Paper Group NANR 116
Reviewers for Volume 44. Multi-Source Multi-Class Fake News Detection. Weakly-supervised 3D Hand Pose Estimation from Monocular RGB Images. Visualizing Group Dynamics based on Multiparty Meeting Understanding. LIA: A Natural Language Programmable Personal Assistant. A Context-aware Convolutional Natural Language Generation model for Dialogue System …
Reviewers for Volume 44
| Title | Reviewers for Volume 44 |
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| Published | 2018-12-01 |
| URL | https://www.aclweb.org/anthology/J18-4013/ |
| https://www.aclweb.org/anthology/J18-4013 | |
| PWC | https://paperswithcode.com/paper/reviewers-for-volume-44 |
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Multi-Source Multi-Class Fake News Detection
| Title | Multi-Source Multi-Class Fake News Detection |
| Authors | Hamid Karimi, Proteek Roy, Sari Saba-Sadiya, Jiliang Tang |
| Abstract | Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent years. Fake news is typically written intentionally to mislead readers, which determines that fake news detection merely based on news content is tremendously challenging. Meanwhile, fake news could contain true evidence to mock true news and presents different degrees of fakeness, which further exacerbates the detection difficulty. On the other hand, the spread of fake news produces various types of data from different perspectives. These multiple sources provide rich contextual information about fake news and offer unprecedented opportunities for advanced fake news detection. In this paper, we study fake news detection with different degrees of fakeness by integrating multiple sources. In particular, we introduce approaches to combine information from multiple sources and to discriminate between different degrees of fakeness, and propose a Multi-source Multi-class Fake news Detection framework MMFD, which combines automated feature extraction, multi-source fusion and automated degrees of fakeness detection into a coherent and interpretable model. Experimental results on the real-world data demonstrate the effectiveness of the proposed framework and extensive experiments are further conducted to understand the working of the proposed framework. |
| Tasks | Fake News Detection |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1131/ |
| https://www.aclweb.org/anthology/C18-1131 | |
| PWC | https://paperswithcode.com/paper/multi-source-multi-class-fake-news-detection |
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Weakly-supervised 3D Hand Pose Estimation from Monocular RGB Images
| Title | Weakly-supervised 3D Hand Pose Estimation from Monocular RGB Images |
| Authors | Yujun Cai, Liuhao Ge, Jianfei Cai, Junsong Yuan |
| Abstract | Compared with depth-based 3D hand pose estimation, it is more challenging to infer 3D hand pose from monocular RGB images, due to substantial depth ambiguity and the difficulty of obtaining fully-annotated training data. Different from existing learning-based monocular RGB-input approaches that require accurate 3D annotations for training, we propose to leverage the depth images that can be easily obtained from commodity RGB-D cameras during training, while during testing we take only RGB inputs for 3D joint predictions. In this way, we alleviate the burden of the costly 3D annotations in real-world dataset. Particularly, we propose a weakly-supervised method, adaptating from fully-annotated synthetic dataset to weakly-labeled real-world dataset with the aid of a depth regularizer, which generates depth maps from predicted 3D pose and serves as weak supervision for 3D pose regression. Extensive experiments on benchmark datasets validate the effectiveness of the proposed depth regularizer in both weakly-supervised and fully-supervised settings. |
| Tasks | Hand Pose Estimation, Pose Estimation |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Yujun_Cai_Weakly-supervised_3D_Hand_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Yujun_Cai_Weakly-supervised_3D_Hand_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/weakly-supervised-3d-hand-pose-estimation |
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Visualizing Group Dynamics based on Multiparty Meeting Understanding
| Title | Visualizing Group Dynamics based on Multiparty Meeting Understanding |
| Authors | Ni Zhang, Tongtao Zhang, Indrani Bhattacharya, Heng Ji, Rich Radke |
| Abstract | Group discussions are usually aimed at sharing opinions, reaching consensus and making good decisions based on group knowledge. During a discussion, participants might adjust their own opinions as well as tune their attitudes towards others{'} opinions, based on the unfolding interactions. In this paper, we demonstrate a framework to visualize such dynamics; at each instant of a conversation, the participants{'} opinions and potential influence on their counterparts is easily visualized. We use multi-party meeting opinion mining based on bipartite graphs to extract opinions and calculate mutual influential factors, using the Lunar Survival Task as a study case. |
| Tasks | Decision Making, Opinion Mining, Speech Recognition |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/D18-2017/ |
| https://www.aclweb.org/anthology/D18-2017 | |
