Paper Group NAWR 34
Subword-level Word Vector Representations for Korean. Robust Document Retrieval and Individual Evidence Modeling for Fact Extraction and Verification.. Knowledge Diffusion for Neural Dialogue Generation. Slot-Gated Modeling for Joint Slot Filling and Intent Prediction. RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network. Beyond …
Subword-level Word Vector Representations for Korean
| Title | Subword-level Word Vector Representations for Korean |
| Authors | Sungjoon Park, Jeongmin Byun, Sion Baek, Yongseok Cho, Alice Oh |
| Abstract | Research on distributed word representations is focused on widely-used languages such as English. Although the same methods can be used for other languages, language-specific knowledge can enhance the accuracy and richness of word vector representations. In this paper, we look at improving distributed word representations for Korean using knowledge about the unique linguistic structure of Korean. Specifically, we decompose Korean words into the jamo-level, beyond the character-level, allowing a systematic use of subword information. To evaluate the vectors, we develop Korean test sets for word similarity and analogy and make them publicly available. The results show that our simple method outperforms word2vec and character-level Skip-Grams on semantic and syntactic similarity and analogy tasks and contributes positively toward downstream NLP tasks such as sentiment analysis. |
| Tasks | Document Classification, Language Modelling, Machine Translation, Sentiment Analysis, Text Classification |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1226/ |
| https://www.aclweb.org/anthology/P18-1226 | |
| PWC | https://paperswithcode.com/paper/subword-level-word-vector-representations-for |
| Repo | https://github.com/SungjoonPark/KoreanWordVectors |
| Framework | none |
Robust Document Retrieval and Individual Evidence Modeling for Fact Extraction and Verification.
| Title | Robust Document Retrieval and Individual Evidence Modeling for Fact Extraction and Verification. |
| Authors | Tuhin Chakrabarty, Tariq Alhindi, Smar Muresan, a |
| Abstract | This paper presents the ColumbiaNLP submission for the FEVER Workshop Shared Task. Our system is an end-to-end pipeline that extracts factual evidence from Wikipedia and infers a decision about the truthfulness of the claim based on the extracted evidence. Our pipeline achieves significant improvement over the baseline for all the components (Document Retrieval, Sentence Selection and Textual Entailment) both on the development set and the test set. Our team finished 6th out of 24 teams on the leader-board based on the preliminary results with a FEVER score of 49.06 on the blind test set compared to 27.45 of the baseline system. |
| Tasks | Natural Language Inference |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5521/ |
| https://www.aclweb.org/anthology/W18-5521 | |
| PWC | https://paperswithcode.com/paper/robust-document-retrieval-and-individual |
| Repo | https://github.com/tuhinjubcse/FEVER-EMNLP |
| Framework | tf |
Knowledge Diffusion for Neural Dialogue Generation
| Title | Knowledge Diffusion for Neural Dialogue Generation |
| Authors | Shuman Liu, Hongshen Chen, Zhaochun Ren, Yang Feng, Qun Liu, Dawei Yin |
| Abstract | End-to-end neural dialogue generation has shown promising results recently, but it does not employ knowledge to guide the generation and hence tends to generate short, general, and meaningless responses. In this paper, we propose a neural knowledge diffusion (NKD) model to introduce knowledge into dialogue generation. This method can not only match the relevant facts for the input utterance but diffuse them to similar entities. With the help of facts matching and entity diffusion, the neural dialogue generation is augmented with the ability of convergent and divergent thinking over the knowledge base. Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats. The experiment results also show that our model outperforms competitive baseline models significantly. |
| Tasks | Dialogue Generation, Question Answering, Task-Oriented Dialogue Systems |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1138/ |
| https://www.aclweb.org/anthology/P18-1138 | |
| PWC | https://paperswithcode.com/paper/knowledge-diffusion-for-neural-dialogue |
| Repo | https://github.com/liushuman/neural-knowledge-diffusion |
| Framework | none |
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
| Title | Slot-Gated Modeling for Joint Slot Filling and Intent Prediction |
| Authors | Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu, Yun-Nung Chen |
| Abstract | Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2{%} and 1.9{%} relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively |
