Paper Group NANR 121
Discovering Phonesthemes with Sparse Regularization. Proceedings of the 22nd Conference on Computational Natural Language Learning. Proceedings of the 5th Workshop on Argument Mining. Grapheme-level Awareness in Word Embeddings for Morphologically Rich Languages. TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Class …
Discovering Phonesthemes with Sparse Regularization
| Title | Discovering Phonesthemes with Sparse Regularization |
| Authors | Nelson F. Liu, Gina-Anne Levow, Noah A. Smith |
| Abstract | We introduce a simple method for extracting non-arbitrary form-meaning representations from a collection of semantic vectors. We treat the problem as one of feature selection for a model trained to predict word vectors from subword features. We apply this model to the problem of automatically discovering phonesthemes, which are submorphemic sound clusters that appear in words with similar meaning. Many of our model-predicted phonesthemes overlap with those proposed in the linguistics literature, and we validate our approach with human judgments. |
| Tasks | Feature Selection |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-1206/ |
| https://www.aclweb.org/anthology/W18-1206 | |
| PWC | https://paperswithcode.com/paper/discovering-phonesthemes-with-sparse |
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Proceedings of the 22nd Conference on Computational Natural Language Learning
| Title | Proceedings of the 22nd Conference on Computational Natural Language Learning |
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| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/K18-1000/ |
| https://www.aclweb.org/anthology/K18-1000 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-22nd-conference-on |
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Proceedings of the 5th Workshop on Argument Mining
| Title | Proceedings of the 5th Workshop on Argument Mining |
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| Abstract | |
| Tasks | Argument Mining |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5200/ |
| https://www.aclweb.org/anthology/W18-5200 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-5th-workshop-on-argument |
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Grapheme-level Awareness in Word Embeddings for Morphologically Rich Languages
| Title | Grapheme-level Awareness in Word Embeddings for Morphologically Rich Languages |
| Authors | Suzi Park, Hyopil Shin |
| Abstract | |
| Tasks | Word Embeddings |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1471/ |
| https://www.aclweb.org/anthology/L18-1471 | |
| PWC | https://paperswithcode.com/paper/grapheme-level-awareness-in-word-embeddings |
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TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts
| Title | TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts |
| Authors | Martin Gluhak, Maria Pia di Buono, Abbas Akkasi, Jan {\v{S}}najder |
| Abstract | We describe two systems for semantic relation classification with which we participated in the SemEval 2018 Task 7, subtask 1 on semantic relation classification: an SVM model and a CNN model. Both models combine dense pretrained word2vec features and hancrafted sparse features. For training the models, we combine the two datasets provided for the subtasks in order to balance the under-represented classes. The SVM model performed better than CNN, achieving a F1-macro score of 69.98{%} on subtask 1.1 and 75.69{%} on subtask 1.2. The system ranked 7th on among 28 submissions on subtask 1.1 and 7th among 20 submissions on subtask 1.2. |
| Tasks | Relation Classification, Relation Extraction |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1135/ |
| https://www.aclweb.org/anthology/S18-1135 | |
| PWC | https://paperswithcode.com/paper/takelab-at-semeval-2018-task-7-combining |
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NTNU at SemEval-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific Papers
| Title | NTNU at SemEval-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific Papers |
| Authors | Biswanath Barik, Utpal Kumar Sikdar, Bj{"o}rn Gamb{"a}ck |
| Abstract | The paper presents NTNU{'}s contribution to SemEval-2018 Task 7 on relation identification and classification. The class weights and parameters of five alternative supervised classifiers were optimized through grid search and cross-validation. The outputs of the classifiers were combined through voting for the final prediction. A wide variety of features were explored, with the most informative identified by feature selection. The best setting achieved F1 scores of 47.4{%} and 66.0{%} in the relation classification subtasks 1.1 and 1.2. For relation identification and classification in subtask 2, it achieved F1 scores of 33.9{%} and 17.0{%}, |
| Tasks | Feature Selection, Relation Classification, Relation Extraction |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1138/ |
| https://www.aclweb.org/anthology/S18-1138 | |
| PWC | https://paperswithcode.com/paper/ntnu-at-semeval-2018-task-7-classifier |
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Topic Intrusion for Automatic Topic Model Evaluation
| Title | Topic Intrusion for Automatic Topic Model Evaluation |
| Authors | Shraey Bhatia, Jey Han Lau, Timothy Baldwin |
