Paper Group NANR 249
Practical Coding Function Design for Time-Of-Flight Imaging. Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features. DANGNT@UIT.VNU-HCM at SemEval 2019 Task 1: Graph Transformation System from Stanford Basic Dependencies to Universal Conceptual Cognitive Annotation (UCCA). Visualizing Linguistic Chan …
Practical Coding Function Design for Time-Of-Flight Imaging
| Title | Practical Coding Function Design for Time-Of-Flight Imaging |
| Authors | Felipe Gutierrez-Barragan, Syed Azer Reza, Andreas Velten, Mohit Gupta |
| Abstract | The depth resolution of a continuous-wave time-of-flight (CW-ToF) imaging system is determined by its coding functions. Recently, there has been growing interest in the design of new high-performance CW-ToF coding functions. However, these functions are typically designed in a hardware agnostic manner, i.e., without considering the practical device limitations, such as bandwidth, source power, digital (binary) function generation. Therefore, despite theoretical improvements, practical implementation of these functions remains a challenge. We present a constrained optimization approach for designing practical coding functions that adhere to hardware constraints. The optimization problem is non-convex with a large search space and no known globally optimal solutions. To make the problem tractable, we design an iterative, alternating least-squares algorithm, along with convex relaxation of the constraints. Using this approach, we design high-performance coding functions that can be implemented on existing hardware with minimal modifications. We demonstrate the performance benefits of the resulting functions via extensive simulations and a hardware prototype. |
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| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Gutierrez-Barragan_Practical_Coding_Function_Design_for_Time-Of-Flight_Imaging_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Gutierrez-Barragan_Practical_Coding_Function_Design_for_Time-Of-Flight_Imaging_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/practical-coding-function-design-for-time-of |
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Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features
| Title | Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features |
| Authors | Rodrigo Agerri |
| Abstract | In this paper we describe our participation to the Hyperpartisan News Detection shared task at SemEval 2019. Motivated by the late arrival of Doris Martin, we test a previously developed document classification system which consists of a combination of clustering features implemented on top of some simple shallow local features. We show how leveraging distributional features obtained from large in-domain unlabeled data helps to easily and quickly develop a reasonably good performing system for detecting hyperpartisan news. The system and models generated for this task are publicly available. |
| Tasks | Document Classification |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2161/ |
| https://www.aclweb.org/anthology/S19-2161 | |
| PWC | https://paperswithcode.com/paper/doris-martin-at-semeval-2019-task-4 |
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DANGNT@UIT.VNU-HCM at SemEval 2019 Task 1: Graph Transformation System from Stanford Basic Dependencies to Universal Conceptual Cognitive Annotation (UCCA)
| Title | DANGNT@UIT.VNU-HCM at SemEval 2019 Task 1: Graph Transformation System from Stanford Basic Dependencies to Universal Conceptual Cognitive Annotation (UCCA) |
| Authors | Dang Tuan Nguyen, Trung Tran |
| Abstract | This paper describes the graph transfor-mation system (GT System) for SemEval 2019 Task 1: Cross-lingual Semantic Parsing with Universal Conceptual Cognitive Annotation (UCCA)1. The input of GT System is a pair of text and its unannotated xml, which is a layer 0 part of UCCA form. The output of GT System is the corresponding full UCCA xml. Based on the idea of graph illustration and transformation, we perform four main tasks when building GT System. At the first task, we illustrate the graph form of stanford dependencies2 of input text. We then transform into an intermediate graph in the second task. At the third task, we continue to transform into ouput graph form. Finally, we create the output UCCA xml. The evaluation results show that our method generates good-quality UCCA xml and has a meaningful contribution to the semantic represetation sub-field in Natural Language Processing. |
| Tasks | Semantic Parsing |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2013/ |
| https://www.aclweb.org/anthology/S19-2013 | |
| PWC | https://paperswithcode.com/paper/dangntuitvnu-hcm-at-semeval-2019-task-1-graph |
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Visualizing Linguistic Change as Dimension Interactions
| Title | Visualizing Linguistic Change as Dimension Interactions |
| Authors | Christin Sch{"a}tzle, Frederik L. Dennig, Michael Blumenschein, Daniel A. Keim, Miriam Butt |
