Paper Group NANR 218
SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction. SHE2: Stochastic Hamiltonian Exploration and Exploitation for Derivative-Free Optimization. Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks. Human Evaluation of Neural Machine Translation: The Case of Deep Learning. Enhanced Bayesian Compression …
SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction
| Title | SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction |
| Authors | Anna H{"a}tty, Dominik Schlechtweg, Sabine Schulte im Walde |
| Abstract | We introduce SURel, a novel dataset with human-annotated meaning shifts between general-language and domain-specific contexts. We show that meaning shifts of term candidates cause errors in term extraction, and demonstrate that the SURel annotation reflects these errors. Furthermore, we illustrate that SURel enables us to assess optimisations of term extraction techniques when incorporating meaning shifts. |
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| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-1001/ |
| https://www.aclweb.org/anthology/S19-1001 | |
| PWC | https://paperswithcode.com/paper/surel-a-gold-standard-for-incorporating |
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SHE2: Stochastic Hamiltonian Exploration and Exploitation for Derivative-Free Optimization
| Title | SHE2: Stochastic Hamiltonian Exploration and Exploitation for Derivative-Free Optimization |
| Authors | Haoyi Xiong, Wenqing Hu, Zhanxing Zhu, Xinjian Li, Yunchao Zhang, Jun Huan |
| Abstract | Derivative-free optimization (DFO) using trust region methods is frequently used for machine learning applications, such as (hyper-)parameter optimization without the derivatives of objective functions known. Inspired by the recent work in continuous-time minimizers, our work models the common trust region methods with the exploration-exploitation using a dynamical system coupling a pair of dynamical processes. While the first exploration process searches the minimum of the blackbox function through minimizing a time-evolving surrogation function, another exploitation process updates the surrogation function time-to-time using the points traversed by the exploration process. The efficiency of derivative-free optimization thus depends on ways the two processes couple. In this paper, we propose a novel dynamical system, namely \ThePrev—\underline{S}tochastic \underline{H}amiltonian \underline{E}xploration and \underline{E}xploitation, that surrogates the subregions of blackbox function using a time-evolving quadratic function, then explores and tracks the minimum of the quadratic functions using a fast-converging Hamiltonian system. The \ThePrev\ algorithm is later provided as a discrete-time numerical approximation to the system. To further accelerate optimization, we present \TheName\ that parallelizes multiple \ThePrev\ threads for concurrent exploration and exploitation. Experiment results based on a wide range of machine learning applications show that \TheName\ outperform a boarder range of derivative-free optimization algorithms with faster convergence speed under the same settings. |
| Tasks | Text-to-Image Generation |
| Published | 2019-05-01 |
| URL | https://openreview.net/forum?id=rklEUjR5tm |
| https://openreview.net/pdf?id=rklEUjR5tm | |
| PWC | https://paperswithcode.com/paper/she2-stochastic-hamiltonian-exploration-and |
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Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks
| Title | Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks |
| Authors | Xiaolong Jiang, Zehao Xiao, Baochang Zhang, Xiantong Zhen, Xianbin Cao, David Doermann, Ling Shao |
| Abstract | Crowd counting has recently attracted increasing interest in computer vision but remains a challenging problem. In this paper, we propose a trellis encoder-decoder network (TEDnet) for crowd counting, which focuses on generating high-quality density estimation maps. The major contributions are four-fold. First, we develop a new trellis architecture that incorporates multiple decoding paths to hierarchically aggregate features at different encoding stages, which improves the representative capability of convolutional features for large variations in objects. Second, we employ dense skip connections interleaved across paths to facilitate sufficient multi-scale feature fusions, which also helps TEDnet to absorb the supervision information. Third, we propose a new combinatorial loss to enforce similarities in local coherence and spatial correlation between maps. By distributedly imposing this combinatorial loss on intermediate outputs, TEDnet can improve the back-propagation process and alleviate the gradient vanishing problem. Finally, on four widely-used benchmarks, our TEDnet achieves the best overall performance in terms of both density map quality and counting accuracy, with an improvement up to 14% in MAE metric. These results validate the effectiveness of TEDnet for crowd counting. |
| Tasks | Crowd Counting, Density Estimation |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Jiang_Crowd_Counting_and_Density_Estimation_by_Trellis_Encoder-Decoder_Networks_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Jiang_Crowd_Counting_and_Density_Estimation_by_Trellis_Encoder-Decoder_Networks_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/crowd-counting-and-density-estimation-by-1 |
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Human Evaluation of Neural Machine Translation: The Case of Deep Learning
| Title | Human Evaluation of Neural Machine Translation: The Case of Deep Learning |
| Authors | Marie Escribe |
