retrieval F1 by 38.80{%}, with (x65) speedup compared to a TF-IDF method. Moreover, we incorporate lexical tagging methods into our pipeline framework to simplify the tasks and render the model more generalizable. As a result, our framework achieves promising performance on a large-scale fact extraction and verification dataset with speedup. |
| Tasks | Question Answering, Stance Detection |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1143/ |
| https://www.aclweb.org/anthology/D18-1143 | |
| PWC | https://paperswithcode.com/paper/improving-large-scale-fact-checking-using |
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Ranking Distributions based on Noisy Sorting
| Title | Ranking Distributions based on Noisy Sorting |
| Authors | Adil El Mesaoudi-Paul, Eyke Hüllermeier, Robert Busa-Fekete |
| Abstract | We propose a new statistical model for ranking data, i.e., a new family of probability distributions on permutations. Our model is inspired by the idea of a data-generating process in the form of a noisy sorting procedure, in which deterministic comparisons between pairs of items are replaced by Bernoulli trials. The probability of producing a certain ranking as a result then essentially depends on the Bernoulli parameters, which can be interpreted as pairwise preferences. We show that our model can be written in closed form if insertion or quick sort are used as sorting algorithms, and propose a maximum likelihood approach for parameter estimation. We also introduce a generalization of the model, in which the constraints on pairwise preferences are relaxed, and for which maximum likelihood estimation can be carried out based on a variation of the generalized iterative scaling algorithm. Experimentally, we show that the models perform very well in terms of goodness of fit, compared to existing models for ranking data. |
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| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=2051 |
| http://proceedings.mlr.press/v80/mesaoudi-paul18a/mesaoudi-paul18a.pdf | |
| PWC | https://paperswithcode.com/paper/ranking-distributions-based-on-noisy-sorting |
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Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction
| Title | Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction |
| Authors | Claudia Blaiotta, Patrick Freund, M. Jorge Cardoso, John Ashburner |
| Abstract | In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated manner. While in principle the generality of the proposed Bayesian approach makes it suitable to address a wide range of medical image computing problems, our work focuses primarily on neuroimaging applications. In particular we validate the proposed method on both real and synthetic brain MR scans including the cervical cord and demonstrate that it yields accurate alignment of brain and spinal cord structures, as compared to state-of-the-art tools for medical image registration. At the same time we illustrate how the resulting tissue probability maps can readily be used to segment, bias correct and spatially normalise unseen data, which are all crucial pre-processing steps for MR imaging studies. |
| Tasks | Diffeomorphic Medical Image Registration, Image Registration, Medical Image Registration |
| Published | 2018-02-01 |
| URL | https://doi.org/10.1016/j.neuroimage.2017.10.060 |
| http://discovery.ucl.ac.uk/10036377/1/Ashburner_1-s2.0-S1053811917308947-main.pdf | |
| PWC | https://paperswithcode.com/paper/generative-diffeomorphic-modelling-of-large |
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An Italian Twitter Corpus of Hate Speech against Immigrants
| Title | An Italian Twitter Corpus of Hate Speech against Immigrants |
| Authors | Manuela Sanguinetti, Fabio Poletto, Cristina Bosco, Viviana Patti, Marco Stranisci |
| Abstract | |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1443/ |
| https://www.aclweb.org/anthology/L18-1443 | |
| PWC | https://paperswithcode.com/paper/an-italian-twitter-corpus-of-hate-speech |
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From dictations to clinical reports using machine translation
| Title | From dictations to clinical reports using machine translation |
| Authors | Gregory Finley, Wael Salloum, Najmeh Sadoughi, Erik Edwards, Am Robinson, a, Nico Axtmann, Michael Brenndoerfer, Mark Miller, David Suendermann-Oeft |
| Abstract | A typical workflow to document clinical encounters entails dictating a summary, running speech recognition, and post-processing the resulting text into a formatted letter. Post-processing entails a host of transformations including punctuation restoration, truecasing, marking sections and headers, converting dates and numerical expressions, parsing lists, etc. In conventional implementations, most of these tasks are accomplished by individual modules. We introduce a novel holistic approach to post-processing that relies on machine callytranslation. We show how this technique outperforms an alternative conventional system{—}even learning to correct speech recognition errors during post-processing{—}while being much simpler to maintain. |