| Abstract | Historical change typically is the result of complex interactions between several linguistic factors. Identifying the relevant factors and understanding how they interact across the temporal dimension is the core remit of historical linguistics. With respect to corpus work, this entails a separate annotation, extraction and painstaking pair-wise comparison of the relevant bits of information. This paper presents a significant extension of HistoBankVis, a multilayer visualization system which allows a fast and interactive exploration of complex linguistic data. Linguistic factors can be understood as data dimensions which show complex interrelationships. We model these relationships with the Parallel Sets technique. We demonstrate the powerful potential of this technique by applying the system to understanding the interaction of case, grammatical relations and word order in the history of Icelandic. |
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| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-4734/ |
| https://www.aclweb.org/anthology/W19-4734 | |
| PWC | https://paperswithcode.com/paper/visualizing-linguistic-change-as-dimension |
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CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM
| Title | CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM |
| Authors | Elham Mohammadi, Hessam Amini, Leila Kosseim |
| Abstract | This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues. For our final system, we used a neural network with pretrained ELMo word embeddings and POS tags as input, GRUs as hidden units, an attention mechanism to capture representations of the dialogues, and an SVM classifier which used the learned network representations to perform the task of multi-class classification.This system yielded a micro-averaged F1 score of 0.7072 for the three emotion classes, improving the baseline by approximately 12{%}. |
| Tasks | Word Embeddings |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2023/ |
| https://www.aclweb.org/anthology/S19-2023 | |
| PWC | https://paperswithcode.com/paper/clac-lab-at-semeval-2019-task-3-contextual |
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CLARK at SemEval-2019 Task 3: Exploring the Role of Context to Identify Emotion in a Short Conversation
| Title | CLARK at SemEval-2019 Task 3: Exploring the Role of Context to Identify Emotion in a Short Conversation |
| Authors | Joseph Cummings, Jason Wilson |
| Abstract | With text lacking valuable information avail-able in other modalities, context may provide useful information to better detect emotions. In this paper, we do a systematic exploration of the role of context in recognizing emotion in a conversation. We use a Naive Bayes model to show that inferring the mood of the conversation before classifying individual utterances leads to better performance. Additionally, we find that using context while train-ing the model significantly decreases performance. Our approach has the additional bene-fit that its performance rivals a baseline LSTM model while requiring fewer resources. |
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| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2024/ |
| https://www.aclweb.org/anthology/S19-2024 | |
| PWC | https://paperswithcode.com/paper/clark-at-semeval-2019-task-3-exploring-the |
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Online EXP3 Learning in Adversarial Bandits with Delayed Feedback
| Title | Online EXP3 Learning in Adversarial Bandits with Delayed Feedback |
| Authors | Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose Blanchet |
| Abstract | Consider a player that in each of T rounds chooses one of K arms. An adversary chooses the cost of each arm in a bounded interval, and a sequence of feedback delays \left{ d_{t}\right} that are unknown to the player. After picking arm a_{t} at round t, the player receives the cost of playing this arm d_{t} rounds later. In cases where t+d_{t}>T, this feedback is simply missing. We prove that the EXP3 algorithm (that uses the delayed feedback upon its arrival) achieves a regret of O\left(\sqrt{\ln K\left(KT+\sum_{t=1}^{T}d_{t}\right)}\right). For the case where \sum_{t=1}^{T}d_{t} and T are unknown, we propose a novel doubling trick for online learning with delays and prove that this adaptive EXP3 achieves a regret of O\left(\sqrt{\ln K\left(K^{2}T+\sum_{t=1}^{T}d_{t}\right)}\right). We then consider a two player zero-sum game where players experience asynchronous delays. We show that even when the delays are large enough such that players no longer enjoy the “no-regret property”, (e.g., where d_{t}=O\left(t\log t\right)) the ergodic average of the strategy profile still converges to the set of Nash equilibria of the game. The result is made possible by choosing an adaptive step size \eta_{t} that is not summable but is square summable, and proving a “weighted regret bound” for this general case. |
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| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/9312-online-exp3-learning-in-adversarial-bandits-with-delayed-feedback |
| http://papers.nips.cc/paper/9312-online-exp3-learning-in-adversarial-bandits-with-delayed-feedback.pdf | |
