Paper Group NANR 235
Meta-Embedding Sentence Representation for Textual Similarity. Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network. Improving Translations by Combining Fuzzy-Match Repair with Automatic Post-Editing. Speech-based Estimation of Bulbar Regression in Amyotrophic Lateral Sclerosis. Transformer-Based Capsule Network For Stoc …
Meta-Embedding Sentence Representation for Textual Similarity
| Title | Meta-Embedding Sentence Representation for Textual Similarity |
| Authors | Amir Hazem, Hern, Nicolas ez |
| Abstract | Word embedding models are now widely used in most NLP applications. Despite their effectiveness, there is no clear evidence about the choice of the most appropriate model. It often depends on the nature of the task and on the quality and size of the used data sets. This remains true for bottom-up sentence embedding models. However, no straightforward investigation has been conducted so far. In this paper, we propose a systematic study of the impact of the main word embedding models on sentence representation. By contrasting in-domain and pre-trained embedding models, we show under which conditions they can be jointly used for bottom-up sentence embeddings. Finally, we propose the first bottom-up meta-embedding representation at the sentence level for textual similarity. Significant improvements are observed in several tasks including question-to-question similarity, paraphrasing and next utterance ranking. |
| Tasks | Question Similarity, Sentence Embedding, Sentence Embeddings |
| Published | 2019-09-01 |
| URL | https://www.aclweb.org/anthology/R19-1055/ |
| https://www.aclweb.org/anthology/R19-1055 | |
| PWC | https://paperswithcode.com/paper/meta-embedding-sentence-representation-for |
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Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network
| Title | Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network |
| Authors | Shuqing Bian, Wayne Xin Zhao, Yang Song, Tao Zhang, Ji-Rong Wen |
| Abstract | Person-job fit has been an important task which aims to automatically match job positions with suitable candidates. Previous methods mainly focus on solving the match task in single-domain setting, which may not work well when labeled data is limited. We study the domain adaptation problem for person-job fit. We first propose a deep global match network for capturing the global semantic interactions between two sentences from a job posting and a candidate resume respectively. Furthermore, we extend the match network and implement domain adaptation in three levels, sentence-level representation, sentence-level match, and global match. Extensive experiment results on a large real-world dataset consisting of six domains have demonstrated the effectiveness of the proposed model, especially when there is not sufficient labeled data. |
| Tasks | Domain Adaptation |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1487/ |
| https://www.aclweb.org/anthology/D19-1487 | |
| PWC | https://paperswithcode.com/paper/domain-adaptation-for-person-job-fit-with |
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Improving Translations by Combining Fuzzy-Match Repair with Automatic Post-Editing
| Title | Improving Translations by Combining Fuzzy-Match Repair with Automatic Post-Editing |
| Authors | John Ortega, Felipe S{'a}nchez-Mart{'\i}nez, Marco Turchi, Matteo Negri |
| Abstract | |
| Tasks | Automatic Post-Editing |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-6625/ |
| https://www.aclweb.org/anthology/W19-6625 | |
| PWC | https://paperswithcode.com/paper/improving-translations-by-combining-fuzzy |
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Speech-based Estimation of Bulbar Regression in Amyotrophic Lateral Sclerosis
| Title | Speech-based Estimation of Bulbar Regression in Amyotrophic Lateral Sclerosis |
| Authors | Alan Wisler, Kristin Teplansky, Jordan Green, Yana Yunusova, Thomas Campbell, Daragh Heitzman, Jun Wang |
| Abstract | Amyotrophic Lateral Sclerosis (ALS) is a progressive neurological disease that leads to degeneration of motor neurons and, as a result, inhibits the ability of the brain to control muscle movements. Monitoring the progression of ALS is of fundamental importance due to the wide variability in disease outlook that exists across patients. This progression is typically tracked using the ALS functional rating scale - revised (ALSFRS-R), which is the current clinical assessment of a patient{'}s level of functional impairment including speech and other motor tasks. In this paper, we investigated automatic estimation of the ALSFRS-R bulbar subscore from acoustic and articulatory movement samples. Experimental results demonstrated the AFSFRS-R bulbar subscore can be predicted from speech samples, which has clinical implication for automatic monitoring of the disease progression of ALS using speech information. |
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| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/W19-1704/ |
| https://www.aclweb.org/anthology/W19-1704 | |
| PWC | https://paperswithcode.com/paper/speech-based-estimation-of-bulbar-regression |