| Abstract | Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. Topic coherence measures how well topic words relate to each other, but offers little insight on the utility of the topics in describing the documents. In this paper, we explore the topic intrusion task {—} the task of guessing an outlier topic given a document and a few topics {—} and propose a method to automate it. We improve upon the state-of-the-art substantially, demonstrating its viability as an alternative method for topic model evaluation. |
| Tasks | Information Retrieval, Topic Models |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1098/ |
| https://www.aclweb.org/anthology/D18-1098 | |
| PWC | https://paperswithcode.com/paper/topic-intrusion-for-automatic-topic-model |
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Rule- and Learning-based Methods for Bridging Resolution in the ARRAU Corpus
| Title | Rule- and Learning-based Methods for Bridging Resolution in the ARRAU Corpus |
| Authors | Ina Roesiger |
| Abstract | We present two systems for bridging resolution, which we submitted to the CRAC shared task on bridging anaphora resolution in the ARRAU corpus (track 2): a rule-based approach following Hou et al. 2014 and a learning-based approach. The re-implementation of Hou et al. 2014 achieves very poor performance when being applied to ARRAU. We found that the reasons for this lie in the different bridging annotations: whereas the rule-based system suggests many referential bridging pairs, ARRAU contains mostly lexical bridging. We describe the differences between these two types of bridging and adapt the rule-based approach to be able to handle lexical bridging. The modified rule-based approach achieves reasonable performance on all (sub)-tasks and outperforms a simple learning-based approach. |
| Tasks | Bridging Anaphora Resolution |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-0703/ |
| https://www.aclweb.org/anthology/W18-0703 | |
| PWC | https://paperswithcode.com/paper/rule-and-learning-based-methods-for-bridging |
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Bandit Learning in Concave N-Person Games
| Title | Bandit Learning in Concave N-Person Games |
| Authors | Mario Bravo, David Leslie, Panayotis Mertikopoulos |
| Abstract | This paper examines the long-run behavior of learning with bandit feedback in non-cooperative concave games. The bandit framework accounts for extremely low-information environments where the agents may not even know they are playing a game; as such, the agents’ most sensible choice in this setting would be to employ a no-regret learning algorithm. In general, this does not mean that the players’ behavior stabilizes in the long run: no-regret learning may lead to cycles, even with perfect gradient information. However, if a standard monotonicity condition is satisfied, our analysis shows that no-regret learning based on mirror descent with bandit feedback converges to Nash equilibrium with probability 1. We also derive an upper bound for the convergence rate of the process that nearly matches the best attainable rate for single-agent bandit stochastic optimization. |
| Tasks | Stochastic Optimization |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7809-bandit-learning-in-concave-n-person-games |
| http://papers.nips.cc/paper/7809-bandit-learning-in-concave-n-person-games.pdf | |
| PWC | https://paperswithcode.com/paper/bandit-learning-in-concave-n-person-games-1 |
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PoseFlow: A Deep Motion Representation for Understanding Human Behaviors in Videos
| Title | PoseFlow: A Deep Motion Representation for Understanding Human Behaviors in Videos |
| Authors | Dingwen Zhang, Guangyu Guo, Dong Huang, Junwei Han |
| Abstract | Motion of the human body is the critical cue for understanding and characterizing human behavior in videos. Most existing approaches explore the motion cue using optical flows. However, optical flow usually contains motion on both the interested human bodies and the undesired background. This “noisy” motion representation makes it very challenging for pose estimation and action recognition in real scenarios. To address this issue, this paper presents a novel deep motion representation, called PoseFlow, which reveals human motion in videos while suppressing background and motion blur, and being robust to occlusion. For learning PoseFlow with mild computational cost, we propose a functionally structured spatial-temporal deep network, PoseFlow Net (PFN), to jointly solve the skeleton localization and matching problems of PoseFlow. Comprehensive experiments show that PFN outperforms the state-of-the-art deep flow estimation models in generating PoseFlow. Moreover, PoseFlow demonstrates its potential on improving two challenging tasks in human video analysis: pose estimation and action recognition. |
| Tasks | Optical Flow Estimation, Pose Estimation, Pose Tracking, Temporal Action Localization |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_PoseFlow_A_Deep_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_PoseFlow_A_Deep_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/poseflow-a-deep-motion-representation-for |
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Corpora with Part-of-Speech Annotations for Three Regional Languages of France: Alsatian, Occitan and Picard
| Title | Corpora with Part-of-Speech Annotations for Three Regional Languages of France: Alsatian, Occitan and Picard |
| Authors | Delphine Bernhard, Anne-Laure Ligozat, Fanny Martin, Myriam Bras, Pierre Magistry, Marianne Vergez-Couret, Lucie Steibl{'e}, Pascale Erhart, Nabil Hathout, Dominique Huck, Christophe Rey, Philippe Reyn{'e}s, Sophie Rosset, Jean Sibille, Thomas Lavergne |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1619/ |