| PWC | https://paperswithcode.com/paper/online-exp3-learning-in-adversarial-bandits |
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CLP at SemEval-2019 Task 3: Multi-Encoder in Hierarchical Attention Networks for Contextual Emotion Detection
| Title | CLP at SemEval-2019 Task 3: Multi-Encoder in Hierarchical Attention Networks for Contextual Emotion Detection |
| Authors | Changjie Li, Yun Xing |
| Abstract | In this paper, we describe the participation of team {''}CLP{''} in SemEval-2019 Task 3 {``}Con- textual Emotion Detection in Text{''} that aims to classify emotion of user utterance in tex- tual conversation. The submitted system is a deep learning architecture based on Hier- archical Attention Networks (HAN) and Em- bedding from Language Model (ELMo). The core of the architecture contains two represen- tation layers. The first one combines the out- puts of ELMo, hand-craft features and Bidi- rectional Long Short-Term Memory with At- tention (Bi-LSTM-Attention) to represent user utterance. The second layer use a Bi-LSTM- Attention encoder to represent the conversa- tion. Our system achieved F1 score of 0.7524 which outperformed the baseline model of the organizers by 0.1656. | |
| Tasks | Language Modelling |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2025/ |
| https://www.aclweb.org/anthology/S19-2025 | |
| PWC | https://paperswithcode.com/paper/clp-at-semeval-2019-task-3-multi-encoder-in |
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Shape Unicode: A Unified Shape Representation
| Title | Shape Unicode: A Unified Shape Representation |
| Authors | Sanjeev Muralikrishnan, Vladimir G. Kim, Matthew Fisher, Siddhartha Chaudhuri |
| Abstract | 3D shapes come in varied representations from a set of points to a set of images, each capturing different aspects of the shape. We propose a unified code for 3D shapes, dubbed Shape Unicode, that imbibes shape cues across these representations into a single code, and a novel framework to learn such a code space for any 3D shape dataset. We discuss this framework as a single go-to training model for any input representation, and demonstrate the effectiveness of the learned code space by applying it directly to common shape analysis tasks – discriminative and generative. In this work, we use three common representations – voxel grids, point clouds and multi-view projections – and combine them into a single code. Note that while we use all three representations at training time, the code can be derived from any single representation during testing. We evaluate this code space on shape retrieval, segmentation and correspondence, and show that the unified code performs better than the individual representations themselves. Additionally, this code space compares quite well to the representation-specific state-of-the-art in these tasks. We also qualitatively discuss linear interpolation between points in this space, by synthesizing from intermediate points. |
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| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Muralikrishnan_Shape_Unicode_A_Unified_Shape_Representation_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Muralikrishnan_Shape_Unicode_A_Unified_Shape_Representation_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/shape-unicode-a-unified-shape-representation |
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Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy
| Title | Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy |
| Authors | Diana Galv{'a}n-Sosa |
| Abstract | Reading comprehension (RC) through question answering is a useful method for evaluating if a reader understands a text. Standard accuracy metrics are used for evaluation, where high accuracy is taken as indicative of a good understanding. However, literature in quality learning suggests that task performance should also be evaluated on the undergone process to answer. The Question-Answer Relationship (QAR) is one of the strategies for evaluating a reader{'}s understanding based on their ability to select different sources of information depending on the question type. We propose the creation of a dataset to learn the QAR strategy with weak supervision. We expect to complement current work on reading comprehension by introducing a new setup for evaluation. |
| Tasks | Accuracy Metrics, Question Answering, Reading Comprehension |
| Published | 2019-07-01 |
| URL | https://www.aclweb.org/anthology/P19-2014/ |
| https://www.aclweb.org/anthology/P19-2014 | |
| PWC | https://paperswithcode.com/paper/active-reading-comprehension-a-dataset-for |
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Spatial Aggregation Facilitates Discovery of Spatial Topics
| Title | Spatial Aggregation Facilitates Discovery of Spatial Topics |
| Authors | Aniruddha Maiti, Slobodan Vucetic |
