Paper Group NANR 50
A Bayesian Nonparametric View on Count-Min Sketch. Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search. 300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes. Japanese Dialogue Corpus of Information Navigation and Attentive Listening Annotated with Extended ISO-24617-2 Dialogue Act Tags. …
A Bayesian Nonparametric View on Count-Min Sketch
| Title | A Bayesian Nonparametric View on Count-Min Sketch |
| Authors | Diana Cai, Michael Mitzenmacher, Ryan P. Adams |
| Abstract | The count-min sketch is a time- and memory-efficient randomized data structure that provides a point estimate of the number of times an item has appeared in a data stream. The count-min sketch and related hash-based data structures are ubiquitous in systems that must track frequencies of data such as URLs, IP addresses, and language n-grams. We present a Bayesian view on the count-min sketch, using the same data structure, but providing a posterior distribution over the frequencies that characterizes the uncertainty arising from the hash-based approximation. In particular, we take a nonparametric approach and consider tokens generated from a Dirichlet process (DP) random measure, which allows for an unbounded number of unique tokens. Using properties of the DP, we show that it is possible to straightforwardly compute posterior marginals of the unknown true counts and that the modes of these marginals recover the count-min sketch estimator, inheriting the associated probabilistic guarantees. Using simulated data with known ground truth, we investigate the properties of these estimators. Lastly, we also study a modified problem in which the observation stream consists of collections of tokens (i.e., documents) arising from a random measure drawn from a stable beta process, which allows for power law scaling behavior in the number of unique tokens. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/8093-a-bayesian-nonparametric-view-on-count-min-sketch |
| http://papers.nips.cc/paper/8093-a-bayesian-nonparametric-view-on-count-min-sketch.pdf | |
| PWC | https://paperswithcode.com/paper/a-bayesian-nonparametric-view-on-count-min |
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Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search
| Title | Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search |
| Authors | Jamie Kiros, William Chan, Geoffrey Hinton |
| Abstract | We introduce Picturebook, a large-scale lookup operation to ground language via {`}snapshots{'} of our physical world accessed through image search. For each word in a vocabulary, we extract the top-$k$ images from Google image search and feed the images through a convolutional network to extract a word embedding. We introduce a multimodal gating function to fuse our Picturebook embeddings with other word representations. We also introduce Inverse Picturebook, a mechanism to map a Picturebook embedding back into words. We experiment and report results across a wide range of tasks: word similarity, natural language inference, semantic relatedness, sentiment/topic classification, image-sentence ranking and machine translation. We also show that gate activations corresponding to Picturebook embeddings are highly correlated to human judgments of concreteness ratings. | |
| Tasks | Image Retrieval, Machine Translation, Natural Language Inference, Word Embeddings |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1085/ |
| https://www.aclweb.org/anthology/P18-1085 | |
| PWC | https://paperswithcode.com/paper/illustrative-language-understanding-large |
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300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes
| Title | 300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes |
| Authors | G{'a}bor Berend, M{'a}rton Makrai, P{'e}ter F{"o}ldi{'a}k |
| Abstract | This paper describes 300-sparsians{'}s participation in SemEval-2018 Task 9: Hypernym Discovery, with a system based on sparse coding and a formal concept hierarchy obtained from word embeddings. Our system took first place in subtasks (1B) Italian (all and entities), (1C) Spanish entities, and (2B) music entities. |
| Tasks | Hypernym Discovery, Word Embeddings |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1152/ |
| https://www.aclweb.org/anthology/S18-1152 | |
| PWC | https://paperswithcode.com/paper/300-sparsans-at-semeval-2018-task-9-hypernymy |
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Japanese Dialogue Corpus of Information Navigation and Attentive Listening Annotated with Extended ISO-24617-2 Dialogue Act Tags
| Title | Japanese Dialogue Corpus of Information Navigation and Attentive Listening Annotated with Extended ISO-24617-2 Dialogue Act Tags |
| Authors | Koichiro Yoshino, Hiroki Tanaka, Kyoshiro Sugiyama, Makoto Kondo, Satoshi Nakamura |
| Abstract | |
| Tasks | Slot Filling, Spoken Dialogue Systems, Task-Oriented Dialogue Systems |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1462/ |
| https://www.aclweb.org/anthology/L18-1462 | |
| PWC | https://paperswithcode.com/paper/japanese-dialogue-corpus-of-information |
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Proceedings of the 1st Workshop on Automatic Text Adaptation (ATA)
| Title | Proceedings of the 1st Workshop on Automatic Text Adaptation (ATA) |
| Authors | Arne J{"o}nsson, Evelina Rennes, Horacio Saggion, Sanja Stajner, Victoria Yaneva |
| Abstract | |
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| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/papers/W/W18/W18-7000/ |