| https://www.aclweb.org/anthology/L18-1619 | |
| PWC | https://paperswithcode.com/paper/corpora-with-part-of-speech-annotations-for |
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Multiple Source Domain Adaptation with Adversarial Learning
| Title | Multiple Source Domain Adaptation with Adversarial Learning |
| Authors | Han Zhao, Shanghang Zhang, Guanhang Wu, Jo~{a}o P. Costeira, Jos'{e} M. F. Moura, Geoffrey J. Gordon |
| Abstract | While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting. Naive application of such algorithms on multiple source domain adaptation problem may lead to suboptimal solutions. We propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances. Compared with existing bounds, the new bound does not require expert knowledge about the target distribution, nor the optimal combination rule for multisource domains. Interestingly, our theory also leads to an efficient learning strategy using adversarial neural networks: we show how to interpret it as learning feature representations that are invariant to the multiple domain shifts while still being discriminative for the learning task. To this end, we propose two models, both of which we call multisource domain adversarial networks (MDANs): the first model optimizes directly our bound, while the second model is a smoothed approximation of the first one, leading to a more data-efficient and task-adaptive model. The optimization tasks of both models are minimax saddle point problems that can be optimized by adversarial training. To demonstrate the effectiveness of MDANs, we conduct extensive experiments showing superior adaptation performance on three real-world datasets: sentiment analysis, digit classification, and vehicle counting. |
| Tasks | Domain Adaptation, Sentiment Analysis |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=ryDNZZZAW |
| https://openreview.net/pdf?id=ryDNZZZAW | |
| PWC | https://paperswithcode.com/paper/multiple-source-domain-adaptation-with-1 |
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Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting
| Title | Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting |
| Authors | Dirk Hovy, Christoph Purschke |
| Abstract | Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. In most languages, dialects exist along a continuum, and are commonly discretized by combining the extent of several preselected linguistic variables. However, the selection of these variables is theory-driven and itself insensitive to change. We use Doc2Vec on a corpus of 16.8M anonymous online posts in the German-speaking area to learn continuous document representations of cities. These representations capture continuous regional linguistic distinctions, and can serve as input to downstream NLP tasks sensitive to regional variation. By incorporating geographic information via retrofitting and agglomerative clustering with structure, we recover dialect areas at various levels of granularity. Evaluating these clusters against an existing dialect map, we achieve a match of up to 0.77 V-score (harmonic mean of cluster completeness and homogeneity). Our results show that representation learning with retrofitting offers a robust general method to automatically expose dialectal differences and regional variation at a finer granularity than was previously possible. |
| Tasks | Dimensionality Reduction, Machine Translation, Representation Learning |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1469/ |
| https://www.aclweb.org/anthology/D18-1469 | |
| PWC | https://paperswithcode.com/paper/capturing-regional-variation-with-distributed |
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Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models
| Title | Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models |
| Authors | Mayank Kulkarni, Kristy Boyer |
| Abstract | There has been an increase in popularity of data-driven question answering systems given their recent success. This pa-per explores the possibility of building a tutorial question answering system for Java programming from data sampled from a community-based question answering forum. This paper reports on the creation of a dataset that could support building such a tutorial question answering system and discusses the methodology to create the 106,386 question strong dataset. We investigate how retrieval-based and generative models perform on the given dataset. The work also investigates the usefulness of using hybrid approaches such as combining retrieval-based and generative models. The results indicate that building data-driven tutorial systems using community-based question answering forums holds significant promise. |
| Tasks | Information Retrieval, Question Answering, Semantic Parsing, Transfer Learning |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-0532/ |
| https://www.aclweb.org/anthology/W18-0532 | |
| PWC | https://paperswithcode.com/paper/toward-data-driven-tutorial-question |
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E-magyar – A Digital Language Processing System
| Title | E-magyar – A Digital Language Processing System |
| Authors | Tam{'a}s V{'a}radi, Eszter Simon, B{'a}lint Sass, Iv{'a}n Mittelholcz, Attila Nov{'a}k, Bal{'a}zs Indig, Rich{'a}rd Farkas, Veronika Vincze |
| Abstract | |
| Tasks | Named Entity Recognition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1208/ |
| https://www.aclweb.org/anthology/L18-1208 | |
| PWC | https://paperswithcode.com/paper/e-magyar-a-digital-language-processing-system |
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