Paper Group NANR 40
Flexible information routing in neural populations through stochastic comodulation. GAN Driven Semi-distant Supervision for Relation Extraction. YNU_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining Classification. Identifying Sensible Lexical Relations in Generated Stories. THU_NGN at SemEval-2019 Task 12: Toponym Detection and …
Flexible information routing in neural populations through stochastic comodulation
| Title | Flexible information routing in neural populations through stochastic comodulation |
| Authors | Caroline Haimerl, Cristina Savin, Eero Simoncelli |
| Abstract | Humans and animals are capable of flexibly switching between a multitude of tasks, each requiring rapid, sensory-informed decision making. Incoming stimuli are processed by a hierarchy of neural circuits consisting of millions of neurons with diverse feature selectivity. At any given moment, only a small subset of these carry task-relevant information. In principle, downstream processing stages could identify the relevant neurons through supervised learning, but this would require many example trials. Such extensive learning periods are inconsistent with the observed flexibility of humans or animals, who can adjust to changes in task parameters or structure almost immediately. Here, we propose a novel solution based on functionally-targeted stochastic modulation. It has been observed that trial-to-trial neural activity is modulated by a shared, low-dimensional, stochastic signal that introduces task-irrelevant noise. Counter-intuitively this noise is preferentially targeted towards task-informative neurons, corrupting the encoded signal. However, we hypothesize that this modulation offers a solution to the identification problem, labeling task-informative neurons so as to facilitate decoding. We simulate an encoding population of spiking neurons whose rates are modulated by a shared stochastic signal, and show that a linear decoder with readout weights approximating neuron-specific modulation strength can achieve near-optimal accuracy. Such a decoder allows fast and flexible task-dependent information routing without relying on hardwired knowledge of the task-informative neurons (as in maximum likelihood) or unrealistically many supervised training trials (as in regression). |
| Tasks | Decision Making |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/9584-flexible-information-routing-in-neural-populations-through-stochastic-comodulation |
| http://papers.nips.cc/paper/9584-flexible-information-routing-in-neural-populations-through-stochastic-comodulation.pdf | |
| PWC | https://paperswithcode.com/paper/flexible-information-routing-in-neural |
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GAN Driven Semi-distant Supervision for Relation Extraction
| Title | GAN Driven Semi-distant Supervision for Relation Extraction |
| Authors | Pengshuai Li, Xinsong Zhang, Weijia Jia, Hai Zhao |
| Abstract | Distant supervision has been widely used in relation extraction tasks without hand-labeled datasets recently. However, the automatically constructed datasets comprise numbers of wrongly labeled negative instances due to the incompleteness of knowledge bases, which is neglected by current distant supervised methods resulting in seriously misleading in both training and testing processes. To address this issue, we propose a novel semi-distant supervision approach for relation extraction by constructing a small accurate dataset and properly leveraging numerous instances without relation labels. In our approach, we construct accurate instances by both knowledge base and entity descriptions determined to avoid wrong negative labeling and further utilize unlabeled instances sufficiently using generative adversarial network (GAN) framework. Experimental results on real-world datasets show that our approach can achieve significant improvements in distant supervised relation extraction over strong baselines. |
| Tasks | Relation Extraction |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/N19-1307/ |
| https://www.aclweb.org/anthology/N19-1307 | |
| PWC | https://paperswithcode.com/paper/gan-driven-semi-distant-supervision-for |
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YNU_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining Classification
| Title | YNU_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining Classification |
| Authors | Yunxia Ding, Xiaobing Zhou, Xuejie Zhang |
| Abstract | In this paper we describe a deep-learning system that competed as SemEval 2019 Task 9-SubTask A: Suggestion Mining from Online Reviews and Forums. We use Word2Vec to learn the distributed representations from sentences. This system is composed of a Stacked Bidirectional Long-Short Memory Network (SBiLSTM) for enriching word representations before and after the sequence relationship with context. We perform an ensemble to improve the effectiveness of our model. Our official submission results achieve an F1-score 0.5659. |