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Transformer-Based Capsule Network For Stock Movement Prediction
| Title | Transformer-Based Capsule Network For Stock Movement Prediction |
| Authors | Jintao Liu, Hongfei Lin, Xikai Liu, Bo Xu, Yuqi Ren, Yufeng Diao, Liang Yang |
| Abstract | |
| Tasks | |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-5511/ |
| https://www.aclweb.org/anthology/W19-5511 | |
| PWC | https://paperswithcode.com/paper/transformer-based-capsule-network-for-stock |
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Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network
| Title | Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network |
| Authors | Wenda Zhao, Bowen Zheng, Qiuhua Lin, Huchuan Lu |
| Abstract | Defocus blur detection (DBD) is a fundamental yet challenging topic, since the homogeneous region is obscure and the transition from the focused area to the unfocused region is gradual. Recent DBD methods make progress through exploring deeper or wider networks with the expense of high memory and computation. In this paper, we propose a novel learning strategy by breaking DBD problem into multiple smaller defocus blur detectors and thus estimate errors can cancel out each other. Our focus is the diversity enhancement via cross-ensemble network. Specifically, we design an end-to-end network composed of two logical parts: feature extractor network (FENet) and defocus blur detector cross-ensemble network (DBD-CENet). FENet is constructed to extract low-level features. Then the features are fed into DBD-CENet containing two parallel-branches for learning two groups of defocus blur detectors. For each individual, we design cross-negative and self-negative correlations and an error function to enhance ensemble diversity and balance individual accuracy. Finally, the multiple defocus blur detectors are combined with a uniformly weighted average to obtain the final DBD map. Experimental results indicate the superiority of our method in terms of accuracy and speed when compared with several state-of-the-art methods. |
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| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Enhancing_Diversity_of_Defocus_Blur_Detectors_via_Cross-Ensemble_Network_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Enhancing_Diversity_of_Defocus_Blur_Detectors_via_Cross-Ensemble_Network_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/enhancing-diversity-of-defocus-blur-detectors |
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On a Chatbot Conducting a Virtual Dialogue in Financial Domain
| Title | On a Chatbot Conducting a Virtual Dialogue in Financial Domain |
| Authors | Boris Galitsky, Dmitry Ilvovsky |
| Abstract | |
| Tasks | Chatbot |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-5517/ |
| https://www.aclweb.org/anthology/W19-5517 | |
| PWC | https://paperswithcode.com/paper/on-a-chatbot-conducting-a-virtual-dialogue-in |
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Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data
| Title | Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data |
| Authors | Yaoyao Zhong, Weihong Deng, Mei Wang, Jiani Hu, Jianteng Peng, Xunqiang Tao, Yaohai Huang |
| Abstract | Large-scale face datasets usually exhibit a massive number of classes, a long-tailed distribution, and severe label noise, which undoubtedly aggravate the difficulty of training. In this paper, we propose a training strategy that treats the head data and the tail data in an unequal way, accompanying with noise-robust loss functions, to take full advantage of their respective characteristics. Specifically, the unequal-training framework provides two training data streams: the first stream applies the head data to learn discriminative face representation supervised by Noise Resistance loss; the second stream applies the tail data to learn auxiliary information by gradually mining the stable discriminative information from confusing tail classes. Consequently, both training streams offer complementary information to deep feature learning. Extensive experiments have demonstrated the effectiveness of the new unequal-training framework and loss functions. Better yet, our method could save a significant amount of GPU memory. With our method, we achieve the best result on MegaFace Challenge 2 (MF2) given a large-scale noisy training data set. |
| Tasks | Face Recognition |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhong_Unequal-Training_for_Deep_Face_Recognition_With_Long-Tailed_Noisy_Data_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhong_Unequal-Training_for_Deep_Face_Recognition_With_Long-Tailed_Noisy_Data_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/unequal-training-for-deep-face-recognition |
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Cross-Lingual Coreference: The Case of Bulgarian and English
| Title | Cross-Lingual Coreference: The Case of Bulgarian and English |
| Authors | Zara Kancheva |