| PWC | https://paperswithcode.com/paper/visualizing-group-dynamics-based-on |
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LIA: A Natural Language Programmable Personal Assistant
| Title | LIA: A Natural Language Programmable Personal Assistant |
| Authors | Igor Labutov, Shashank Srivastava, Tom Mitchell |
| Abstract | We present LIA, an intelligent personal assistant that can be programmed using natural language. Our system demonstrates multiple competencies towards learning from human-like interactions. These include the ability to be taught reusable conditional procedures, the ability to be taught new knowledge about the world (concepts in an ontology) and the ability to be taught how to ground that knowledge in a set of sensors and effectors. Building such a system highlights design questions regarding the overall architecture that such an agent should have, as well as questions about parsing and grounding language in situational contexts. We outline key properties of this architecture, and demonstrate a prototype that embodies them in the form of a personal assistant on an Android device. |
| Tasks | Semantic Parsing |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/D18-2025/ |
| https://www.aclweb.org/anthology/D18-2025 | |
| PWC | https://paperswithcode.com/paper/lia-a-natural-language-programmable-personal |
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A Context-aware Convolutional Natural Language Generation model for Dialogue Systems
| Title | A Context-aware Convolutional Natural Language Generation model for Dialogue Systems |
| Authors | Sourab Mangrulkar, Suhani Shrivastava, Veena Thenkanidiyoor, Dileep Aroor Dinesh |
| Abstract | Natural language generation (NLG) is an important component in spoken dialog systems (SDSs). A model for NLG involves sequence to sequence learning. State-of-the-art NLG models are built using recurrent neural network (RNN) based sequence to sequence models (Ond{\v{r}}ej Du{\v{s}}ek and Filip Jur{\v{c}}{'\i}{\v{c}}ek, 2016a). Convolutional sequence to sequence based models have been used in the domain of machine translation but their application as Natural Language Generators in dialogue systems is still unexplored. In this work, we propose a novel approach to NLG using convolutional neural network (CNN) based sequence to sequence learning. CNN-based approach allows to build a hierarchical model which encapsulates dependencies between words via shorter path unlike RNNs. In contrast to recurrent models, convolutional approach allows for efficient utilization of computational resources by parallelizing computations over all elements, and eases the learning process by applying constant number of nonlinearities. We also propose to use CNN-based reranker for obtaining responses having semantic correspondence with input dialogue acts. The proposed model is capable of entrainment. Studies using a standard dataset shows the effectiveness of the proposed CNN-based approach to NLG. |
| Tasks | Machine Translation, Spoken Dialogue Systems, Text Generation |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-5020/ |
| https://www.aclweb.org/anthology/W18-5020 | |
| PWC | https://paperswithcode.com/paper/a-context-aware-convolutional-natural |
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The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task
| Title | The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task |
| Authors | Vassilis Papavassiliou, Sokratis Sofianopoulos, Prokopis Prokopidis, Stelios Piperidis |
| Abstract | This paper describes the submission of the Institute for Language and Speech Processing/Athena Research and Innovation Center (ILSP/ARC) for the WMT 2018 Parallel Corpus Filtering shared task. We explore several properties of sentences and sentence pairs that our system explored in the context of the task with the purpose of clustering sentence pairs according to their appropriateness in training MT systems. We also discuss alternative methods for ranking the sentence pairs of the most appropriate clusters with the aim of generating the two datasets (of 10 and 100 million words as required in the task) that were evaluated. By summarizing the results of several experiments that were carried out by the organizers during the evaluation phase, our submission achieved an average BLEU score of 26.41, even though it does not make use of any language-specific resources like bilingual lexica, monolingual corpora, or MT output, while the average score of the best participant system was 27.91. |
| Tasks | Language Modelling, Machine Translation, Outlier Detection |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-6484/ |
| https://www.aclweb.org/anthology/W18-6484 | |
| PWC | https://paperswithcode.com/paper/the-ilsparc-submission-to-the-wmt-2018 |
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Expletives in Universal Dependency Treebanks
| Title | Expletives in Universal Dependency Treebanks |
| Authors | Gosse Bouma, Jan Hajic, Dag Haug, Joakim Nivre, Per Erik Solberg, Lilja {\O}vrelid |