| Tasks | Machine Translation, Speech Recognition |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-3015/ |
| https://www.aclweb.org/anthology/N18-3015 | |
| PWC | https://paperswithcode.com/paper/from-dictations-to-clinical-reports-using |
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GroupCap: Group-Based Image Captioning With Structured Relevance and Diversity Constraints
| Title | GroupCap: Group-Based Image Captioning With Structured Relevance and Diversity Constraints |
| Authors | Fuhai Chen, Rongrong Ji, Xiaoshuai Sun, Yongjian Wu, Jinsong Su |
| Abstract | Most image captioning models focus on one-line (single image) captioning, where the correlations like relevance and diversity among group images (e.g., within the same album or event) are simply neglected, resulting in less accurate and diverse captions. Recent works mainly consider imposing the diversity during the online inference only, which neglect the correlation among visual structures in offline training. In this paper, we propose a novel group-based image captioning scheme (termed GroupCap), which jointly models the structured relevance and diversity among visual contents of group images towards an optimal collaborative captioning. In particular, we first propose a visual tree parser (VP-Tree) to construct the structured semantic correlations within individual images. Then, the relevance and diversity among images are well modeled by exploiting the correlations among their tree structures. Finally, such correlations are modeled as constraints and sent into the LSTM-based captioning generator. In offline optimization, we adopt an end-to-end formulation, which jointly trains the visual tree parser, the structured relevance and diversity constraints, as well as the LSTM based captioning model. To facilitate quantitative evaluation, we further release two group captioning datasets derived from the MS-COCO benchmark, serving as the first of their kind. Quantitative results show that the proposed GroupCap model outperforms the state-of-the-art and alternative approaches, which can generate much more accurate and discriminative captions under various evaluation metrics. |
| Tasks | Image Captioning |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_GroupCap_Group-Based_Image_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_GroupCap_Group-Based_Image_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/groupcap-group-based-image-captioning-with |
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Utilizing Large Twitter Corpora to Create Sentiment Lexica
| Title | Utilizing Large Twitter Corpora to Create Sentiment Lexica |
| Authors | Valerij Fredriksen, Brage Jahren, Bj{"o}rn Gamb{"a}ck |
| Abstract | |
| Tasks | Sentiment Analysis |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1447/ |
| https://www.aclweb.org/anthology/L18-1447 | |
| PWC | https://paperswithcode.com/paper/utilizing-large-twitter-corpora-to-create |
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Dialog Intent Structure: A Hierarchical Schema of Linked Dialog Acts
| Title | Dialog Intent Structure: A Hierarchical Schema of Linked Dialog Acts |
| Authors | Silvia Pareti, L, Tatiana o |
| Abstract | |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1460/ |
| https://www.aclweb.org/anthology/L18-1460 | |
| PWC | https://paperswithcode.com/paper/dialog-intent-structure-a-hierarchical-schema |
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Boundary Seeking GANs
| Title | Boundary Seeking GANs |
| Authors | R Devon Hjelm, Athul Paul Jacob, Adam Trischler, Gerry Che, Kyunghyun Cho, Yoshua Bengio |
| Abstract | Generative adversarial networks are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions. GANs, as normally formulated, rely on the generated samples being completely differentiable w.r.t. the generative parameters, and thus do not work for discrete data. We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator. The importance weights have a strong connection to the decision boundary of the discriminator, and we call our method boundary-seeking GANs (BGANs). We demonstrate the effectiveness of the proposed algorithm with discrete image and character-based natural language generation. In addition, the boundary-seeking objective extends to continuous data, which can be used to improve stability of training, and we demonstrate this on Celeba, Large-scale Scene Understanding (LSUN) bedrooms, and Imagenet without conditioning. |
| Tasks | Scene Understanding, Text Generation |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=rkTS8lZAb |
| https://openreview.net/pdf?id=rkTS8lZAb | |
| PWC | https://paperswithcode.com/paper/boundary-seeking-gans |
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Book Review: Bayesian Analysis in Natural Language Processing by Shay Cohen
| Title | Book Review: Bayesian Analysis in Natural Language Processing by Shay Cohen |
| Authors | Kevin Duh |
| Abstract | |
| Tasks | Coreference Resolution, Machine Translation |
| Published | 2018-03-01 |
| URL | https://www.aclweb.org/anthology/J18-1006/ |
| https://www.aclweb.org/anthology/J18-1006 | |
| PWC | https://paperswithcode.com/paper/book-review-bayesian-analysis-in-natural |