| Abstract | The paper presents several common approaches towards cross- and multi-lingual coreference resolution in a search of the most effective practices to be applied within the work on Bulgarian-English manual coreference annotation of a short story. The work aims at outlining the typology of the differences in the annotated parallel texts. The results of the research prove to be comparable with the tendencies observed in similar works on other Slavic languages and show surprising differences between the types of markables and their frequency in Bulgarian and English. |
| Tasks | Coreference Resolution |
| Published | 2019-09-01 |
| URL | https://www.aclweb.org/anthology/R19-2006/ |
| https://www.aclweb.org/anthology/R19-2006 | |
| PWC | https://paperswithcode.com/paper/cross-lingual-coreference-the-case-of |
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Unraveling Antonym’s Word Vectors through a Siamese-like Network
| Title | Unraveling Antonym’s Word Vectors through a Siamese-like Network |
| Authors | Mathias Etcheverry, Dina Wonsever |
| Abstract | Discriminating antonyms and synonyms is an important NLP task that has the difficulty that both, antonyms and synonyms, contains similar distributional information. Consequently, pairs of antonyms and synonyms may have similar word vectors. We present an approach to unravel antonymy and synonymy from word vectors based on a siamese network inspired approach. The model consists of a two-phase training of the same base network: a pre-training phase according to a siamese model supervised by synonyms and a training phase on antonyms through a siamese-like model that supports the antitransitivity present in antonymy. The approach makes use of the claim that the antonyms in common of a word tend to be synonyms. We show that our approach outperforms distributional and pattern-based approaches, relaying on a simple feed forward network as base network of the training phases. |
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| Published | 2019-07-01 |
| URL | https://www.aclweb.org/anthology/P19-1319/ |
| https://www.aclweb.org/anthology/P19-1319 | |
| PWC | https://paperswithcode.com/paper/unraveling-antonyms-word-vectors-through-a |
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Video Dialog via Progressive Inference and Cross-Transformer
| Title | Video Dialog via Progressive Inference and Cross-Transformer |
| Authors | Weike Jin, Zhou Zhao, Mao Gu, Jun Xiao, Furu Wei, Yueting Zhuang |
| Abstract | Video dialog is a new and challenging task, which requires the agent to answer questions combining video information with dialog history. And different from single-turn video question answering, the additional dialog history is important for video dialog, which often includes contextual information for the question. Existing visual dialog methods mainly use RNN to encode the dialog history as a single vector representation, which might be rough and straightforward. Some more advanced methods utilize hierarchical structure, attention and memory mechanisms, which still lack an explicit reasoning process. In this paper, we introduce a novel progressive inference mechanism for video dialog, which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous. In order to tackle the multi-modal fusion problem, we propose a cross-transformer module, which could learn more fine-grained and comprehensive interactions both inside and between the modalities. And besides answer generation, we also consider question generation, which is more challenging but significant for a complete video dialog system. We evaluate our method on two large-scale datasets, and the extensive experiments show the effectiveness of our method. |
| Tasks | Question Answering, Question Generation, Video Question Answering, Visual Dialog |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1217/ |
| https://www.aclweb.org/anthology/D19-1217 | |
| PWC | https://paperswithcode.com/paper/video-dialog-via-progressive-inference-and |
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Personalized Substitution Ranking for Lexical Simplification
| Title | Personalized Substitution Ranking for Lexical Simplification |
| Authors | John Lee, Chak Yan Yeung |
| Abstract | A lexical simplification (LS) system substitutes difficult words in a text with simpler ones to make it easier for the user to understand. In the typical LS pipeline, the Substitution Ranking step determines the best substitution out of a set of candidates. Most current systems do not consider the user{'}s vocabulary proficiency, and always aim for the simplest candidate. This approach may overlook less-simple candidates that the user can understand, and that are semantically closer to the original word. We propose a personalized approach for Substitution Ranking to identify the candidate that is the closest synonym and is non-complex for the user. In experiments on learners of English at different proficiency levels, we show that this approach enhances the semantic faithfulness of the output, at the cost of a relatively small increase in the number of complex words. |
| Tasks | Lexical Simplification |
| Published | 2019-10-01 |
| URL | https://www.aclweb.org/anthology/W19-8634/ |