| https://www.aclweb.org/anthology/W18-7000 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-1st-workshop-on-automatic |
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Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse
| Title | Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse |
| Authors | Sheng Xu, Peifeng Li, Guodong Zhou, Qiaoming Zhu |
| Abstract | The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information. In this paper, we propose a novel text matching network (TMN) that encodes the discourse units and the paragraphs by combining Bi-LSTM and CNN to capture both global dependency information and local n-gram information. Moreover, it introduces three components of text matching, the Cosine, Bilinear and Single Layer Network, to incorporate various similarities and interactions among the discourse units. Experimental results on the Chinese Discourse TreeBank show that our proposed TMN model significantly outperforms various strong baselines in both micro-F1 and macro-F1. |
| Tasks | Question Answering, Text Matching |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1044/ |
| https://www.aclweb.org/anthology/C18-1044 | |
| PWC | https://paperswithcode.com/paper/employing-text-matching-network-to-recognise |
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ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet
| Title | ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet |
| Authors | Rui Mao, Guanyi Chen, Ruizhe Li, Chenghua Lin |
| Abstract | This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word{'}s similarity with each of the two semantically similar words, based on an extended word embedding method and WordNet. Instead of computing the similarities between the attribute and semantically similar words by using standard word embeddings, we propose a novel method that combines word and context embeddings which can better measure similarities. Our model is simple and effective, which achieves an average F1 score of 0.62 on the test set. |
| Tasks | Semantic Textual Similarity, Word Embeddings |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1169/ |
| https://www.aclweb.org/anthology/S18-1169 | |
| PWC | https://paperswithcode.com/paper/abdn-at-semeval-2018-task-10-recognising |
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YNU_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension
| Title | YNU_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension |
| Authors | Peng Ding, Xiaobing Zhou |
| Abstract | We firstly use GloVe to learn the distributed representations automatically from the instance, question and answer triples. Then an attentionbased Bidirectional LSTM (BiLSTM) model is used to encode the triples. We also perform a simple ensemble method to improve the effectiveness of our model. The system we developed obtains an encouraging result on this task. It achieves the accuracy 0.7472 on the test set. We rank 5th according to the official ranking. |
| Tasks | Machine Translation, Reading Comprehension, Text Summarization |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1174/ |
| https://www.aclweb.org/anthology/S18-1174 | |
| PWC | https://paperswithcode.com/paper/ynu_deep-at-semeval-2018-task-11-an-ensemble |
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ITNLP-ARC at SemEval-2018 Task 12: Argument Reasoning Comprehension with Attention
| Title | ITNLP-ARC at SemEval-2018 Task 12: Argument Reasoning Comprehension with Attention |
| Authors | Wenjie Liu, Chengjie Sun, Lei Lin, Bingquan Liu |
| Abstract | Reasoning is a very important topic and has many important applications in the field of natural language processing. Semantic Evaluation (SemEval) 2018 Task 12 {``}The Argument Reasoning Comprehension{''} committed to research natural language reasoning. In this task, we proposed a novel argument reasoning comprehension system, ITNLP-ARC, which use Neural Networks technology to solve this problem. In our system, the LSTM model is involved to encode both the premise sentences and the warrant sentences. The attention model is used to merge the two premise sentence vectors. Through comparing the similarity between the attention vector and each of the two warrant vectors, we choose the one with higher similarity as our system{'}s final answer. | |
| Tasks | Information Retrieval, Machine Translation, Natural Language Inference, Relation Extraction, Semantic Textual Similarity, Text Summarization |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1183/ |
| https://www.aclweb.org/anthology/S18-1183 | |
| PWC | https://paperswithcode.com/paper/itnlp-arc-at-semeval-2018-task-12-argument |
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Semantic Parsing for Technical Support Questions
| Title | Semantic Parsing for Technical Support Questions |
| Authors | Abhirut Gupta, Anupama Ray, Gargi Dasgupta, Gautam Singh, Pooja Aggarwal, Prateeti Mohapatra |
| Abstract | Technical support problems are very complex. In contrast to regular web queries (that contain few keywords) or factoid questions (which are a few sentences), these problems usually include attributes like a detailed description of what is failing (symptom), steps taken in an effort to remediate the failure (activity), and sometimes a specific request or ask (intent). Automating support is the task of automatically providing answers to these problems given a corpus of solution documents. Traditional approaches to this task rely on information retrieval and are keyword based; looking for keyword overlap between the question and solution documents and ignoring these attributes. We present an approach for semantic parsing of technical questions that uses grammatical structure to extract these attributes as a baseline, and a CRF based model that can improve performance considerably in the presence of annotated data for training. We also demonstrate that combined with reasoning, these attributes help outperform retrieval baselines. |
