Paper Group NANR 54
Second-Order Non-Local Attention Networks for Person Re-Identification. Robust and fast heart rate variability analysis of long and noisy electrocardiograms using neural networks and images. IT–IST at the SIGMORPHON 2019 Shared Task: Sparse Two-headed Models for Inflection. What Does This Word Mean? Explaining Contextualized Embeddings with Natura …
Second-Order Non-Local Attention Networks for Person Re-Identification
| Title | Second-Order Non-Local Attention Networks for Person Re-Identification |
| Authors | Bryan (Ning) Xia, Yuan Gong, Yizhe Zhang, Christian Poellabauer |
| Abstract | Recent efforts have shown promising results for person re-identification by designing part-based architectures to allow a neural network to learn discriminative representations from semantically coherent parts. Some efforts use soft attention to reallocate distant outliers to their most similar parts, while others adjust part granularity to incorporate more distant positions for learning the relationships. Others seek to generalize part-based methods by introducing a dropout mechanism on consecutive regions of the feature map to enhance distant region relationships. However, only few prior efforts model the distant or non-local positions of the feature map directly for the person re-ID task. In this paper, we propose a novel attention mechanism to directly model long-range relationships via second-order feature statistics. When combined with a generalized DropBlock module, our method performs equally to or better than state-of-the-art results for mainstream person re-identification datasets, including Market1501, CUHK03, and DukeMTMC-reID. |
| Tasks | Person Re-Identification |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Xia_Second-Order_Non-Local_Attention_Networks_for_Person_Re-Identification_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Xia_Second-Order_Non-Local_Attention_Networks_for_Person_Re-Identification_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/second-order-non-local-attention-networks-for-1 |
| Repo | |
| Framework | |
Robust and fast heart rate variability analysis of long and noisy electrocardiograms using neural networks and images
| Title | Robust and fast heart rate variability analysis of long and noisy electrocardiograms using neural networks and images |
| Authors | Sean Parsons, Jan Huizinga |
| Abstract | Heart rate variability studies depend on the robust calculation of the tachogram, the heart rate times series, usually by the detection of R peaks in the electrocardiogram (ECG). ECGs however are subject to a number of sources of noise which are difficult to filter and therefore reduce the tachogram accuracy. We describe a pipeline for fast calculation of tachograms from noisy ECGs of several hours’ length. The pipeline consists of three stages. A neural network (NN) trained to detect R peaks and distinguish these from noise; a measure to robustly detect false positives (FPs) and negatives (FNs) produced by the NN; a simple “alarm” algorithm for automatically removing FPs and interpolating FNs. In addition, we introduce the approach of encoding ECGs, tachograms and other cardiac time series in the form of raster images, which greatly speeds and eases their visual inspection and analysis. |
| Tasks | Electrocardiography (ECG), Heart Rate Variability, Time Series |
| Published | 2019-02-16 |
| URL | https://arxiv.org/abs/1902.06151 |
| https://arxiv.org/pdf/1902.06151 | |
| PWC | https://paperswithcode.com/paper/robust-and-fast-heart-rate-variability |
| Repo | |
| Framework | |
IT–IST at the SIGMORPHON 2019 Shared Task: Sparse Two-headed Models for Inflection
| Title | IT–IST at the SIGMORPHON 2019 Shared Task: Sparse Two-headed Models for Inflection |
| Authors | Ben Peters, Andr{'e} F. T. Martins |
| Abstract | This paper presents the Instituto de Telecomunica{\c{c}}{~o}es{–}Instituto Superior T{'e}cnico submission to Task 1 of the SIGMORPHON 2019 Shared Task. Our models combine sparse sequence-to-sequence models with a two-headed attention mechanism that learns separate attention distributions for the lemma and inflectional tags. Among submissions to Task 1, our models rank second and third. Despite the low data setting of the task (only 100 in-language training examples), they learn plausible inflection patterns and often concentrate all probability mass into a small set of hypotheses, making beam search exact. |
| Tasks | |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-4207/ |
| https://www.aclweb.org/anthology/W19-4207 | |
| PWC | https://paperswithcode.com/paper/it-ist-at-the-sigmorphon-2019-shared-task |
| Repo | |
| Framework | |
What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition
