Paper Group NANR 1
Noise-Tolerant Interactive Learning Using Pairwise Comparisons. HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment. Proceedings of ACL 2017, Student Research Workshop. Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization. Wild Devs’ at SemEval-2017 Task 2: Using Neural N …
Noise-Tolerant Interactive Learning Using Pairwise Comparisons
| Title | Noise-Tolerant Interactive Learning Using Pairwise Comparisons |
| Authors | Yichong Xu, Hongyang Zhang, Kyle Miller, Aarti Singh, Artur Dubrawski |
| Abstract | We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive. Learning from such oracles has multiple applications where obtaining direct labels is harder but pairwise comparisons are easier, and the algorithm can leverage both types of oracles. In this paper, we attempt to characterize how the access to an easier comparison oracle helps in improving the label and total query complexity. We show that the comparison oracle reduces the learning problem to that of learning a threshold function. We then present an algorithm that interactively queries the label and comparison oracles and we characterize its query complexity under Tsybakov and adversarial noise conditions for the comparison and labeling oracles. Our lower bounds show that our label and total query complexity is almost optimal. |
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| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/6837-noise-tolerant-interactive-learning-using-pairwise-comparisons |
| http://papers.nips.cc/paper/6837-noise-tolerant-interactive-learning-using-pairwise-comparisons.pdf | |
| PWC | https://paperswithcode.com/paper/noise-tolerant-interactive-learning-using |
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HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment
| Title | HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment |
| Authors | Behrang QasemiZadeh, Laura Kallmeyer |
| Abstract | This paper describes the HHU system that participated in Task 2 of SemEval 2017, Multilingual and Cross-lingual Semantic Word Similarity. We introduce our unsupervised embedding learning technique and describe how it was employed and configured to address the problems of monolingual and multilingual word similarity measurement. This paper reports from empirical evaluations on the benchmark provided by the task{'}s organizers. |
| Tasks | Learning Word Embeddings, Semantic Textual Similarity, Word Embeddings |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2039/ |
| https://www.aclweb.org/anthology/S17-2039 | |
| PWC | https://paperswithcode.com/paper/hhu-at-semeval-2017-task-2-fast-hash-based |
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Proceedings of ACL 2017, Student Research Workshop
| Title | Proceedings of ACL 2017, Student Research Workshop |
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| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/P17-3000/ |
| https://www.aclweb.org/anthology/P17-3000 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-acl-2017-student-research |
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Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization
| Title | Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization |
| Authors | Yossi Arjevani |
| Abstract | We study the conditions under which one is able to efficiently apply variance-reduction and acceleration schemes on finite sums problems. First, we show that perhaps surprisingly, the finite sum structure, by itself, is not sufficient for obtaining a complexity bound of $\tilde{\cO}((n+L/\mu)\ln(1/\epsilon))$ for $L$-smooth and $\mu$-strongly convex finite sums - one must also know exactly which individual function is being referred to by the oracle at each iteration. Next, we show that for a broad class of first-order and coordinate-descent finite sums algorithms (including, e.g., SDCA, SVRG, SAG), it is not possible to get an `accelerated’ complexity bound of $\tilde{\cO}((n+\sqrt{n L/\mu})\ln(1/\epsilon))$, unless the strong convexity parameter is given explicitly. Lastly, we show that when this class of algorithms is used for minimizing $L$-smooth and non-strongly convex finite sums, the optimal complexity bound is $\tilde{\cO}(n+L/\epsilon)$, assuming that (on average) the same update rule is used for any iteration, and $\tilde{\cO}(n+\sqrt{nL/\epsilon})$, otherwise. | |
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| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/6945-limitations-on-variance-reduction-and-acceleration-schemes-for-finite-sums-optimization |
| http://papers.nips.cc/paper/6945-limitations-on-variance-reduction-and-acceleration-schemes-for-finite-sums-optimization.pdf | |
| PWC | https://paperswithcode.com/paper/limitations-on-variance-reduction-and-1 |
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Wild Devs’ at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity
| Title | Wild Devs’ at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity |
| Authors | R{\u{a}}zvan-Gabriel Rotari, Ionu{\textcommabelow{t}} Hulub, {\textcommabelow{S}}tefan Oprea, Mihaela Pl{\u{a}}mad{\u{a}}-Onofrei, Alina Beatrice Loren{\c{t}}, Raluca Preisler, Adrian Iftene, Tr, Diana ab{\u{a}}{\textcommabelow{t}} |
| Abstract | This paper presents Wild Devs{'} participation in the SemEval-2017 Task 2 {``}Multi-lingual and Cross-lingual Semantic Word Similarity{''}, which tries to automatically measure the semantic similarity between two words. The system was build using neural networks, having as input a collection of word pairs, whereas the output consists of a list of scores, from 0 to 4, corresponding to the degree of similarity between the word pairs. | |
| Tasks | Multilingual Word Embeddings, Semantic Similarity, Semantic Textual Similarity, Word Embeddings |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2042/ |
| https://www.aclweb.org/anthology/S17-2042 | |
