Paper Group NANR 40
Comparing Computational Cognitive Models of Generalization in a Language Acquisition Task. Automatic Classification by Topic Domain for Meta Data Generation, Web Corpus Evaluation, and Corpus Comparison. DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity. Using Syntactic and Semantic Context to Explo …
Comparing Computational Cognitive Models of Generalization in a Language Acquisition Task
| Title | Comparing Computational Cognitive Models of Generalization in a Language Acquisition Task |
| Authors | Libby Barak, Adele E. Goldberg, Suzanne Stevenson |
| Abstract | |
| Tasks | Language Acquisition |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1010/ |
| https://www.aclweb.org/anthology/D16-1010 | |
| PWC | https://paperswithcode.com/paper/comparing-computational-cognitive-models-of |
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Automatic Classification by Topic Domain for Meta Data Generation, Web Corpus Evaluation, and Corpus Comparison
| Title | Automatic Classification by Topic Domain for Meta Data Generation, Web Corpus Evaluation, and Corpus Comparison |
| Authors | Rol Sch{"a}fer, , Felix Bildhauer |
| Abstract | |
| Tasks | Text Categorization |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2601/ |
| https://www.aclweb.org/anthology/W16-2601 | |
| PWC | https://paperswithcode.com/paper/automatic-classification-by-topic-domain-for |
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DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity
| Title | DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity |
| Authors | Chris Hokamp, Piyush Arora |
| Abstract | |
| Tasks | Machine Translation, Semantic Textual Similarity, Sentence Embeddings |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1100/ |
| https://www.aclweb.org/anthology/S16-1100 | |
| PWC | https://paperswithcode.com/paper/dcu-semaniacs-at-semeval-2016-task-1 |
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Using Syntactic and Semantic Context to Explore Psychodemographic Differences in Self-reference
| Title | Using Syntactic and Semantic Context to Explore Psychodemographic Differences in Self-reference |
| Authors | Masoud Rouhizadeh, Lyle Ungar, Anneke Buffone, H Andrew Schwartz |
| Abstract | |
| Tasks | |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1219/ |
| https://www.aclweb.org/anthology/D16-1219 | |
| PWC | https://paperswithcode.com/paper/using-syntactic-and-semantic-context-to |
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Assessing the Prosody of Non-Native Speakers of English: Measures and Feature Sets
| Title | Assessing the Prosody of Non-Native Speakers of English: Measures and Feature Sets |
| Authors | Eduardo Coutinho, Florian H{"o}nig, Yue Zhang, Simone Hantke, Anton Batliner, Elmar N{"o}th, Bj{"o}rn Schuller |
| Abstract | In this paper, we describe a new database with audio recordings of non-native (L2) speakers of English, and the perceptual evaluation experiment conducted with native English speakers for assessing the prosody of each recording. These annotations are then used to compute the gold standard using different methods, and a series of regression experiments is conducted to evaluate their impact on the performance of a regression model predicting the degree of naturalness of L2 speech. Further, we compare the relevance of different feature groups modelling prosody in general (without speech tempo), speech rate and pauses modelling speech tempo (fluency), voice quality, and a variety of spectral features. We also discuss the impact of various fusion strategies on performance.Overall, our results demonstrate that the prosody of non-native speakers of English as L2 can be reliably assessed using supra-segmental audio features; prosodic features seem to be the most important ones. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1211/ |
| https://www.aclweb.org/anthology/L16-1211 | |
| PWC | https://paperswithcode.com/paper/assessing-the-prosody-of-non-native-speakers |
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Discriminative Reranking for Grammatical Error Correction with Statistical Machine Translation
| Title | Discriminative Reranking for Grammatical Error Correction with Statistical Machine Translation |
| Authors | Tomoya Mizumoto, Yuji Matsumoto |
| Abstract | |
| Tasks | Grammatical Error Correction, Machine Translation |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1133/ |
| https://www.aclweb.org/anthology/N16-1133 | |
| PWC | https://paperswithcode.com/paper/discriminative-reranking-for-grammatical |