| Title | What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition |
| Authors | Ting-Yun Chang, Yun-Nung Chen |
| Abstract | Contextualized word embeddings have boosted many NLP tasks compared with traditional static word embeddings. However, the word with a specific sense may have different contextualized embeddings due to its various contexts. To further investigate what contextualized word embeddings capture, this paper analyzes whether they can indicate the corresponding sense definitions and proposes a general framework that is capable of explaining word meanings given contextualized word embeddings for better interpretation. The experiments show that both ELMo and BERT embeddings can be well interpreted via a readable textual form, and the findings may benefit the research community for a better understanding of what the embeddings capture. |
| Tasks | Word Embeddings |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1627/ |
| https://www.aclweb.org/anthology/D19-1627 | |
| PWC | https://paperswithcode.com/paper/what-does-this-word-mean-explaining |
| Repo | |
| Framework | |
Neural Generation for Czech: Data and Baselines
| Title | Neural Generation for Czech: Data and Baselines |
| Authors | Ond{\v{r}}ej Du{\v{s}}ek, Filip Jur{\v{c}}{'\i}{\v{c}}ek |
| Abstract | We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach. While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires inflecting named entities, delexicalization or copy mechanisms do not work out-of-the-box and lexicalizing the generated outputs is non-trivial. In our experiments, we present two different approaches to this this problem: (1) using a neural language model to select the correct inflected form while lexicalizing, (2) a two-step generation setup: our sequence-to-sequence model generates an interleaved sequence of lemmas and morphological tags, which are then inflected by a morphological generator. |
| Tasks | Language Modelling |
| Published | 2019-10-01 |
| URL | https://www.aclweb.org/anthology/W19-8670/ |
| https://www.aclweb.org/anthology/W19-8670 | |
| PWC | https://paperswithcode.com/paper/neural-generation-for-czech-data-and-1 |
| Repo | |
| Framework | |
APE-QUEST
| Title | APE-QUEST |
| Authors | Joachim Van den Bogaert, Heidi Depraetere, Sara Szoc, Tom Vanallemeersch, Koen Van Winckel, Frederic Everaert, Lucia Specia, Julia Ive, Maxim Khalilov, Christine Maroti, Eduardo Farah, Artur Ventura |
| Abstract | |
| Tasks | |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-6717/ |
| https://www.aclweb.org/anthology/W19-6717 | |
| PWC | https://paperswithcode.com/paper/ape-quest |
| Repo | |
| Framework | |
Exploring Human Gender Stereotypes with Word Association Test
| Title | Exploring Human Gender Stereotypes with Word Association Test |
| Authors | Yupei Du, Yuanbin Wu, Man Lan |
| Abstract | Word embeddings have been widely used to study gender stereotypes in texts. One key problem regarding existing bias scores is to evaluate their validities: do they really reflect true bias levels? For a small set of words (e.g. occupations), we can rely on human annotations or external data. However, for most words, evaluating the correctness of them is still an open problem. In this work, we utilize word association test, which contains rich types of word connections annotated by human participants, to explore how gender stereotypes spread within our minds. Specifically, we use random walk on word association graph to derive bias scores for a large amount of words. Experiments show that these bias scores correlate well with bias in the real world. More importantly, comparing with word-embedding-based bias scores, it provides a different perspective on gender stereotypes in words. |
| Tasks | Word Embeddings |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1635/ |
| https://www.aclweb.org/anthology/D19-1635 | |
| PWC | https://paperswithcode.com/paper/exploring-human-gender-stereotypes-with-word |
| Repo | |
| Framework | |
Linguistic Analysis of Schizophrenia in Reddit Posts
| Title | Linguistic Analysis of Schizophrenia in Reddit Posts |
| Authors | Jonathan Zomick, Sarah Ita Levitan, Mark Serper |
| Abstract | We explore linguistic indicators of schizophrenia in Reddit discussion forums. Schizophrenia (SZ) is a chronic mental disorder that affects a person{'}s thoughts and behaviors. Identifying and detecting signs of SZ is difficult given that SZ is relatively uncommon, affecting approximately 1{%} of the US population, and people suffering with SZ often believe that they do not have the disorder. Linguistic abnormalities are a hallmark of SZ and many of the illness{'}s symptoms are manifested through language. In this paper we leverage the vast amount of data available from social media and use statistical and machine learning approaches to study linguistic characteristics of SZ. We collected and analyzed a large corpus of Reddit posts from users claiming to have received a formal diagnosis of SZ and identified several linguistic features that differentiated these users from a control (CTL) group. We compared these results to other findings on social media linguistic analysis and SZ. We also developed a machine learning classifier to automatically identify self-identified users with SZ on Reddit. |