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| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2223/ |
| https://www.aclweb.org/anthology/S19-2223 | |
| PWC | https://paperswithcode.com/paper/ynu_dyx-at-semeval-2019-task-9-a-stacked |
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Identifying Sensible Lexical Relations in Generated Stories
| Title | Identifying Sensible Lexical Relations in Generated Stories |
| Authors | Melissa Roemmele |
| Abstract | As with many text generation tasks, the focus of recent progress on story generation has been in producing texts that are perceived to {``}make sense{''} as a whole. There are few automated metrics that address this dimension of story quality even on a shallow lexical level. To initiate investigation into such metrics, we apply a simple approach to identifying word relations that contribute to the {`}narrative sense{'} of a story. We use this approach to comparatively analyze the output of a few notable story generation systems in terms of these relations. We characterize differences in the distributions of relations according to their strength within each story. | |
| Tasks | Text Generation |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/W19-2406/ |
| https://www.aclweb.org/anthology/W19-2406 | |
| PWC | https://paperswithcode.com/paper/identifying-sensible-lexical-relations-in |
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THU_NGN at SemEval-2019 Task 12: Toponym Detection and Disambiguation on Scientific Papers
| Title | THU_NGN at SemEval-2019 Task 12: Toponym Detection and Disambiguation on Scientific Papers |
| Authors | Tao Qi, Suyu Ge, Chuhan Wu, Yubo Chen, Yongfeng Huang |
| Abstract | First name: Tao Last name: Qi Email: taoqi.qt@gmail.com Affiliation: Department of Electronic Engineering, Tsinghua University First name: Suyu Last name: Ge Email: gesy17@mails.tsinghua.edu.cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Chuhan Last name: Wu Email: wuch15@mails.tsinghua.edu.cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Yubo Last name: Chen Email: chen-yb18@mails.tsinghua.edu.cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Yongfeng Last name: Huang Email: yfhuang@mail.tsinghua.edu.cn Affiliation: Department of Electronic Engineering, Tsinghua University Toponym resolution is an important and challenging task in the neural language processing field, and has wide applications such as emergency response and social media geographical event analysis. Toponym resolution can be roughly divided into two independent steps, i.e., toponym detection and toponym disambiguation. In order to facilitate the study on toponym resolution, the SemEval 2019 task 12 is proposed, which contains three subtasks, i.e., toponym detection, toponym disambiguation and toponym resolution. In this paper, we introduce our system that participated in the SemEval 2019 task 12. For toponym detection, in our approach we use TagLM as the basic model, and explore the use of various features in this task, such as word embeddings extracted from pre-trained language models, POS tags and lexical features extracted from dictionaries. For toponym disambiguation, we propose a heuristics rule-based method using toponym frequency and population. Our systems achieved 83.03{%} strict macro F1, 74.50 strict micro F1, 85.92 overlap macro F1 and 78.47 overlap micro F1 in toponym detection subtask. |
| Tasks | Word Embeddings |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2229/ |
| https://www.aclweb.org/anthology/S19-2229 | |
| PWC | https://paperswithcode.com/paper/thu_ngn-at-semeval-2019-task-12-toponym |
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Cross-Linguistic Semantic Annotation: Reconciling the Language-Specific and the Universal
| Title | Cross-Linguistic Semantic Annotation: Reconciling the Language-Specific and the Universal |
| Authors | Jens E. L. Van Gysel, Meagan Vigus, Pavlina Kalm, Sook-kyung Lee, Michael Regan, William Croft |
| Abstract | Developers of cross-linguistic semantic annotation schemes face a number of issues not encountered in monolingual annotation. This paper discusses four such issues, related to the establishment of annotation labels, and the treatment of languages with more fine-grained, more coarse-grained, and cross-cutting categories. We propose that a lattice-like architecture of the annotation categories can adequately handle all four issues, and at the same time remain both intuitive for annotators and faithful to typological insights. This position is supported by a brief annotation experiment. |
| Tasks | |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-3301/ |
| https://www.aclweb.org/anthology/W19-3301 | |
| PWC | https://paperswithcode.com/paper/cross-linguistic-semantic-annotation |
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Using Snomed to recognize and index chemical and drug mentions.