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The FBK Participation in the WMT 2016 Automatic Post-editing Shared Task
| Title | The FBK Participation in the WMT 2016 Automatic Post-editing Shared Task |
| Authors | Rajen Chatterjee, Jos{'e} G. C. de Souza, Matteo Negri, Marco Turchi |
| Abstract | |
| Tasks | Automatic Post-Editing, Data Augmentation, Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2377/ |
| https://www.aclweb.org/anthology/W16-2377 | |
| PWC | https://paperswithcode.com/paper/the-fbk-participation-in-the-wmt-2016 |
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Automatic Labeling of Topic Models Using Text Summaries
| Title | Automatic Labeling of Topic Models Using Text Summaries |
| Authors | Xiaojun Wan, Tianming Wang |
| Abstract | |
| Tasks | Information Retrieval, Topic Models |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1217/ |
| https://www.aclweb.org/anthology/P16-1217 | |
| PWC | https://paperswithcode.com/paper/automatic-labeling-of-topic-models-using-text |
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ICL-HD at SemEval-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word Embeddings
| Title | ICL-HD at SemEval-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word Embeddings |
| Authors | Angelika Kirilin, Felix Krauss, Yannick Versley |
| Abstract | |
| Tasks | Named Entity Recognition, Word Embeddings, Word Sense Disambiguation |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1144/ |
| https://www.aclweb.org/anthology/S16-1144 | |
| PWC | https://paperswithcode.com/paper/icl-hd-at-semeval-2016-task-10-improving-the |
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A House United: Bridging the Script and Lexical Barrier between Hindi and Urdu
| Title | A House United: Bridging the Script and Lexical Barrier between Hindi and Urdu |
| Authors | Riyaz A. Bhat, Irshad A. Bhat, Naman Jain, Dipti Misra Sharma |
| Abstract | In Computational Linguistics, Hindi and Urdu are not viewed as a monolithic entity and have received separate attention with respect to their text processing. From part-of-speech tagging to machine translation, models are separately trained for both Hindi and Urdu despite the fact that they represent the same language. The reasons mainly are their divergent literary vocabularies and separate orthographies, and probably also their political status and the social perception that they are two separate languages. In this article, we propose a simple but efficient approach to bridge the lexical and orthographic differences between Hindi and Urdu texts. With respect to text processing, addressing the differences between the Hindi and Urdu texts would be beneficial in the following ways: (a) instead of training separate models, their individual resources can be augmented to train single, unified models for better generalization, and (b) their individual text processing applications can be used interchangeably under varied resource conditions. To remove the script barrier, we learn accurate statistical transliteration models which use sentence-level decoding to resolve word ambiguity. Similarly, we learn cross-register word embeddings from the harmonized Hindi and Urdu corpora to nullify their lexical divergences. As a proof of the concept, we evaluate our approach on the Hindi and Urdu dependency parsing under two scenarios: (a) resource sharing, and (b) resource augmentation. We demonstrate that a neural network-based dependency parser trained on augmented, harmonized Hindi and Urdu resources performs significantly better than the parsing models trained separately on the individual resources. We also show that we can achieve near state-of-the-art results when the parsers are used interchangeably. |
| Tasks | Dependency Parsing, Machine Translation, Part-Of-Speech Tagging, Transliteration, Word Embeddings |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1039/ |
| https://www.aclweb.org/anthology/C16-1039 | |
| PWC | https://paperswithcode.com/paper/a-house-united-bridging-the-script-and |
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Knowledge-Guided Linguistic Rewrites for Inference Rule Verification
| Title | Knowledge-Guided Linguistic Rewrites for Inference Rule Verification |
| Authors | Prachi Jain, {Mausam} |
| Abstract | |
| Tasks | Natural Language Inference, Open Information Extraction, Reading Comprehension |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1011/ |
| https://www.aclweb.org/anthology/N16-1011 | |