| PWC | https://paperswithcode.com/paper/wild-devs-at-semeval-2017-task-2-using-neural |
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Reconstructing perceived faces from brain activations with deep adversarial neural decoding
| Title | Reconstructing perceived faces from brain activations with deep adversarial neural decoding |
| Authors | Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch, Rob Van Lier, Marcel A. J. Van Gerven |
| Abstract | Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning. Our approach first inverts the linear transformation from latent features to brain responses with maximum a posteriori estimation and then inverts the nonlinear transformation from perceived stimuli to latent features with adversarial training of convolutional neural networks. We test our approach with a functional magnetic resonance imaging experiment and show that it can generate state-of-the-art reconstructions of perceived faces from brain activations. |
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| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/7012-reconstructing-perceived-faces-from-brain-activations-with-deep-adversarial-neural-decoding |
| http://papers.nips.cc/paper/7012-reconstructing-perceived-faces-from-brain-activations-with-deep-adversarial-neural-decoding.pdf | |
| PWC | https://paperswithcode.com/paper/reconstructing-perceived-faces-from-brain |
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V for Vocab: An Intelligent Flashcard Application
| Title | V for Vocab: An Intelligent Flashcard Application |
| Authors | Nihal V. Nayak, Tanmay Chinchore, Aishwarya Hanumanth Rao, Shane Michael Martin, Sagar Nagaraj Simha, G. M. Lingaraju, H. S. Jamadagni |
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| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/P17-3005/ |
| https://www.aclweb.org/anthology/P17-3005 | |
| PWC | https://paperswithcode.com/paper/v-for-vocab-an-intelligent-flashcard |
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SoccEval: An Annotation Schema for Rating Soccer Players
| Title | SoccEval: An Annotation Schema for Rating Soccer Players |
| Authors | Jose Ramirez, Matthew Garber, Xinhao Wang |
| Abstract | |
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| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/P17-3015/ |
| https://www.aclweb.org/anthology/P17-3015 | |
| PWC | https://paperswithcode.com/paper/socceval-an-annotation-schema-for-rating |
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A Multiform Balanced Dependency Treebank for Romanian
| Title | A Multiform Balanced Dependency Treebank for Romanian |
| Authors | Mihaela Colhon, C{\u{a}}t{\u{a}}lina M{\u{a}}r{\u{a}}nduc, C{\u{a}}t{\u{a}}lin Mititelu |
| Abstract | The UAIC-RoDia-DepTb is a balanced treebank, containing texts in non-standard language: 2,575 chats sentences, old Romanian texts (a Gospel printed in 1648, a codex of laws printed in 1818, a novel written in 1910), regional popular poetry, legal texts, Romanian and foreign fiction, quotations. The proportions are comparable; each of these types of texts is represented by subsets of at least 1,000 phrases, so that the parser can be trained on their peculiarities. The annotation of the treebank started in 2007, and it has classical tags, such as those in school grammar, with the intention of using the resource for didactic purposes. The classification of circumstantial modifiers is rich in semantic information. We present in this paper the development in progress of this resource which has been automatically annotated and entirely manually corrected. We try to add new texts, and to make it available in more formats, by keeping all the morphological and syntactic information annotated, and adding logical-semantic information. We will describe here two conversions, from the classic syntactic format into Universal Dependencies format and into a logical-semantic layer, which will be shortly presented. |
| Tasks | Machine Translation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-7802/ |
| https://doi.org/10.26615/978-954-452-040-3_002 | |
| PWC | https://paperswithcode.com/paper/a-multiform-balanced-dependency-treebank-for |
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Finding Structure in Figurative Language: Metaphor Detection with Topic-based Frames
| Title | Finding Structure in Figurative Language: Metaphor Detection with Topic-based Frames |
| Authors | Hyeju Jang, Keith Maki, Eduard Hovy, Carolyn Ros{'e} |
| Abstract | In this paper, we present a novel and highly effective method for induction and application of metaphor frame templates as a step toward detecting metaphor in extended discourse. We infer implicit facets of a given metaphor frame using a semi-supervised bootstrapping approach on an unlabeled corpus. Our model applies this frame facet information to metaphor detection, and achieves the state-of-the-art performance on a social media dataset when building upon other proven features in a nonlinear machine learning model. In addition, we illustrate the mechanism through which the frame and topic information enable the more accurate metaphor detection. |
| Tasks | Machine Translation |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-5538/ |
| https://www.aclweb.org/anthology/W17-5538 | |
| PWC | https://paperswithcode.com/paper/finding-structure-in-figurative-language |
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A Game with a Purpose for Automatic Detection of Children’s Speech Disabilities using Limited Speech Resources
| Title | A Game with a Purpose for Automatic Detection of Children’s Speech Disabilities using Limited Speech Resources |
| Authors | Reem Salem, Mohamed Elmahdy, Slim Abdennadher, Injy Hamed |