| Tasks | |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/W19-3009/ |
| https://www.aclweb.org/anthology/W19-3009 | |
| PWC | https://paperswithcode.com/paper/linguistic-analysis-of-schizophrenia-in |
| Repo | |
| Framework | |
Underspecification and interpretive parallelism in Dependent Type Semantics
| Title | Underspecification and interpretive parallelism in Dependent Type Semantics |
| Authors | Yusuke Kubota, Koji Mineshima, Robert Levine, Daisuke Bekki |
| Abstract | |
| Tasks | |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/W19-1001/ |
| https://www.aclweb.org/anthology/W19-1001 | |
| PWC | https://paperswithcode.com/paper/underspecification-and-interpretive |
| Repo | |
| Framework | |
InGAN: Capturing and Retargeting the “DNA” of a Natural Image
| Title | InGAN: Capturing and Retargeting the “DNA” of a Natural Image |
| Authors | Assaf Shocher, Shai Bagon, Phillip Isola, Michal Irani |
| Abstract | Generative Adversarial Networks (GANs) typically learn a distribution of images in a large image dataset, and are then able to generate new images from this distribution. However, each natural image has its own internal statistics, captured by its unique distribution of patches. In this paper we propose an “Internal GAN” (InGAN) – an image-specific GAN – which trains on a single input image and learns its internal distribution of patches. It is then able to synthesize a plethora of new natural images of significantly different sizes, shapes and aspect-ratios - all with the same internal patch-distribution (same “DNA”) as the input image. In particular, despite large changes in global size/shape of the image, all elements inside the image maintain their local size/shape. InGAN is fully unsupervised, requiring no additional data other than the input image itself. Once trained on the input image, it can remap the input to any size or shape in a single feedforward pass, while preserving the same internal patch distribution. InGAN provides a unified framework for a variety of tasks, bridging the gap between textures and natural images. |
| Tasks | |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Shocher_InGAN_Capturing_and_Retargeting_the_DNA_of_a_Natural_Image_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Shocher_InGAN_Capturing_and_Retargeting_the_DNA_of_a_Natural_Image_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/ingan-capturing-and-retargeting-the-dna-of-a |
| Repo | |
| Framework | |
Relating RNN Layers with the Spectral WFA Ranks in Sequence Modelling
| Title | Relating RNN Layers with the Spectral WFA Ranks in Sequence Modelling |
| Authors | Farhana Ferdousi Liza, Marek Grzes |
| Abstract | We analyse Recurrent Neural Networks (RNNs) to understand the significance of multiple LSTM layers. We argue that the Weighted Finite-state Automata (WFA) trained using a spectral learning algorithm are helpful to analyse RNNs. Our results suggest that multiple LSTM layers in RNNs help learning distributed hidden states, but have a smaller impact on the ability to learn long-term dependencies. The analysis is based on the empirical results, however relevant theory (whenever possible) was discussed to justify and support our conclusions. |
| Tasks | |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-3903/ |
| https://www.aclweb.org/anthology/W19-3903 | |
| PWC | https://paperswithcode.com/paper/relating-rnn-layers-with-the-spectral-wfa |
| Repo | |
| Framework | |
A Cross-Topic Method for Supervised Relevance Classification
| Title | A Cross-Topic Method for Supervised Relevance Classification |
| Authors | Jiawei Yong |
| Abstract | In relevance classification, we hope to judge whether some utterances expressed on a topic are relevant or not. A usual method is to train a specific classifier respectively for each topic. However, in that way, it easily causes an underfitting problem in supervised learning model, since annotated data can be insufficient for every single topic. In this paper, we explore the common features beyond different topics and propose our cross-topic relevance embedding aggregation methodology (CREAM) that can expand the range of training data and apply what has been learned from source topics to a target topic. In our experiment, we show that our proposal could capture common features within a small amount of annotated data and improve the performance of relevance classification compared with other baselines. |