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PoSTWITA-UD: an Italian Twitter Treebank in Universal Dependencies
| Title | PoSTWITA-UD: an Italian Twitter Treebank in Universal Dependencies |
| Authors | Manuela Sanguinetti, Cristina Bosco, Alberto Lavelli, Aless Mazzei, ro, Oronzo Antonelli, Fabio Tamburini |
| Abstract | |
| Tasks | Opinion Mining, Sentiment Analysis |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1279/ |
| https://www.aclweb.org/anthology/L18-1279 | |
| PWC | https://paperswithcode.com/paper/postwita-ud-an-italian-twitter-treebank-in |
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Modeling Collaborative Multimodal Behavior in Group Dialogues: The MULTISIMO Corpus
| Title | Modeling Collaborative Multimodal Behavior in Group Dialogues: The MULTISIMO Corpus |
| Authors | Maria Koutsombogera, Carl Vogel |
| Abstract | |
| Tasks | |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1466/ |
| https://www.aclweb.org/anthology/L18-1466 | |
| PWC | https://paperswithcode.com/paper/modeling-collaborative-multimodal-behavior-in |
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Scalable Construction and Reasoning of Massive Knowledge Bases
| Title | Scalable Construction and Reasoning of Massive Knowledge Bases |
| Authors | Xiang Ren, Nanyun Peng, William Yang Wang |
| Abstract | In today{'}s information-based society, there is abundant knowledge out there carried in the form of natural language texts (e.g., news articles, social media posts, scientific publications), which spans across various domains (e.g., corporate documents, advertisements, legal acts, medical reports), which grows at an astonishing rate. Yet this knowledge is mostly inaccessible to computers and overwhelming for human experts to absorb. How to turn such massive and unstructured text data into structured, actionable knowledge, and furthermore, how to teach machines learn to reason and complete the extracted knowledge is a grand challenge to the research community. Traditional IE systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. In the first part of the tutorial, we introduce how to extract structured facts (i.e., entities and their relations for types of interest) from text corpora to construct knowledge bases, with a focus on methods that are weakly-supervised and domain-independent for timely knowledge base construction across various application domains. In the second part, we introduce how to leverage other knowledge, such as the distributional statistics of characters and words, the annotations for other tasks and other domains, and the linguistics and problem structures, to combat the problem of inadequate supervision, and conduct low-resource information extraction. In the third part, we describe recent advances in knowledge base reasoning. We start with the gentle introduction to the literature, focusing on path-based and embedding based methods. We then describe DeepPath, a recent attempt of using deep reinforcement learning to combine the best of both worlds for knowledge base reasoning. |
| Tasks | |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-6003/ |
| https://www.aclweb.org/anthology/N18-6003 | |
| PWC | https://paperswithcode.com/paper/scalable-construction-and-reasoning-of |
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CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition
| Title | CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition |
| Authors | Jedrzej Kozerawski, Matthew Turk |
| Abstract | This work addresses the novel problem of one-shot one-class classification. The goal is to estimate a classification decision boundary for a novel class based on a single image example. Our method exploits transfer learning to model the transformation from a representation of the input, extracted by a Convolutional Neural Network, to a classification decision boundary. We use a deep neural network to learn this transformation from a large labelled dataset of images and their associated class decision boundaries generated from ImageNet, and then apply the learned decision boundary to classify subsequent query images. We tested our approach on several benchmark datasets and significantly outperformed the baseline methods. |
| Tasks | Transfer Learning |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Kozerawski_CLEAR_Cumulative_LEARning_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Kozerawski_CLEAR_Cumulative_LEARning_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/clear-cumulative-learning-for-one-shot-one |
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Leveraging Lexical Resources and Constraint Grammar for Rule-Based Part-of-Speech Tagging in Welsh
| Title | Leveraging Lexical Resources and Constraint Grammar for Rule-Based Part-of-Speech Tagging in Welsh |
| Authors | Steven Neale, Kevin Donnelly, Gareth Watkins, Dawn Knight |
| Abstract | |
| Tasks | Part-Of-Speech Tagging |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1623/ |
| https://www.aclweb.org/anthology/L18-1623 | |
| PWC | https://paperswithcode.com/paper/leveraging-lexical-resources-and-constraint |
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