| Tasks | Information Retrieval, Semantic Parsing |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1275/ |
| https://www.aclweb.org/anthology/C18-1275 | |
| PWC | https://paperswithcode.com/paper/semantic-parsing-for-technical-support |
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Matics Software Suite: New Tools for Evaluation and Data Exploration
| Title | Matics Software Suite: New Tools for Evaluation and Data Exploration |
| Authors | Olivier Galibert, Guillaume Bernard, Agnes Delaborde, Sabrina Lecadre, Juliette Kahn |
| Abstract | |
| Tasks | Optical Character Recognition, Speaker Diarization, Speaker Identification, Speech Recognition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1319/ |
| https://www.aclweb.org/anthology/L18-1319 | |
| PWC | https://paperswithcode.com/paper/matics-software-suite-new-tools-for |
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Frequency-Domain Dynamic Pruning for Convolutional Neural Networks
| Title | Frequency-Domain Dynamic Pruning for Convolutional Neural Networks |
| Authors | Zhenhua Liu, Jizheng Xu, Xiulian Peng, Ruiqin Xiong |
| Abstract | Deep convolutional neural networks have demonstrated their powerfulness in a variety of applications. However, the storage and computational requirements have largely restricted their further extensions on mobile devices. Recently, pruning of unimportant parameters has been used for both network compression and acceleration. Considering that there are spatial redundancy within most filters in a CNN, we propose a frequency-domain dynamic pruning scheme to exploit the spatial correlations. The frequency-domain coefficients are pruned dynamically in each iteration and different frequency bands are pruned discriminatively, given their different importance on accuracy. Experimental results demonstrate that the proposed scheme can outperform previous spatial-domain counterparts by a large margin. Specifically, it can achieve a compression ratio of 8.4x and a theoretical inference speed-up of 9.2x for ResNet-110, while the accuracy is even better than the reference model on CIFAR-110. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7382-frequency-domain-dynamic-pruning-for-convolutional-neural-networks |
| http://papers.nips.cc/paper/7382-frequency-domain-dynamic-pruning-for-convolutional-neural-networks.pdf | |
| PWC | https://paperswithcode.com/paper/frequency-domain-dynamic-pruning-for |
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MirasVoice: A bilingual (English-Persian) speech corpus
| Title | MirasVoice: A bilingual (English-Persian) speech corpus |
| Authors | Amir Vaheb, Ali Janalizadeh Choobbasti, S.H.E. Mortazavi Najafabadi, Saeid Safavi, Behnam Sabeti |
| Abstract | |
| Tasks | Speaker Recognition, Speaker Verification, Speech Recognition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1459/ |
| https://www.aclweb.org/anthology/L18-1459 | |
| PWC | https://paperswithcode.com/paper/mirasvoice-a-bilingual-english-persian-speech |
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Content-Sensitive Supervoxels via Uniform Tessellations on Video Manifolds
| Title | Content-Sensitive Supervoxels via Uniform Tessellations on Video Manifolds |
| Authors | Ran Yi, Yong-Jin Liu, Yu-Kun Lai |
| Abstract | Supervoxels are perceptually meaningful atomic regions in videos, obtained by grouping voxels that exhibit coherence in both appearance and motion. In this paper, we propose content-sensitive supervoxels (CSS), which are regularly-shaped 3D primitive volumes that possess the following characteristic: they are typically larger and longer in content-sparse regions (i.e., with homogeneous appearance and motion), and smaller and shorter in content-dense regions (i.e., with high variation of appearance and/or motion). To compute CSS, we map a video X to a 3-dimensional manifold M embedded in R^6, whose volume elements give a good measure of the content density in X. We propose an efficient Lloyd-like method with a splitting-merging scheme to compute a uniform tessellation on M, which induces the CSS in X. Theoretically our method has a good competitive ratio O(1). We also present a simple extension of CSS to stream CSS for processing long videos that cannot be loaded into main memory at once. We evaluate CSS, stream CSS and seven representative supervoxel methods on four video datasets. The results show that our method outperforms existing supervoxel methods. |
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| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Yi_Content-Sensitive_Supervoxels_via_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Yi_Content-Sensitive_Supervoxels_via_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/content-sensitive-supervoxels-via-uniform |
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The AnnCor CHILDES Treebank
| Title | The AnnCor CHILDES Treebank |
| Authors | Jan Odijk, Alexis Dimitriadis, Martijn van der Klis, Marjo van Koppen, Meie Otten, Remco van der Veen |
| Abstract | |
| Tasks | Language Acquisition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1360/ |
| https://www.aclweb.org/anthology/L18-1360 | |
| PWC | https://paperswithcode.com/paper/the-anncor-childes-treebank |
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