| Abstract | Although treebanks annotated according to the guidelines of Universal Dependencies (UD) now exist for many languages, the goal of annotating the same phenomena in a cross-linguistically consistent fashion is not always met. In this paper, we investigate one phenomenon where we believe such consistency is lacking, namely expletive elements. Such elements occupy a position that is structurally associated with a core argument (or sometimes an oblique dependent), yet are non-referential and semantically void. Many UD treebanks identify at least some elements as expletive, but the range of phenomena differs between treebanks, even for closely related languages, and sometimes even for different treebanks for the same language. In this paper, we present criteria for identifying expletives that are applicable across languages and compatible with the goals of UD, give an overview of expletives as found in current UD treebanks, and present recommendations for the annotation of expletives so that more consistent annotation can be achieved in future releases. |
| Tasks | Coreference Resolution, Question Answering |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-6003/ |
| https://www.aclweb.org/anthology/W18-6003 | |
| PWC | https://paperswithcode.com/paper/expletives-in-universal-dependency-treebanks |
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View-graph Selection Framework for SfM
| Title | View-graph Selection Framework for SfM |
| Authors | Rajvi Shah, Visesh Chari, P J Narayanan |
| Abstract | View-graph is an essential input to large-scale structure from motion (SfM) pipelines. Accuracy and efficiency of large-scale SfM is crucially dependent on the input view-graph. Inconsistent or inaccurate edges can lead to inferior or wrong reconstruction. Most SfM methods remove `undesirable’ images and pairs using several fixed heuristic criteria, and propose tailor-made solutions to achieve specific reconstruction objectives such as efficiency, accuracy, or disambiguation. In contrast to these disparate solutions, we propose an optimization based formulation that can be used to achieve these different reconstruction objectives with task-specific cost modeling that uses and construct a very efficient network flow based formulation for its approximate solution. The abstraction brought on by this selection mechanism separates the challenges specific to datasets and reconstruction objectives from the standard SfM pipeline and improves its generalization. This paper mainly focuses on application of this framework with standard SfM pipeline for accurate and ghost-free reconstructions of highly ambiguous datasets. To model selection costs for this task, we introduce new disambiguation priors based on local geometry. We further demonstrate versatility of the method by using it for the general objective of accurate and efficient reconstruction of large-scale Internet datasets using costs based on well-known SfM priors. | |
| Tasks | Model Selection |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Rajvi_Shah_View-graph_Selection_Framework_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Rajvi_Shah_View-graph_Selection_Framework_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/view-graph-selection-framework-for-sfm |
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A Multimodal Corpus for Mutual Gaze and Joint Attention in Multiparty Situated Interaction
| Title | A Multimodal Corpus for Mutual Gaze and Joint Attention in Multiparty Situated Interaction |
| Authors | Dimosthenis Kontogiorgos, Vanya Avramova, Alex, Simon erson, Patrik Jonell, Catharine Oertel, Jonas Beskow, Gabriel Skantze, Joakim Gustafson |
| Abstract | |
| Tasks | Motion Capture |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1019/ |
| https://www.aclweb.org/anthology/L18-1019 | |
| PWC | https://paperswithcode.com/paper/a-multimodal-corpus-for-mutual-gaze-and-joint |
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Learning Word Embeddings for Low-Resource Languages by PU Learning
| Title | Learning Word Embeddings for Low-Resource Languages by PU Learning |
| Authors | Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang |
| Abstract | Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how to effectively learn a word embedding model on a corpus with only a few million tokens. In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved. In contrast to existing approaches often only sample a few unobserved word pairs as negative samples, we argue that the zero entries in the co-occurrence matrix also provide valuable information. We then design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix and validate the proposed approaches in four different languages. |
| Tasks | Document Ranking, Image Captioning, Learning Word Embeddings, Named Entity Recognition, Question Answering, Sentiment Analysis, Word Embeddings |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-1093/ |
| https://www.aclweb.org/anthology/N18-1093 | |
| PWC | https://paperswithcode.com/paper/learning-word-embeddings-for-low-resource |
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Vectorial Semantic Spaces Do Not Encode Human Judgments of Intervention Similarity
| Title | Vectorial Semantic Spaces Do Not Encode Human Judgments of Intervention Similarity |
| Authors | Paola Merlo, Francesco Ackermann |