| Abstract | Speech therapists and researchers are becoming more concerned with the use of computer-based systems in the therapy of speech disorders. In this paper, we propose a computer-based game with a purpose (GWAP) for speech therapy of Egyptian speaking children suffering from Dyslalia. Our aim is to detect if a certain phoneme is pronounced correctly. An Egyptian Arabic speech corpus has been collected. A baseline acoustic model was trained using the Egyptian corpus. In order to benefit from existing large amounts of Modern Standard Arabic (MSA) resources, MSA acoustic models were adapted with the collected Egyptian corpus. An independent testing set that covers common speech disorders has been collected for Egyptian speakers. Results show that adapted acoustic models give better recognition accuracy which could be relied on in the game and that children show more interest in playing the game than in visiting the therapist. A noticeable progress in children Dyslalia appeared with the proposed system. |
| Tasks | Information Retrieval |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-7704/ |
| https://doi.org/10.26615/978-954-452-038-0_004 | |
| PWC | https://paperswithcode.com/paper/a-game-with-a-purpose-for-automatic-detection |
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UPC-USMBA at SemEval-2017 Task 3: Combining multiple approaches for CQA for Arabic
| Title | UPC-USMBA at SemEval-2017 Task 3: Combining multiple approaches for CQA for Arabic |
| Authors | Yassine El Adlouni, Imane Lahbari, Horacio Rodr{'\i}guez, Mohammed Meknassi, Said Ouatik El Alaoui, Noureddine Ennahnahi |
| Abstract | This paper presents a description of the participation of the UPC-USMBA team in the SemEval 2017 Task 3, subtask D, Arabic. Our approach for facing the task is based on a combination of a set of atomic classifiers. The atomic classifiers include lexical string based, based on vectorial representations and rulebased. Several combination approaches have been tried. |
| Tasks | Question Answering |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2044/ |
| https://www.aclweb.org/anthology/S17-2044 | |
| PWC | https://paperswithcode.com/paper/upc-usmba-at-semeval-2017-task-3-combining |
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Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering
| Title | Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering |
| Authors | Wenzheng Feng, Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou |
| Abstract | This paper presents the system in SemEval-2017 Task 3, Community Question Answering (CQA). We develop a ranking system that is capable of capturing semantic relations between text pairs with little word overlap. In addition to traditional NLP features, we introduce several neural network based matching features which enable our system to measure text similarity beyond lexicons. Our system significantly outperforms baseline methods and holds the second place in Subtask A and the fifth place in Subtask B, which demonstrates its efficacy on answer selection and question retrieval. |
| Tasks | Answer Selection, Community Question Answering, Learning-To-Rank, Question Answering, Question Similarity |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2045/ |
| https://www.aclweb.org/anthology/S17-2045 | |
| PWC | https://paperswithcode.com/paper/beihang-msra-at-semeval-2017-task-3-a-ranking |
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Spell-Checking based on Syllabification and Character-level Graphs for a Peruvian Agglutinative Language
| Title | Spell-Checking based on Syllabification and Character-level Graphs for a Peruvian Agglutinative Language |
| Authors | Carlo Alva, Arturo Oncevay |
| Abstract | There are several native languages in Peru which are mostly agglutinative. These languages are transmitted from generation to generation mainly in oral form, causing different forms of writing across different communities. For this reason, there are recent efforts to standardize the spelling in the written texts, and it would be beneficial to support these tasks with an automatic tool such as an spell-checker. In this way, this spelling corrector is being developed based on two steps: an automatic rule-based syllabification method and a character-level graph to detect the degree of error in a misspelled word. The experiments were realized on Shipibo-konibo, a highly agglutinative and amazonian language, and the results obtained have been promising in a dataset built for the purpose. |
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| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4116/ |
| https://www.aclweb.org/anthology/W17-4116 | |
| PWC | https://paperswithcode.com/paper/spell-checking-based-on-syllabification-and |
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Improving Black-box Speech Recognition using Semantic Parsing
| Title | Improving Black-box Speech Recognition using Semantic Parsing |
| Authors | Rodolfo Corona, Jesse Thomason, Raymond Mooney |
| Abstract | Speech is a natural channel for human-computer interaction in robotics and consumer applications. Natural language understanding pipelines that start with speech can have trouble recovering from speech recognition errors. Black-box automatic speech recognition (ASR) systems, built for general purpose use, are unable to take advantage of in-domain language models that could otherwise ameliorate these errors. In this work, we present a method for re-ranking black-box ASR hypotheses using an in-domain language model and semantic parser trained for a particular task. Our re-ranking method significantly improves both transcription accuracy and semantic understanding over a state-of-the-art ASR{'}s vanilla output. |
| Tasks | Language Modelling, Semantic Parsing, Speech Recognition |
| Published | 2017-11-01 |
| URL | https://www.aclweb.org/anthology/I17-2021/ |
| https://www.aclweb.org/anthology/I17-2021 | |
| PWC | https://paperswithcode.com/paper/improving-black-box-speech-recognition-using |
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