| Title | Using Snomed to recognize and index chemical and drug mentions. |
| Authors | Pilar L{'o}pez {'U}beda, Manuel Carlos D{'\i}az Galiano, L. Alfonso Urena Lopez, Maite Martin |
| Abstract | In this paper we describe a new named entity extraction system. Our work proposes a system for the identification and annotation of drug names in Spanish biomedical texts based on machine learning and deep learning models. Subsequently, a standardized code using Snomed is assigned to these drugs, for this purpose, Natural Language Processing tools and techniques have been used, and a dictionary of different sources of information has been built. The results are promising, we obtain 78{%} in F1 score on the first sub-track and in the second task we map with Snomed correctly 72{%} of the found entities. |
| Tasks | Entity Extraction |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5718/ |
| https://www.aclweb.org/anthology/D19-5718 | |
| PWC | https://paperswithcode.com/paper/using-snomed-to-recognize-and-index-chemical |
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Bacteria Biotope at BioNLP Open Shared Tasks 2019
| Title | Bacteria Biotope at BioNLP Open Shared Tasks 2019 |
| Authors | Robert Bossy, Louise Del{'e}ger, Estelle Chaix, Mouhamadou Ba, Claire N{'e}dellec |
| Abstract | This paper presents the fourth edition of the Bacteria Biotope task at BioNLP Open Shared Tasks 2019. The task focuses on the extraction of the locations and phenotypes of microorganisms from PubMed abstracts and full-text excerpts, and the characterization of these entities with respect to reference knowledge sources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by the importance of the knowledge on biodiversity for fundamental research and applications in microbiology. The paper describes the different proposed subtasks, the corpus characteristics, and the challenge organization. We also provide an analysis of the results obtained by participants, and inspect the evolution of the results since the last edition in 2016. |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5719/ |
| https://www.aclweb.org/anthology/D19-5719 | |
| PWC | https://paperswithcode.com/paper/bacteria-biotope-at-bionlp-open-shared-tasks |
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Team Peter Brinkmann at SemEval-2019 Task 4: Detecting Biased News Articles Using Convolutional Neural Networks
| Title | Team Peter Brinkmann at SemEval-2019 Task 4: Detecting Biased News Articles Using Convolutional Neural Networks |
| Authors | Michael F{"a}rber, Agon Qurdina, Lule Ahmedi |
| Abstract | In this paper, we present an approach for classifying news articles as biased (i.e., hyperpartisan) or unbiased, based on a convolutional neural network. We experiment with various embedding methods (pretrained and trained on the training dataset) and variations of the convolutional neural network architecture and compare the results. When evaluating our best performing approach on the actual test data set of the SemEval 2019 Task 4, we obtained relatively low precision and accuracy values, while gaining the highest recall rate among all 42 participating teams. |
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| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2180/ |
| https://www.aclweb.org/anthology/S19-2180 | |
| PWC | https://paperswithcode.com/paper/team-peter-brinkmann-at-semeval-2019-task-4 |
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Local Stability and Performance of Simple Gradient Penalty $\mu$-Wasserstein GAN
| Title | Local Stability and Performance of Simple Gradient Penalty $\mu$-Wasserstein GAN |
| Authors | Cheolhyeong Kim, Seungtae Park, Hyung Ju Hwang |
| Abstract | Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution. Recent studies have proposed stabilizing the training process for the WGAN and implementing the Lipschitz constraint. In this study, we prove the local stability of optimizing the simple gradient penalty $\mu$-WGAN(SGP $\mu$-WGAN) under suitable assumptions regarding the equilibrium and penalty measure $\mu$. The measure valued differentiation concept is employed to deal with the derivative of the penalty terms, which is helpful for handling abstract singular measures with lower dimensional support. Based on this analysis, we claim that penalizing the data manifold or sample manifold is the key to regularizing the original WGAN with a gradient penalty. Experimental results obtained with unintuitive penalty measures that satisfy our assumptions are also provided to support our theoretical results. |
| Tasks | |
| Published | 2019-05-01 |
| URL | https://openreview.net/forum?id=H1ecDoR5Y7 |
| https://openreview.net/pdf?id=H1ecDoR5Y7 | |
| PWC | https://paperswithcode.com/paper/local-stability-and-performance-of-simple-1 |
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Meta-Learning to Detect Rare Objects
| Title | Meta-Learning to Detect Rare Objects |
| Authors | Yu-Xiong Wang, Deva Ramanan, Martial Hebert |
| Abstract | Few-shot learning, i.e., learning novel concepts from few examples, is fundamental to practical visual recognition systems. While most of existing work has focused on few-shot classification, we make a step towards few-shot object detection, a more challenging yet under-explored task. We develop a conceptually simple but powerful meta-learning based framework that simultaneously tackles few-shot classification and few-shot localization in a unified, coherent way. This framework leverages meta-level knowledge about “model parameter generation” from base classes with abundant data to facilitate the generation of a detector for novel classes. Our key insight is to disentangle the learning of category-agnostic and category-specific components in a CNN based detection model. In particular, we introduce a weight prediction meta-model that enables predicting the parameters of category-specific components from few examples. We systematically benchmark the performance of modern detectors in the small-sample size regime. Experiments in a variety of realistic scenarios, including within-domain, cross-domain, and long-tailed settings, demonstrate the effectiveness and generality of our approach under different notions of novel classes. |