| Abstract | Despite their practical success and impressive performances, neural-network-based and distributed semantics techniques have often been criticized as they remain fundamentally opaque and difficult to interpret. In a vein similar to recent pieces of work investigating the linguistic abilities of these representations, we study another core, defining property of language: the property of long-distance dependencies. Human languages exhibit the ability to interpret discontinuous elements distant from each other in the string as if they were adjacent. This ability is blocked if a similar, but extraneous, element intervenes between the discontinuous components. We present results that show, under exhaustive and precise conditions, that one kind of word embeddings and the similarity spaces they define do not encode the properties of intervention similarity in long-distance dependencies, and that therefore they fail to represent this core linguistic notion. |
| Tasks | Word Embeddings |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/K18-1038/ |
| https://www.aclweb.org/anthology/K18-1038 | |
| PWC | https://paperswithcode.com/paper/vectorial-semantic-spaces-do-not-encode-human |
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Differentially Private k-Means with Constant Multiplicative Error
| Title | Differentially Private k-Means with Constant Multiplicative Error |
| Authors | Uri Stemmer, Haim Kaplan |
| Abstract | We design new differentially private algorithms for the Euclidean k-means problem, both in the centralized model and in the local model of differential privacy. In both models, our algorithms achieve significantly improved error guarantees than the previous state-of-the-art. In addition, in the local model, our algorithm significantly reduces the number of interaction rounds. Although the problem has been widely studied in the context of differential privacy, all of the existing constructions achieve only super constant approximation factors. We present, for the first time, efficient private algorithms for the problem with constant multiplicative error. Furthermore, we show how to modify our algorithms so they compute private coresets for k-means clustering in both models. |
| Tasks | |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7788-differentially-private-k-means-with-constant-multiplicative-error |
| http://papers.nips.cc/paper/7788-differentially-private-k-means-with-constant-multiplicative-error.pdf | |
| PWC | https://paperswithcode.com/paper/differentially-private-k-means-with-constant |
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Polarimetric Dense Monocular SLAM
| Title | Polarimetric Dense Monocular SLAM |
| Authors | Luwei Yang, Feitong Tan, Ao Li, Zhaopeng Cui, Yasutaka Furukawa, Ping Tan |
| Abstract | This paper presents a novel polarimetric dense monocular SLAM (PDMS) algorithm based on a polarization camera. The algorithm exploits both photometric and polarimetric light information to produce more accurate and complete geometry. The polarimetric information allows us to recover the azimuth angle of surface normals from each video frame to facilitate dense reconstruction, especially at textureless or specular regions. There are two challenges in our approach: 1) surface azimuth angles from the polarization camera are very noisy; and 2) we need a near real-time solution for SLAM. Previous successful methods on polarimetric multi-view stereo are offline and require manually pre-segmented object masks to suppress the effects of erroneous angle information along boundaries. Our fully automatic approach efficiently iterates azimuth-based depth propagations, two-view depth consistency check, and depth optimization to produce a depthmap in real-time, where all the algorithmic steps are carefully designed to enable a GPU implementation. To our knowledge, this paper is the first to propose a photometric and polarimetric method for dense SLAM. We have qualitatively and quantitatively evaluated our algorithm against a few of competing methods, demonstrating the superior performance on various indoor and outdoor scenes. |
| Tasks | |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Polarimetric_Dense_Monocular_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_Polarimetric_Dense_Monocular_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/polarimetric-dense-monocular-slam |
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Verbal Multiword Expressions in Basque Corpora
| Title | Verbal Multiword Expressions in Basque Corpora |
| Authors | Uxoa I{~n}urrieta, Itziar Aduriz, Ainara Estarrona, Itziar Gonzalez-Dios, Antton Gurrutxaga, Ruben Urizar, I{~n}aki Alegria |
| Abstract | This paper presents a Basque corpus where Verbal Multiword Expressions (VMWEs) were annotated following universal guidelines. Information on the annotation is given, and some ideas for discussion upon the guidelines are also proposed. The corpus is useful not only for NLP-related research, but also to draw conclusions on Basque phraseology in comparison with other languages. |
| Tasks | |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4911/ |
| https://www.aclweb.org/anthology/W18-4911 | |
| PWC | https://paperswithcode.com/paper/verbal-multiword-expressions-in-basque |
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