| Tasks | Intent Detection, Slot Filling, Spoken Dialogue Systems, Spoken Language Understanding |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-2118/ |
| https://www.aclweb.org/anthology/N18-2118 | |
| PWC | https://paperswithcode.com/paper/slot-gated-modeling-for-joint-slot-filling |
| Repo | https://github.com/MiuLab/SlotGated-SLU |
| Framework | tf |
RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network
| Title | RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network |
| Authors | Zinan Lin, Yongfeng Huang, Jilong Wang |
| Abstract | Quantization index modulation (QIM) steganography makes it possible to hide secret information in voice-over IP (VoIP) streams, which could be utilized by unauthorized entities to set up covert channels for malicious purposes. Detecting short QIM steganography samples, as is required by real circumstances, remains an unsolved challenge. In this paper, we propose an effective online steganalysis method to detect QIM steganography. We find four strong codeword correlation patterns in VoIP streams, which will be distorted after embedding with hidden data. To extract those correlation features, we propose the codeword correlation model, which is based on recurrent neural network (RNN). Furthermore, we propose the feature classification model to classify those correlation features into cover speech and stego speech categories. The whole RNN-based steganalysis model (RNN-SM) is trained in a supervised learning framework. Experiments show that on full embedding rate samples, RNN-SM is of high detection accuracy, which remains over 90% even when the sample is as short as 0.1 s, and is significantly higher than other state-of-the-art methods. For the challenging task of conducting steganalysis towards low embedding rate samples, RNN-SM also achieves a high accuracy. The average testing time for each sample is below 0.15% of sample length. These clues show that RNN-SM meets the short sample detection demand and is a state-of-the-art algorithm for online VoIP steganalysis. |
| Tasks | Quantization |
| Published | 2018-02-15 |
| URL | https://github.com/fjxmlzn/RNN-SM |
| http://www.andrew.cmu.edu/user/zinanl/publications/rnn-sm.pdf | |
| PWC | https://paperswithcode.com/paper/rnn-sm-fast-steganalysis-of-voip-streams |
| Repo | https://github.com/fjxmlzn/RNN-SM |
| Framework | none |
Beyond local reasoning for stereo confidence estimation with deep learning
| Title | Beyond local reasoning for stereo confidence estimation with deep learning |
| Authors | Fabio Tosi, Matteo Poggi, Antonio Benincasa, Stefano Mattoccia |
| Abstract | Confidence measures for stereo gained popularity in recent years due to their improved capability to detect outliers and the increasing number of applications exploiting these cues. In this field, convolutional neural networks achieved top-performance compared to other known techniques in the literature by processing local information to tell disparity assignments from outliers. Despite this outstanding achievements, all approaches rely on clues extracted with small receptive fields thus ignoring most of the overall image content. Therefore, in this paper, we propose to exploit nearby and farther clues available from image and disparity domains to obtain a more accurate confidence estimation. While local information is very effective for detecting high frequency patterns, it lacks insights from farther regions in the scene. On the other hand, enlarging the receptive field allows to include clues from farther regions but produces smoother uncertainty estimation, not particularly accurate when dealing with high frequency patterns. For these reasons, we propose in this paper a multi-stage cascaded network to combine the best of the two worlds. Extensive experiments on three datasets using three popular stereo algorithms prove that the proposed framework outperforms state-of-the-art confidence estimation techniques. |
| Tasks | |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Fabio_Tosi_Beyond_local_reasoning_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Fabio_Tosi_Beyond_local_reasoning_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/beyond-local-reasoning-for-stereo-confidence |
| Repo | https://github.com/fabiotosi92/LGC-Tensorflow |
| Framework | tf |
A High Coverage Method for Automatic False Friends Detection for Spanish and Portuguese
| Title | A High Coverage Method for Automatic False Friends Detection for Spanish and Portuguese |
| Authors | Santiago Castro, Jairo Bonanata, Aiala Ros{'a} |