| Tasks | Few-Shot Learning, Few-Shot Object Detection, Meta-Learning, Object Detection |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Meta-Learning_to_Detect_Rare_Objects_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Meta-Learning_to_Detect_Rare_Objects_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/meta-learning-to-detect-rare-objects |
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RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection
| Title | RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection |
| Authors | Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, Alex M. Bronstein |
| Abstract | Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples. In this work, we propose a new method for DML that simultaneously learns the backbone network parameters, the embedding space, and the multi-modal distribution of each of the training categories in that space, in a single end-to-end training process. Our approach outperforms state-of-the-art methods for DML-based object classification on a variety of standard fine-grained datasets. Furthermore, we demonstrate the effectiveness of our approach on the problem of few-shot object detection, by incorporating the proposed DML architecture as a classification head into a standard object detection model. We achieve the best results on the ImageNet-LOC dataset compared to strong baselines, when only a few training examples are available. We also offer the community a new episodic benchmark based on the ImageNet dataset for the few-shot object detection task. |
| Tasks | Few-Shot Object Detection, Metric Learning, Object Classification, Object Detection |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Karlinsky_RepMet_Representative-Based_Metric_Learning_for_Classification_and_Few-Shot_Object_Detection_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Karlinsky_RepMet_Representative-Based_Metric_Learning_for_Classification_and_Few-Shot_Object_Detection_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/repmet-representative-based-metric-learning-1 |
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DEFT: A corpus for definition extraction in free- and semi-structured text
| Title | DEFT: A corpus for definition extraction in free- and semi-structured text |
| Authors | Sasha Spala, Nicholas A. Miller, Yiming Yang, Franck Dernoncourt, Carl Dockhorn |
| Abstract | Definition extraction has been a popular topic in NLP research for well more than a decade, but has been historically limited to well-defined, structured, and narrow conditions. In reality, natural language is messy, and messy data requires both complex solutions and data that reflects that reality. In this paper, we present a robust English corpus and annotation schema that allows us to explore the less straightforward examples of term-definition structures in free and semi-structured text. |
| Tasks | |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-4015/ |
| https://www.aclweb.org/anthology/W19-4015 | |
| PWC | https://paperswithcode.com/paper/deft-a-corpus-for-definition-extraction-in |
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I Know the Feeling: Learning to Converse with Empathy
| Title | I Know the Feeling: Learning to Converse with Empathy |
| Authors | Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau |
| Abstract | Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling. One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill that is trivial for humans. Research in this area is made difficult by the paucity of suitable publicly available datasets both for emotion and dialogues. This work proposes a new task for empathetic dialogue generation and EmpatheticDialogues, a dataset of 25k conversations grounded in emotional situations to facilitate training and evaluating dialogue systems. Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, while improving on other metrics as well (e.g. perceived relevance of responses, BLEU scores), compared to models merely trained on large-scale Internet conversation data. We also present empirical comparisons of several ways to improve the performance of a given model by leveraging existing models or datasets without requiring lengthy re-training of the full model. |
| Tasks | Dialogue Generation |
| Published | 2019-05-01 |
| URL | https://openreview.net/forum?id=HyesW2C9YQ |
| https://openreview.net/pdf?id=HyesW2C9YQ | |
| PWC | https://paperswithcode.com/paper/i-know-the-feeling-learning-to-converse-with-1 |
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Sparse High-Dimensional Isotonic Regression
| Title | Sparse High-Dimensional Isotonic Regression |
| Authors | David Gamarnik, Julia Gaudio |
| Abstract | We consider the problem of estimating an unknown coordinate-wise monotone function given noisy measurements, known as the isotonic regression problem. Often, only a small subset of the features affects the output. This motivates the sparse isotonic regression setting, which we consider here. We provide an upper bound on the expected VC entropy of the space of sparse coordinate-wise monotone functions, and identify the regime of statistical consistency of our estimator. We also propose a linear program to recover the active coordinates, and provide theoretical recovery guarantees. We close with experiments on cancer classification, and show that our method significantly outperforms several standard methods. |
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| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/9447-sparse-high-dimensional-isotonic-regression |
| http://papers.nips.cc/paper/9447-sparse-high-dimensional-isotonic-regression.pdf | |
| PWC | https://paperswithcode.com/paper/sparse-high-dimensional-isotonic-regression |
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