Paper Group NANR 199
Towards an Automatic Assessment of Crowdsourced Data for NLU. Incremental Natural Language Processing: Challenges, Strategies, and Evaluation. Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018) …
Towards an Automatic Assessment of Crowdsourced Data for NLU
| Title | Towards an Automatic Assessment of Crowdsourced Data for NLU |
| Authors | Patricia Braunger, Wolfgang Maier, Jan Wessling, Maria Schmidt |
| Abstract | |
| Tasks | Language Modelling, Speech Recognition, Text Generation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1315/ |
| https://www.aclweb.org/anthology/L18-1315 | |
| PWC | https://paperswithcode.com/paper/towards-an-automatic-assessment-of |
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Incremental Natural Language Processing: Challenges, Strategies, and Evaluation
| Title | Incremental Natural Language Processing: Challenges, Strategies, and Evaluation |
| Authors | Arne K{"o}hn |
| Abstract | Incrementality is ubiquitous in human-human interaction and beneficial for human-computer interaction. It has been a topic of research in different parts of the NLP community, mostly with focus on the specific topic at hand even though incremental systems have to deal with similar challenges regardless of domain. In this survey, I consolidate and categorize the approaches, identifying similarities and differences in the computation and data, and show trade-offs that have to be considered. A focus lies on evaluating incremental systems because the standard metrics often fail to capture the incremental properties of a system and coming up with a suitable evaluation scheme is non-trivial. |
| Tasks | Dialogue Understanding |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1253/ |
| https://www.aclweb.org/anthology/C18-1253 | |
| PWC | https://paperswithcode.com/paper/incremental-natural-language-processing |
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Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction
| Title | Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction |
| Authors | Jekaterina Belakova, Dimitra Gkatzia |
| Abstract | One of the most natural ways for human robot communication is through spoken language. Training human-robot interaction systems require access to large datasets which are expensive to obtain and labour intensive. In this paper, we describe an approach for learning from minimal data, using as a toy example language understanding in spoken dialogue systems. Understanding of spoken language is crucial because it has implications for natural language generation, i.e. correctly understanding a user{'}s utterance will lead to choosing the right response/action. Finally, we discuss implications for Natural Language Generation in Human-Robot Interaction. |
| Tasks | One-Shot Learning, Spoken Dialogue Systems, Text Generation |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-6902/ |
| https://www.aclweb.org/anthology/W18-6902 | |
| PWC | https://paperswithcode.com/paper/learning-from-limited-datasets-implications |
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Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018)
| Title | Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018) |
| Authors | Nicoletta Calzolari, Khalid Choukri, Christopher Cieri, Thierry Declerck, Koiti Hasida, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis, Takenobu Tokunaga, Sara Goggi, H{'e}l{`e}ne Mazo |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/papers/L/L18/L18-1000/ |
| https://www.aclweb.org/anthology/L18-1000 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-eleventh-international |
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Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization
| Title | Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization |
| Authors | Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Xiaoning Qian, Yung-Yu Chuang |
| Abstract | In this paper, we address co-saliency detection in a set of images jointly covering objects of a specific class by an unsupervised convolutional neural network (CNN). Our method does not require any additional training data in the form of object masks. We decompose co-saliency detection into two sub-tasks, single-image saliency detection and cross-image co-occurrence region discovery corresponding to two novel unsupervised losses, the single-image saliency (SIS) loss and the co-occurrence (COOC) loss. The two losses are modeled on a graphical model where the former and the latter act as the unary and pairwise terms, respectively. These two tasks can be jointly optimized for generating co-saliency maps of high quality. Furthermore, the quality of the generated co-saliency maps can be enhanced via two extensions: map sharpening by self-paced learning and boundary preserving by fully connected conditional random fields. Experiments show that our method achieves superior results, even outperforming many supervised methods. |
| Tasks | Co-Saliency Detection, Saliency Detection |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Kuang-Jui_Hsu_Unsupervised_CNN-based_co-saliency_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Kuang-Jui_Hsu_Unsupervised_CNN-based_co-saliency_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-cnn-based-co-saliency-detection |
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Tailoring Neural Architectures for Translating from Morphologically Rich Languages
| Title | Tailoring Neural Architectures for Translating from Morphologically Rich Languages |
| Authors | Peyman Passban, Andy Way, Qun Liu |
| Abstract | A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures. When translating from morphologically rich languages (MRLs), a source word could be mapped to several words or even a full sentence on the target side, which means an MCW should not be treated as an atomic unit. In order to provide better translations for MRLs, we boost the existing neural machine translation (NMT) architecture with a double- channel encoder and a double-attentive decoder. The main goal targeted in this research is to provide richer information on the encoder side and redesign the decoder accordingly to benefit from such information. Our experimental results demonstrate that we could achieve our goal as the proposed model outperforms existing subword- and character-based architectures and showed significant improvements on translating from German, Russian, and Turkish into English. |