| PWC | https://paperswithcode.com/paper/knowledge-guided-linguistic-rewrites-for |
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Embedding Open-domain Common-sense Knowledge from Text
| Title | Embedding Open-domain Common-sense Knowledge from Text |
| Authors | Travis Goodwin, S Harabagiu, a |
| Abstract | Our ability to understand language often relies on common-sense knowledge ― background information the speaker can assume is known by the reader. Similarly, our comprehension of the language used in complex domains relies on access to domain-specific knowledge. Capturing common-sense and domain-specific knowledge can be achieved by taking advantage of recent advances in open information extraction (IE) techniques and, more importantly, of knowledge embeddings, which are multi-dimensional representations of concepts and relations. Building a knowledge graph for representing common-sense knowledge in which concepts discerned from noun phrases are cast as vertices and lexicalized relations are cast as edges leads to learning the embeddings of common-sense knowledge accounting for semantic compositionality as well as implied knowledge. Common-sense knowledge is acquired from a vast collection of blogs and books as well as from WordNet. Similarly, medical knowledge is learned from two large sets of electronic health records. The evaluation results of these two forms of knowledge are promising: the same knowledge acquisition methodology based on learning knowledge embeddings works well both for common-sense knowledge and for medical knowledge Interestingly, the common-sense knowledge that we have acquired was evaluated as being less neutral than than the medical knowledge, as it often reflected the opinion of the knowledge utterer. In addition, the acquired medical knowledge was evaluated as more plausible than the common-sense knowledge, reflecting the complexity of acquiring common-sense knowledge due to the pragmatics and economicity of language. |
| Tasks | Common Sense Reasoning, Open Information Extraction |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1732/ |
| https://www.aclweb.org/anthology/L16-1732 | |
| PWC | https://paperswithcode.com/paper/embedding-open-domain-common-sense-knowledge |
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Focal Prominence Underlying Distribution of Mandarin Minimizers
| Title | Focal Prominence Underlying Distribution of Mandarin Minimizers |
| Authors | I-Hsuan Chen |
| Abstract | |
| Tasks | |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/Y16-2017/ |
| https://www.aclweb.org/anthology/Y16-2017 | |
| PWC | https://paperswithcode.com/paper/focal-prominence-underlying-distribution-of |
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Measuring Diversified Proficiency of Japanese Learners of English
| Title | Measuring Diversified Proficiency of Japanese Learners of English |
| Authors | Yasunari Harada |
| Abstract | |
| Tasks | Speech Recognition |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/Y16-1005/ |
| https://www.aclweb.org/anthology/Y16-1005 | |
| PWC | https://paperswithcode.com/paper/measuring-diversified-proficiency-of-japanese |
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The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM
| Title | The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM |
| Authors | Damek Davis, Brent Edmunds, Madeleine Udell |
| Abstract | We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex, nonsmooth optimization problems. SAPALM is the first asynchronous parallel optimization method that provably converges on a large class of nonconvex, nonsmooth problems. We prove that SAPALM matches the best known rates of convergence — among synchronous or asynchronous methods — on this problem class. We provide upper bounds on the number of workers for which we can expect to see a linear speedup, which match the best bounds known for less complex problems, and show that in practice SAPALM achieves this linear speedup. We demonstrate state-of-the-art performance on several matrix factorization problems. |
| Tasks | |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6428-the-sound-of-apalm-clapping-faster-nonsmooth-nonconvex-optimization-with-stochastic-asynchronous-palm |
| http://papers.nips.cc/paper/6428-the-sound-of-apalm-clapping-faster-nonsmooth-nonconvex-optimization-with-stochastic-asynchronous-palm.pdf | |
| PWC | https://paperswithcode.com/paper/the-sound-of-apalm-clapping-faster-nonsmooth |
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