| Abstract | Spatial aggregation refers to merging of documents created at the same spatial location. We show that by spatial aggregation of a large collection of documents and applying a traditional topic discovery algorithm on the aggregated data we can efficiently discover spatially distinct topics. By looking at topic discovery through matrix factorization lenses we show that spatial aggregation allows low rank approximation of the original document-word matrix, in which spatially distinct topics are preserved and non-spatial topics are aggregated into a single topic. Our experiments on synthetic data confirm this observation. Our experiments on 4.7 million tweets collected during the Sandy Hurricane in 2012 show that spatial and temporal aggregation allows rapid discovery of relevant spatial and temporal topics during that period. Our work indicates that different forms of document aggregation might be effective in rapid discovery of various types of distinct topics from large collections of documents. |
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| Published | 2019-07-01 |
| URL | https://www.aclweb.org/anthology/P19-1025/ |
| https://www.aclweb.org/anthology/P19-1025 | |
| PWC | https://paperswithcode.com/paper/spatial-aggregation-facilitates-discovery-of |
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E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text
| Title | E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text |
| Authors | Harsh Patel |
| Abstract | This paper discusses the solution to the problem statement of the SemEval19: EmoContext competition which is {''}Contextual Emotion Detection in Texts{''}. The paper includes the explanation of an architecture that I created by exploiting the embedding layers of Word2Vec and GloVe using LSTMs as memory unit cells which detects approximate emotion of chats between two people in the English language provided in the textual form. The set of emotions on which the model was trained was Happy, Sad, Angry and Others. The paper also includes an analysis of different conventional machine learning algorithms in comparison to E-LSTM. |
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| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2030/ |
| https://www.aclweb.org/anthology/S19-2030 | |
| PWC | https://paperswithcode.com/paper/e-lstm-at-semeval-2019-task-3-semantic-and |
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Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation
| Title | Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation |
| Authors | Bram Bulte, Arda Tezcan |
| Abstract | We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM). We propose and test two methods for augmenting NMT training data with fuzzy TM matches. Tests on the DGT-TM data set for two language pairs show consistent and substantial improvements over a range of baseline systems. The results suggest that this method is promising for any translation environment in which a sizeable TM is available and a certain amount of repetition across translations is to be expected, especially considering its ease of implementation. |
| Tasks | Data Augmentation, Machine Translation |
| Published | 2019-07-01 |
| URL | https://www.aclweb.org/anthology/P19-1175/ |
| https://www.aclweb.org/anthology/P19-1175 | |
| PWC | https://paperswithcode.com/paper/neural-fuzzy-repair-integrating-fuzzy-matches |
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Improving Anaphora Resolution in Neural Machine Translation Using Curriculum Learning
| Title | Improving Anaphora Resolution in Neural Machine Translation Using Curriculum Learning |
| Authors | Dario Stojanovski, Alex Fraser, er |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-6614/ |
| https://www.aclweb.org/anthology/W19-6614 | |
| PWC | https://paperswithcode.com/paper/improving-anaphora-resolution-in-neural |
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Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks
| Title | Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks |
| Authors | Xingwei Tan, Yi Cai, Changxi Zhu |
| Abstract | Aspect-level sentiment classification, which is a fine-grained sentiment analysis task, has received lots of attention these years. There is a phenomenon that people express both positive and negative sentiments towards an aspect at the same time. Such opinions with conflicting sentiments, however, are ignored by existing studies, which design models based on the absence of them. We argue that the exclusion of conflict opinions is problematic, for the reason that it represents an important style of human thinking {–} dialectic thinking. If a real-world sentiment classification system ignores the existence of conflict opinions when it is designed, it will incorrectly mixed conflict opinions into other sentiment polarity categories in action. Existing models have problems when recognizing conflicting opinions, such as data sparsity. In this paper, we propose a multi-label classification model with dual attention mechanism to address these problems. |
| Tasks | Multi-Label Classification, Sentiment Analysis |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1342/ |
| https://www.aclweb.org/anthology/D19-1342 | |
| PWC | https://paperswithcode.com/paper/recognizing-conflict-opinions-in-aspect-level |
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