| Tasks | Machine Translation |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1265/ |
| https://www.aclweb.org/anthology/C18-1265 | |
| PWC | https://paperswithcode.com/paper/tailoring-neural-architectures-for |
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Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings
| Title | Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings |
| Authors | Hong-You Chen, Cheng-Syuan Lee, Keng-Te Liao, Shou-De Lin |
| Abstract | Lexicon relation extraction given distributional representation of words is an important topic in NLP. We observe that the state-of-the-art projection-based methods cannot be generalized to handle unseen hypernyms. We propose to analyze it in the perspective of pollution and construct the corresponding indicator to measure it. We propose a word relation autoencoder (WRAE) model to address the challenge. Experiments on several hypernym-like lexicon datasets show that our model outperforms the competitors significantly. |
| Tasks | Relation Extraction, Word Embeddings |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1519/ |
| https://www.aclweb.org/anthology/D18-1519 | |
| PWC | https://paperswithcode.com/paper/word-relation-autoencoder-for-unseen-hypernym |
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TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights
| Title | TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights |
| Authors | Diwen Wan, Fumin Shen, Li Liu, Fan Zhu, Jie Qin, Ling Shao, Heng Tao Shen |
| Abstract | Despite the remarkable success of Convolutional Neural Networks (CNNs) on generalized visual tasks, high computational and memory costs restrict their comprehensive applications on consumer electronics (e.g., portable or smart wearable devices). Recent advancements in binarized networks have demonstrated progress on reducing computational and memory costs, however, they suffer from significant performance degradation comparing to their full-precision counterparts. Thus, a highly-economical yet effective CNN that is authentically applicable to consumer electronics is at urgent need. In this work, we propose a Ternary-Binary Network (TBN), which provides an efficient approximation to standard CNNs. Based on an accelerated ternary-binary matrix multiplication, TBN replaces the arithmetical operations in standard CNNs with efficient XOR, AND and bitcount operations, and thus provides an optimal tradeoff between memory, efficiency and performance. TBN demonstrates its consistent effectiveness when applied to various CNN architectures (e.g., AlexNet and ResNet) on multiple datasets of different scales, and provides ~32x memory savings and 40x faster convolutional operations. Meanwhile, TBN can outperform XNOR-Network by up to 5.5% (top-1 accuracy) on the ImageNet classification task, and up to 4.4% (mAP score) on the PASCAL VOC object detection task. |
| Tasks | Object Detection |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Diwen_Wan_TBN_Convolutional_Neural_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Diwen_Wan_TBN_Convolutional_Neural_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/tbn-convolutional-neural-network-with-ternary |
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ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets
| Title | ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets |
| Authors | Kevin Swanberg, Madiha Mirza, Ted Pedersen, Zhenduo Wang |
| Abstract | This paper describes the ALANIS system that participated in Task 3 of SemEval-2018. We develop a system for detection of irony, as well as the detection of three types of irony: verbal polar irony, other verbal irony, and situational irony. The system uses a logistic regression model in subtask A and a voted classifier system with manually developed features to identify ironic tweets. This model improves on a naive bayes baseline by about 8 percent on training set. |
| Tasks | Feature Engineering, Feature Selection, Semantic Textual Similarity |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1082/ |
| https://www.aclweb.org/anthology/S18-1082 | |
| PWC | https://paperswithcode.com/paper/alanis-at-semeval-2018-task-3-a-feature |
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uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network
| Title | uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network |
| Authors | Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Diana Inkpen, David Van Bruwaene |
| Abstract | We propose a novel attentive hybrid GRU-based network (SAHGN), which we used at SemEval-2018 Task 1: Affect in Tweets. Our network has two main characteristics, 1) has the ability to internally optimize its feature representation using attention mechanisms, and 2) provides a hybrid representation using a character level Convolutional Neural Network (CNN), as well as a self-attentive word-level encoder. The key advantage of our model is its ability to signify the relevant and important information that enables self-optimization. Results are reported on the valence intensity regression task. |
| Tasks | Tokenization |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1027/ |
| https://www.aclweb.org/anthology/S18-1027 | |
| PWC | https://paperswithcode.com/paper/uottawa-at-semeval-2018-task-1-self-attentive |
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Universal Growth in Production Economies
| Title | Universal Growth in Production Economies |
| Authors | Simina Branzei, Ruta Mehta, Noam Nisan |
| Abstract | We study a simple variant of the von Neumann model of an expanding economy, in which multiple producers make goods according to their production function. The players trade their goods at the market and then use the bundles received as inputs for the production in the next round. The decision that players have to make is how to invest their money (i.e. bids) in each round. We show that a simple decentralized dynamic, where players update their bids on the goods in the market proportionally to how useful the investments were, leads to growth of the economy in the long term (whenever growth is possible) but also creates unbounded inequality, i.e. very rich and very poor players emerge. We analyze several other phenomena, such as how the relation of a player with others influences its development and the Gini index of the system. |