| Abstract | Recent advances in artificial neural networks now have a great impact on translation technology. A considerable achievement was reached in this field with the publication of L{'}Apprentissage Profond. This book, originally written in English (Deep Learning), was entirely machine-translated into French and post-edited by several experts. In this context, it appears essential to have a clear vision of the performance of MT tools. Providing an evaluation of NMT is precisely the aim of the present research paper. To accomplish this objective, a framework for error categorisation was built and a comparative analysis of the raw translation output and the post-edited version was performed with the purpose of identifying recurring patterns of errors. The findings showed that even though some grammatical errors were spotted, the output was generally correct from a linguistic point of view. The most recurring errors are linked to the specialised terminology employed in this book. Further errors include parts of text that were not translated as well as edits based on stylistic preferences. The major part of the output was not acceptable as such and required several edits per segment, but some sentences were of publishable quality and were therefore left untouched in the final version. |
| Tasks | Machine Translation |
| Published | 2019-09-01 |
| URL | https://www.aclweb.org/anthology/W19-8705/ |
| https://www.aclweb.org/anthology/W19-8705 | |
| PWC | https://paperswithcode.com/paper/human-evaluation-of-neural-machine |
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Enhanced Bayesian Compression via Deep Reinforcement Learning
| Title | Enhanced Bayesian Compression via Deep Reinforcement Learning |
| Authors | Xin Yuan, Liangliang Ren, Jiwen Lu, Jie Zhou |
| Abstract | In this paper, we propose an Enhanced Bayesian Compression method to flexibly compress the deep networks via reinforcement learning. Unlike the existing Bayesian compression method which cannot explicitly enforce quantization weights during training, our method learns flexible codebooks in each layer for an optimal network quantization. To dynamically adjust the state of codebooks, we employ an Actor-Critic network to collaborate with the original deep network. Different from most existing network quantization methods, our EBC does not require re-training procedures after the quantization. Experimental results show that our method obtains low-bit precision with acceptable accuracy drop on MNIST, CIFAR and ImageNet. |
| Tasks | Quantization |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Yuan_Enhanced_Bayesian_Compression_via_Deep_Reinforcement_Learning_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Yuan_Enhanced_Bayesian_Compression_via_Deep_Reinforcement_Learning_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/enhanced-bayesian-compression-via-deep |
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Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration
| Title | Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration |
| Authors | Zhufeng Pan, Kun Bai, Yan Wang, Lianqiang Zhou, Xiaojiang Liu |
| Abstract | In multi-turn dialogue, utterances do not always take the full form of sentences. These incomplete utterances will greatly reduce the performance of open-domain dialogue systems. Restoring more incomplete utterances from context could potentially help the systems generate more relevant responses. To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K is collected and manually labeled with the explicit relation between an utterance and its context. We also propose a {``}pick-and-combine{''} model to restore the incomplete utterance from its context. Experimental results demonstrate that the annotated dataset and the proposed approach significantly boost the response quality of both single-turn and multi-turn dialogue systems. | |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1191/ |
| https://www.aclweb.org/anthology/D19-1191 | |
| PWC | https://paperswithcode.com/paper/improving-open-domain-dialogue-systems-via |
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AMP: Adaptive Masked Proxies for Few-Shot Segmentation
| Title | AMP: Adaptive Masked Proxies for Few-Shot Segmentation |
| Authors | Mennatullah Siam, Boris N. Oreshkin, Martin Jagersand |
| Abstract | Deep learning has thrived by training on large-scale datasets. However, in robotics applications sample efficiency is critical. We propose a novel adaptive masked proxies method that constructs the final segmentation layer weights from few labelled samples. It utilizes multi-resolution average pooling on base embeddings masked with the label to act as a positive proxy for the new class, while fusing it with the previously learned class signatures. Our method is evaluated on PASCAL-5^i dataset and outperforms the state-of-the-art in the few-shot semantic segmentation. Unlike previous methods, our approach does not require a second branch to estimate parameters or prototypes, which enables it to be used with 2-stream motion and appearance based segmentation networks. We further propose a novel setup for evaluating continual learning of object segmentation which we name incremental PASCAL (iPASCAL) where our method outperforms the baseline method. Our code is publicly available at https://github.com/MSiam/AdaptiveMaskedProxies. |
| Tasks | Continual Learning, Few-Shot Semantic Segmentation, Semantic Segmentation |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Siam_AMP_Adaptive_Masked_Proxies_for_Few-Shot_Segmentation_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Siam_AMP_Adaptive_Masked_Proxies_for_Few-Shot_Segmentation_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/amp-adaptive-masked-proxies-for-few-shot |