| Tasks | |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5520/ |
| https://www.aclweb.org/anthology/D19-5520 | |
| PWC | https://paperswithcode.com/paper/a-cross-topic-method-for-supervised-relevance |
| Repo | |
| Framework | |
Improving Neural Machine Translation with Neural Syntactic Distance
| Title | Improving Neural Machine Translation with Neural Syntactic Distance |
| Authors | Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao |
| Abstract | The explicit use of syntactic information has been proved useful for neural machine translation (NMT). However, previous methods resort to either tree-structured neural networks or long linearized sequences, both of which are inefficient. Neural syntactic distance (NSD) enables us to represent a constituent tree using a sequence whose length is identical to the number of words in the sentence. NSD has been used for constituent parsing, but not in machine translation. We propose five strategies to improve NMT with NSD. Experiments show that it is not trivial to improve NMT with NSD; however, the proposed strategies are shown to improve translation performance of the baseline model (+2.1 (En{–}Ja), +1.3 (Ja{–}En), +1.2 (En{–}Ch), and +1.0 (Ch{–}En) BLEU). |
| Tasks | Machine Translation |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/N19-1205/ |
| https://www.aclweb.org/anthology/N19-1205 | |
| PWC | https://paperswithcode.com/paper/improving-neural-machine-translation-with-2 |
| Repo | |
| Framework | |
Learning Meshes for Dense Visual SLAM
| Title | Learning Meshes for Dense Visual SLAM |
| Authors | Michael Bloesch, Tristan Laidlow, Ronald Clark, Stefan Leutenegger, Andrew J. Davison |
| Abstract | Estimating motion and surrounding geometry of a moving camera remains a challenging inference problem. From an information theoretic point of view, estimates should get better as more information is included, such as is done in dense SLAM, but this is strongly dependent on the validity of the underlying models. In the present paper, we use triangular meshes as both compact and dense geometry representation. To allow for simple and fast usage, we propose a view-based formulation for which we predict the in-plane vertex coordinates directly from images and then employ the remaining vertex depth components as free variables. Flexible and continuous integration of information is achieved through the use of a residual based inference technique. This so-called factor graph encodes all information as mapping from free variables to residuals, the squared sum of which is minimised during inference. We propose the use of different types of learnable residuals, which are trained end-to-end to increase their suitability as information bearing models and to enable accurate and reliable estimation. Detailed evaluation of all components is provided on both synthetic and real data which confirms the practicability of the presented approach. |
| Tasks | |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Bloesch_Learning_Meshes_for_Dense_Visual_SLAM_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Bloesch_Learning_Meshes_for_Dense_Visual_SLAM_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/learning-meshes-for-dense-visual-slam |
| Repo | |
| Framework | |
DeepAnalyzer at SemEval-2019 Task 6: A deep learning-based ensemble method for identifying offensive tweets
| Title | DeepAnalyzer at SemEval-2019 Task 6: A deep learning-based ensemble method for identifying offensive tweets |
| Authors | Gretel Liz De la Pe{~n}a, Paolo Rosso |
| Abstract | This paper describes the system we developed for SemEval 2019 on Identifying and Categorizing Offensive Language in Social Media (OffensEval - Task 6). The task focuses on offensive language in tweets. It is organized into three sub-tasks for offensive language identification; automatic categorization of offense types and offense target identification. The approach for the first subtask is a deep learning-based ensemble method which uses a Bidirectional LSTM Recurrent Neural Network and a Convolutional Neural Network. Additionally we use the information from part-of-speech tagging of tweets for target identification and combine previous results for categorization of offense types. |
| Tasks | Language Identification, Part-Of-Speech Tagging |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2104/ |
| https://www.aclweb.org/anthology/S19-2104 | |
| PWC | https://paperswithcode.com/paper/deepanalyzer-at-semeval-2019-task-6-a-deep |
| Repo | |
| Framework | |