| https://www.aclweb.org/anthology/W19-8634 | |
| PWC | https://paperswithcode.com/paper/personalized-substitution-ranking-for-lexical |
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VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization
| Title | VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization |
| Authors | Hyungtak Choi, Lohith Ravuru, Tomasz Dryja{'n}ski, Sunghan Rye, Donghyun Lee, Hojung Lee, Inchul Hwang |
| Abstract | This paper describes our submission to the TL;DR challenge. Neural abstractive summarization models have been successful in generating fluent and consistent summaries with advancements like the copy (Pointer-generator) and coverage mechanisms. However, these models suffer from their extractive nature as they learn to copy words from the source text. In this paper, we propose a novel abstractive model based on Variational Autoencoder (VAE) to address this issue. We also propose a Unified Summarization Framework for the generation of summaries. Our model eliminates non-critical information at a sentence-level with an extractive summarization module and generates the summary word by word using an abstractive summarization module. To implement our framework, we combine submodules with state-of-the-art techniques including Pointer-Generator Network (PGN) and BERT while also using our new VAE-PGN abstractive model. We evaluate our model on the benchmark Reddit corpus as part of the TL;DR challenge and show that our model outperforms the baseline in ROUGE score while generating diverse summaries. |
| Tasks | Abstractive Text Summarization, Text Summarization |
| Published | 2019-10-01 |
| URL | https://www.aclweb.org/anthology/W19-8664/ |
| https://www.aclweb.org/anthology/W19-8664 | |
| PWC | https://paperswithcode.com/paper/vae-pgn-based-abstractive-model-in-multi |
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Neuro-Inspired Eye Tracking With Eye Movement Dynamics
| Title | Neuro-Inspired Eye Tracking With Eye Movement Dynamics |
| Authors | Kang Wang, Hui Su, Qiang Ji |
| Abstract | Generalizing eye tracking to new subjects/environments remains challenging for existing appearance-based methods. To address this issue, we propose to leverage on eye movement dynamics inspired by neurological studies. Studies show that there exist several common eye movement types, independent of viewing contents and subjects, such as fixation, saccade, and smooth pursuits. Incorporating generic eye movement dynamics can therefore improve the generalization capabilities. In particular, we propose a novel Dynamic Gaze Transition Network (DGTN) to capture the underlying eye movement dynamics and serve as the topdown gaze prior. Combined with the bottom-up gaze measurements from the deep convolutional neural network, our method achieves better performance for both within-dataset and cross-dataset evaluations compared to state-of-the-art. In addition, a new DynamicGaze dataset is also constructed to study eye movement dynamics and eye gaze estimation. |
| Tasks | Eye Tracking, Gaze Estimation |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Neuro-Inspired_Eye_Tracking_With_Eye_Movement_Dynamics_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Neuro-Inspired_Eye_Tracking_With_Eye_Movement_Dynamics_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/neuro-inspired-eye-tracking-with-eye-movement |
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Generalizing Eye Tracking With Bayesian Adversarial Learning
| Title | Generalizing Eye Tracking With Bayesian Adversarial Learning |
| Authors | Kang Wang, Rui Zhao, Hui Su, Qiang Ji |
| Abstract | Existing appearance-based gaze estimation approaches with CNN have poor generalization performance. By systematically studying this issue, we identify three major factors: 1) appearance variations; 2) head pose variations and 3) over-fitting issue with point estimation. To improve the generalization performance, we propose to incorporate adversarial learning and Bayesian inference into a unified framework. In particular, we first add an adversarial component into traditional CNN-based gaze estimator so that we can learn features that are gaze-responsive but can generalize to appearance and pose variations. Next, we extend the point-estimation based deterministic model to a Bayesian framework so that gaze estimation can be performed using all parameters instead of only one set of parameters. Besides improved performance on several benchmark datasets, the proposed method also enables online adaptation of the model to new subjects/environments, demonstrating the potential usage for practical real-time eye tracking applications. |
| Tasks | Bayesian Inference, Eye Tracking, Gaze Estimation |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Generalizing_Eye_Tracking_With_Bayesian_Adversarial_Learning_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Generalizing_Eye_Tracking_With_Bayesian_Adversarial_Learning_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/generalizing-eye-tracking-with-bayesian |
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