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The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English
| Title | The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English |
| Authors | Francisco Guzm{'a}n, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, Marc{'}Aurelio Ranzato |
| Abstract | For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challenges of learning with limited supervision, it is difficult to evaluate methods trained on low-resource language pairs because of the lack of freely and publicly available benchmarks. In this work, we introduce the FLORES evaluation datasets for Nepali{–}English and Sinhala{–} English, based on sentences translated from Wikipedia. Compared to English, these are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available. We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised. Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low-resource MT. Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores. |
| Tasks | Machine Translation |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1632/ |
| https://www.aclweb.org/anthology/D19-1632 | |
| PWC | https://paperswithcode.com/paper/the-flores-evaluation-datasets-for-low |
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Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework
| Title | Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework |
| Authors | Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Shuming Shi |
| Abstract | End-to-end sequence generation is a popular technique for developing open domain dialogue systems, though they suffer from the \textit{safe response problem}. Researchers have attempted to tackle this problem by incorporating generative models with the returns of retrieval systems. Recently, a skeleton-then-response framework has been shown promising results for this task. Nevertheless, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator are still challenging. This paper presents a novel framework in which the skeleton extraction is made by an interpretable matching model and the following skeleton-guided response generation is accomplished by a separately trained generator. Extensive experiments demonstrate the effectiveness of our model designs. |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1195/ |
| https://www.aclweb.org/anthology/D19-1195 | |
| PWC | https://paperswithcode.com/paper/retrieval-guided-dialogue-response-generation |
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Tagger for Polish Computer Mediated Communication Texts
| Title | Tagger for Polish Computer Mediated Communication Texts |
| Authors | Wiktor Walentynowicz, Maciej Piasecki, Marcin Oleksy |
| Abstract | In this paper we present a morpho-syntactic tagger dedicated to Computer-mediated Communication texts in Polish. Its construction is based on an expanded RNN-based neural network adapted to the work on noisy texts. Among several techniques, the tagger utilises fastText embedding vectors, sequential character embedding vectors, and Brown clustering for the coarse-grained representation of sentence structures. In addition a set of manually written rules was proposed for post-processing. The system was trained to disambiguate descriptions of words in relation to Parts of Speech tags together with the full morphological information in terms of values for the different grammatical categories. We present also evaluation of several model variants on the gold standard annotated CMC data, comparison to the state-of-the-art taggers for Polish and error analysis. The proposed tagger shows significantly better results in this domain and demonstrates the viability of adaptation. |
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| Published | 2019-09-01 |
| URL | https://www.aclweb.org/anthology/R19-1148/ |
| https://www.aclweb.org/anthology/R19-1148 | |
| PWC | https://paperswithcode.com/paper/tagger-for-polish-computer-mediated |
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No, you’re not alone: A better way to find people with similar experiences on Reddit
| Title | No, you’re not alone: A better way to find people with similar experiences on Reddit |
| Authors | Zhilin Wang, Elena Rastorgueva, Weizhe Lin, Xiaodong Wu |
| Abstract | We present a probabilistic clustering algorithm that can help Reddit users to find posts that discuss experiences similar to their own. This model is built upon the BERT Next Sentence Prediction model and reduces the time complexity for clustering all posts in a corpus from O(n{^{}}2) to O(n) with respect to the number of posts. We demonstrate that such probabilistic clustering can yield a performance better than baseline clustering methods based on Latent Dirichlet Allocation (Blei et al., 2003) and Word2Vec (Mikolov et al., 2013). Furthermore, there is a high degree of coherence between our probabilistic clustering and the exhaustive comparison O(n{^{}}2) algorithm in which the similarity between every pair of posts is found. This makes the use of the BERT Next Sentence Prediction model more practical for unsupervised clustering tasks due to the high runtime overhead of each BERT computation. |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5540/ |
| https://www.aclweb.org/anthology/D19-5540 | |
| PWC | https://paperswithcode.com/paper/no-youre-not-alone-a-better-way-to-find |
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Local Features and Visual Words Emerge in Activations
| Title | Local Features and Visual Words Emerge in Activations |
| Authors | Oriane Simeoni, Yannis Avrithis, Ondrej Chum |