| Abstract | False friends are words in two languages that look or sound similar, but have different meanings. They are a common source of confusion among language learners. Methods to detect them automatically do exist, however they make use of large aligned bilingual corpora, which are hard to find and expensive to build, or encounter problems dealing with infrequent words. In this work we propose a high coverage method that uses word vector representations to build a false friends classifier for any pair of languages, which we apply to the particular case of Spanish and Portuguese. The required resources are a large corpus for each language and a small bilingual lexicon for the pair. |
| Tasks | |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-3903/ |
| https://www.aclweb.org/anthology/W18-3903 | |
| PWC | https://paperswithcode.com/paper/a-high-coverage-method-for-automatic-false |
| Repo | https://github.com/pln-fing-udelar/false-friends |
| Framework | none |
Deep Dirichlet Multinomial Regression
| Title | Deep Dirichlet Multinomial Regression |
| Authors | Adrian Benton, Mark Dredze |
| Abstract | Dirichlet Multinomial Regression (DMR) and other supervised topic models can incorporate arbitrary document-level features to inform topic priors. However, their ability to model corpora are limited by the representation and selection of these features {–} a choice the topic modeler must make. Instead, we seek models that can learn the feature representations upon which to condition topic selection. We present deep Dirichlet Multinomial Regression (dDMR), a generative topic model that simultaneously learns document feature representations and topics. We evaluate dDMR on three datasets: New York Times articles with fine-grained tags, Amazon product reviews with product images, and Reddit posts with subreddit identity. dDMR learns representations that outperform DMR and LDA according to heldout perplexity and are more effective at downstream predictive tasks as the number of topics grows. Additionally, human subjects judge dDMR topics as being more representative of associated document features. Finally, we find that supervision leads to faster convergence as compared to an LDA baseline and that dDMR{'}s model fit is less sensitive to training parameters than DMR. |
| Tasks | Topic Models |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-1034/ |
| https://www.aclweb.org/anthology/N18-1034 | |
| PWC | https://paperswithcode.com/paper/deep-dirichlet-multinomial-regression |
| Repo | https://github.com/abenton/deep-dmr |
| Framework | none |
A Framework for the Quantitative Evaluation of Disentangled Representations
| Title | A Framework for the Quantitative Evaluation of Disentangled Representations |
| Authors | Cian Eastwood, Christopher K. I. Williams |
| Abstract | Recent AI research has emphasised the importance of learning disentangled representations of the explanatory factors behind data. Despite the growing interest in models which can learn such representations, visual inspection remains the standard evaluation metric. While various desiderata have been implied in recent definitions, it is currently unclear what exactly makes one disentangled representation better than another. In this work we propose a framework for the quantitative evaluation of disentangled representations when the ground-truth latent structure is available. Three criteria are explicitly defined and quantified to elucidate the quality of learnt representations and thus compare models on an equal basis. To illustrate the appropriateness of the framework, we employ it to compare quantitatively the representations learned by recent state-of-the-art models. |
| Tasks | |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=By-7dz-AZ |
| https://openreview.net/pdf?id=By-7dz-AZ | |
| PWC | https://paperswithcode.com/paper/a-framework-for-the-quantitative-evaluation |
| Repo | https://github.com/cianeastwood/qedr |
| Framework | tf |
Unsupervised Attention-guided Image-to-Image Translation
| Title | Unsupervised Attention-guided Image-to-Image Translation |
| Authors | Youssef Alami Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim |
| Abstract | Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene. Motivated by the important role of attention in human perception, we tackle this limitation by introducing unsupervised attention mechanisms which are jointly adversarially trained with the generators and discriminators. We empirically demonstrate that our approach is able to attend to relevant regions in the image without requiring any additional supervision, and that by doing so it achieves more realistic mappings compared to recent approaches. |
| Tasks | Image-to-Image Translation, Unsupervised Image-To-Image Translation |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7627-unsupervised-attention-guided-image-to-image-translation |