| Tasks | |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7467-universal-growth-in-production-economies |
| http://papers.nips.cc/paper/7467-universal-growth-in-production-economies.pdf | |
| PWC | https://paperswithcode.com/paper/universal-growth-in-production-economies |
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Pruning Basic Elements for Better Automatic Evaluation of Summaries
| Title | Pruning Basic Elements for Better Automatic Evaluation of Summaries |
| Authors | Ukyo Honda, Tsutomu Hirao, Masaaki Nagata |
| Abstract | We propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements (pBE). Although the BE concept is widely used for the automated evaluation of summaries, its weakness is that it redundantly matches basic elements. To avoid this redundancy, pBE prunes basic elements by (1) disregarding frequency count of basic elements and (2) reducing semantically overlapped basic elements based on word similarity. Even though it is simple, pBE outperforms ROUGE in DUC datasets in most cases and achieves the highest rank correlation coefficient in TAC 2011 AESOP task. |
| Tasks | Word Embeddings |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-2104/ |
| https://www.aclweb.org/anthology/N18-2104 | |
| PWC | https://paperswithcode.com/paper/pruning-basic-elements-for-better-automatic |
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Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning
| Title | Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning |
| Authors | Aras Dargazany, Kunal Mankodiya |
| Abstract | In vanilla backpropagation (VBP), activation function matters considerably in terms of non-linearity and differentiability. Vanishing gradient has been an important problem related to the bad choice of activation function in deep learning (DL). This work shows that a differentiable activation function is not necessary any more for error backpropagation. The derivative of the activation function can be replaced by an iterative temporal differencing (ITD) using fixed random feedback weight alignment (FBA). Using FBA with ITD, we can transform the VBP into a more biologically plausible approach for learning deep neural network architectures. We don’t claim that ITD works completely the same as the spike-time dependent plasticity (STDP) in our brain but this work can be a step toward the integration of STDP-based error backpropagation in deep learning. |
| Tasks | |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=BkpXqwUTZ |
| https://openreview.net/pdf?id=BkpXqwUTZ | |
| PWC | https://paperswithcode.com/paper/iterative-temporal-differencing-with-fixed |
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Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts
| Title | Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts |
| Authors | Annie Louis, Charles Sutton |
| Abstract | An essential aspect to understanding narratives is to grasp the interaction between characters in a story and the actions they take. We examine whether computational models can capture this interaction, when both character attributes and actions are expressed as complex natural language descriptions. We propose role-playing games as a testbed for this problem, and introduce a large corpus of game transcripts collected from online discussion forums. Using neural language models which combine character and action descriptions from these stories, we show that we can learn the latent ties. Action sequences are better predicted when the character performing the action is also taken into account, and vice versa for character attributes. |
| Tasks | Topic Models |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-2111/ |
| https://www.aclweb.org/anthology/N18-2111 | |
| PWC | https://paperswithcode.com/paper/deep-dungeons-and-dragons-learning-character |
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Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks
| Title | Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks |
| Authors | Aojun Zhou, Anbang Yao, Kuan Wang, Yurong Chen |
| Abstract | Benefiting from tens of millions of hierarchically stacked learnable parameters, Deep Neural Networks (DNNs) have demonstrated overwhelming accuracy on a variety of artificial intelligence tasks. However reversely, the large size of DNN models lays a heavy burden on storage, computation and power consumption, which prohibits their deployments on the embedded and mobile systems. In this paper, we propose Explicit Loss-error-aware Quantization (ELQ), a new method that can train DNN models with very low-bit parameter values such as ternary and binary ones to approximate 32-bit floating-point counterparts without noticeable loss of predication accuracy. Unlike existing methods that usually pose the problem as a straightforward approximation of the layer-wise weights or outputs of the original full-precision model (specifically, minimizing the error of the layer-wise weights or inner products of the weights and the inputs between the original and respective quantized models), our ELQ elaborately bridges the loss perturbation from the weight quantization and an incremental quantization strategy to address DNN quantization. Through explicitly regularizing the loss perturbation and the weight approximation error in an incremental way, we show that such a new optimization method is theoretically reasonable and practically effective. As validated with two mainstream convolutional neural network families (i.e., fully convolutional and non-fully convolutional), our ELQ shows better results than the state-of-the-art quantization methods on the large scale ImageNet classification dataset. Code will be made publicly available. |
| Tasks | Quantization |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhou_Explicit_Loss-Error-Aware_Quantization_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Explicit_Loss-Error-Aware_Quantization_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/explicit-loss-error-aware-quantization-for |
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