| Abstract | We propose a novel method of deep spatial matching (DSM) for image retrieval. Initial ranking is based on image descriptors extracted from convolutional neural network activations by global pooling, as in recent state-of-the-art work. However, the same sparse 3D activation tensor is also approximated by a collection of local features. These local features are then robustly matched to approximate the optimal alignment of the tensors. This happens without any network modification, additional layers or training. No local feature detection happens on the original image. No local feature descriptors and no visual vocabulary are needed throughout the whole process. We experimentally show that the proposed method achieves the state-of-the-art performance on standard benchmarks across different network architectures and different global pooling methods. The highest gain in performance is achieved when diffusion on the nearest-neighbor graph of global descriptors is initiated from spatially verified images. |
| Tasks | Image Retrieval |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Simeoni_Local_Features_and_Visual_Words_Emerge_in_Activations_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Simeoni_Local_Features_and_Visual_Words_Emerge_in_Activations_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/local-features-and-visual-words-emerge-in-1 |
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Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations
| Title | Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations |
| Authors | Ran Le, Wenpeng Hu, Mingyue Shang, Zhenjun You, Lidong Bing, Dongyan Zhao, Rui Yan |
| Abstract | Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario. In real multi- party conversations, we can observe who is speaking, but the addressee information is not always explicit. In this paper, we aim to tackle the challenge of identifying all the miss- ing addressees in a conversation session. To this end, we introduce a novel who-to-whom (W2W) model which models users and utterances in the session jointly in an interactive way. We conduct experiments on the benchmark Ubuntu Multi-Party Conversation Corpus and the experimental results demonstrate that our model outperforms baselines with consistent improvements. |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1199/ |
| https://www.aclweb.org/anthology/D19-1199 | |
| PWC | https://paperswithcode.com/paper/who-is-speaking-to-whom-learning-to-identify |
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Incorporating Textual Information on User Behavior for Personality Prediction
| Title | Incorporating Textual Information on User Behavior for Personality Prediction |
| Authors | Kosuke Yamada, Ryohei Sasano, Koichi Takeda |
| Abstract | Several recent studies have shown that textual information of user posts and user behaviors such as liking and sharing the specific posts are useful for predicting the personality of social media users. However, less attention has been paid to the textual information derived from the user behaviors. In this paper, we investigate the effect of textual information on user behaviors for personality prediction. Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors. They also show that taking user behaviors into account is crucial for predicting the personality of users who do not post frequently. |
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| Published | 2019-07-01 |
| URL | https://www.aclweb.org/anthology/P19-2024/ |
| https://www.aclweb.org/anthology/P19-2024 | |
| PWC | https://paperswithcode.com/paper/incorporating-textual-information-on-user |
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Attribute-Driven Feature Disentangling and Temporal Aggregation for Video Person Re-Identification
| Title | Attribute-Driven Feature Disentangling and Temporal Aggregation for Video Person Re-Identification |
| Authors | Yiru Zhao, Xu Shen, Zhongming Jin, Hongtao Lu, Xian-sheng Hua |
| Abstract | Video-based person re-identification plays an important role in surveillance video analysis, expanding image-based methods by learning features of multiple frames. Most existing methods fuse features by temporal average-pooling, without exploring the different frame weights caused by various viewpoints, poses, and occlusions. In this paper, we propose an attribute-driven method for feature disentangling and frame re-weighting. The features of single frames are disentangled into groups of sub-features, each corresponds to specific semantic attributes. The sub-features are re-weighted by the confidence of attribute recognition and then aggregated at the temporal dimension as the final representation. By means of this strategy, the most informative regions of each frame are enhanced and contributes to a more discriminative sequence representation. Extensive ablation studies demonstrate the effectiveness of feature disentangling as well as temporal re-weighting. The experimental results on the iLIDS-VID, PRID-2011 and MARS datasets demonstrate that our proposed method outperforms existing state-of-the-art approaches. |
| Tasks | Person Re-Identification, Video-Based Person Re-Identification |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Attribute-Driven_Feature_Disentangling_and_Temporal_Aggregation_for_Video_Person_Re-Identification_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Attribute-Driven_Feature_Disentangling_and_Temporal_Aggregation_for_Video_Person_Re-Identification_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/attribute-driven-feature-disentangling-and |
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