| http://papers.nips.cc/paper/7627-unsupervised-attention-guided-image-to-image-translation.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-attention-guided-image-to-image-1 |
| Repo | https://github.com/AlamiMejjati/Unsupervised-Attention-guided-Image-to-Image-Translation |
| Framework | tf |
Juman++: A Morphological Analysis Toolkit for Scriptio Continua
| Title | Juman++: A Morphological Analysis Toolkit for Scriptio Continua |
| Authors | Arseny Tolmachev, Daisuke Kawahara, Sadao Kurohashi |
| Abstract | We present a three-part toolkit for developing morphological analyzers for languages without natural word boundaries. The first part is a C++11/14 lattice-based morphological analysis library that uses a combination of linear and recurrent neural net language models for analysis. The other parts are a tool for exposing problems in the trained model and a partial annotation tool. Our morphological analyzer of Japanese achieves new SOTA on Jumandic-based corpora while being 250 times faster than the previous one. We also perform a small experiment and quantitive analysis and experience of using development tools. All components of the toolkit is open source and available under a permissive Apache 2 License. |
| Tasks | Art Analysis, Language Modelling, Morphological Analysis, Part-Of-Speech Tagging, Tokenization |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/D18-2010/ |
| https://www.aclweb.org/anthology/D18-2010 | |
| PWC | https://paperswithcode.com/paper/juman-a-morphological-analysis-toolkit-for |
| Repo | https://github.com/ku-nlp/jumanpp |
| Framework | none |
See and Think: Disentangling Semantic Scene Completion
| Title | See and Think: Disentangling Semantic Scene Completion |
| Authors | Shice Liu, Yu Hu, Yiming Zeng, Qiankun Tang, Beibei Jin, Yinhe Han, Xiaowei Li |
| Abstract | Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings. In this work, we propose a disentangled framework, sequentially carrying out 2D semantic segmentation, 2D-3D reprojection and 3D semantic scene completion. This three-stage framework has three advantages: (1) explicit semantic segmentation significantly boosts performance; (2) flexible fusion ways of sensor data bring good extensibility; (3) progress in any subtask will promote the holistic performance. Experimental results show that regardless of inputing a single depth or RGB-D, our framework can generate high-quality semantic scene completion, and outperforms state-of-the-art approaches on both synthetic and real datasets. |
| Tasks | Semantic Segmentation |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7310-see-and-think-disentangling-semantic-scene-completion |
| http://papers.nips.cc/paper/7310-see-and-think-disentangling-semantic-scene-completion.pdf | |
| PWC | https://paperswithcode.com/paper/see-and-think-disentangling-semantic-scene |
| Repo | https://github.com/ShiceLiu/SATNet |
| Framework | pytorch |
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning
| Title | Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning |
| Authors | Yaosheng Yang, Wenliang Chen, Zhenghua Li, Zhengqiu He, Min Zhang |
| Abstract | A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data. One solution is to utilize the method of distant supervision, which has been widely used in relation extraction, to automatically populate annotated training data without humancost. The distant supervision assumption here is that if a string in text is included in a predefined dictionary of entities, the string might be an entity. However, this kind of auto-generated data suffers from two main problems: incomplete and noisy annotations, which affect the performance of NER models. In this paper, we propose a novel approach which can partially solve the above problems of distant supervision for NER. In our approach, to handle the incomplete problem, we apply partial annotation learning to reduce the effect of unknown labels of characters. As for noisy annotation, we design an instance selector based on reinforcement learning to distinguish positive sentences from auto-generated annotations. In experiments, we create two datasets for Chinese named entity recognition in two domains with the help of distant supervision. The experimental results show that the proposed approach obtains better performance than the comparison systems on both two datasets. |
| Tasks | Chinese Named Entity Recognition, Named Entity Recognition, Relation Extraction |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1183/ |
| https://www.aclweb.org/anthology/C18-1183 | |
| PWC | https://paperswithcode.com/paper/distantly-supervised-ner-with-partial |
| Repo | https://github.com/rainarch/DSNER |
| Framework | tf |
