Paper Group NANR 241
Multi-object Tracking with Neural Gating Using Bilinear LSTM. Learning Scalar Adjective Intensity from Paraphrases. UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions. SemEval-2018 Task 9: Hypernym Discovery. TRAVERSAL at PARSEME Shared Task 2018: Identification of Verbal Multiword Expressions Using a Discrimin …
Multi-object Tracking with Neural Gating Using Bilinear LSTM
| Title | Multi-object Tracking with Neural Gating Using Bilinear LSTM |
| Authors | Chanho Kim, Fuxin Li, James M. Rehg |
| Abstract | In recent deep online and near-online multi-object tracking approaches, a difficulty has been to incorporate long-term appearance models to efficiently score object tracks under severe occlusion and multiple missing detections. In this paper, we propose a novel recurrent network model, the bilinear LSTM, in order to improve long-term appearance models via a recurrent network. Based on intuitions drawn from recursive least squares, bilinear LSTM stores building blocks of a linear predictor in its memory, which is then coupled with the input in a multiplicative manner, instead of the additive coupling in conventional LSTM approaches. Such coupling resembles an online learned classifier/regressor at each time step, which we have found to improve performances in using LSTM for appearance modeling. We also propose novel data augmentation approaches to efficiently train recurrent models that score object tracks on both appearance and motion. We train an LSTM that can score object tracks based on both appearance and motion and utilize it in a multiple hypothesis tracking framework. In experiments, we show that with our novel LSTM model, we achieved state-of-the-art performance on near-online multiple object tracking on the MOT 2016 and MOT 2017 benchmarks. |
| Tasks | Data Augmentation, Multi-Object Tracking, Multiple Object Tracking, Object Tracking, Online Multi-Object Tracking |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Chanho_Kim_Multi-object_Tracking_with_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Chanho_Kim_Multi-object_Tracking_with_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/multi-object-tracking-with-neural-gating |
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Learning Scalar Adjective Intensity from Paraphrases
| Title | Learning Scalar Adjective Intensity from Paraphrases |
| Authors | Anne Cocos, Skyler Wharton, Ellie Pavlick, Marianna Apidianaki, Chris Callison-Burch |
| Abstract | Adjectives like {}warm{''}, {}hot{''}, and {}scalding{''} all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrase-based method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair {}really hot{''} {\textless}{–}{\textgreater} {}scalding{''} suggests that {}hot{''} {\textless} {}scalding{''}. We show that combining this paraphrase evidence with existing, complementary pattern- and lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to {}yes/no{''} questions. |
| Tasks | Hypernym Discovery, Question Answering, Sentiment Analysis |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1202/ |
| https://www.aclweb.org/anthology/D18-1202 | |
| PWC | https://paperswithcode.com/paper/learning-scalar-adjective-intensity-from |
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UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions
| Title | UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions |
| Authors | Tom{'a}{\v{s}} Brychc{'\i}n, Tom{'a}{\v{s}} Hercig, Josef Steinberger, Michal Konkol |
| Abstract | We present our UWB system for the task of capturing discriminative attributes at SemEval 2018. Given two words and an attribute, the system decides, whether this attribute is discriminative between the words or not. Assuming Distributional Hypothesis, i.e., a word meaning is related to the distribution across contexts, we introduce several approaches to compare word contextual information. We experiment with state-of-the-art semantic spaces and with simple co-occurrence statistics. We show the word distribution in the corpus has potential for detecting discriminative attributes. Our system achieves F1 score 72.1{%} and is ranked {#}4 among 26 submitted systems. |
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| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1153/ |
| https://www.aclweb.org/anthology/S18-1153 | |
| PWC | https://paperswithcode.com/paper/uwb-at-semeval-2018-task-10-capturing |
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SemEval-2018 Task 9: Hypernym Discovery
| Title | SemEval-2018 Task 9: Hypernym Discovery |
| Authors | Jose Camacho-Collados, Claudio Delli Bovi, Luis Espinosa-Anke, Sergio Oramas, Tommaso Pasini, Enrico Santus, Vered Shwartz, Roberto Navigli, Horacio Saggion |
| Abstract | This paper describes the SemEval 2018 Shared Task on Hypernym Discovery. We put forward this task as a complementary benchmark for modeling hypernymy, a problem which has traditionally been cast as a binary classification task, taking a pair of candidate words as input. Instead, our reformulated task is defined as follows: given an input term, retrieve (or discover) its suitable hypernyms from a target corpus. We proposed five different subtasks covering three languages (English, Spanish, and Italian), and two specific domains of knowledge in English (Medical and Music). Participants were allowed to compete in any or all of the subtasks. Overall, a total of 11 teams participated, with a total of 39 different systems submitted through all subtasks. Data, results and further information about the task can be found at \url{https://competitions.codalab.org/competitions/17119}. |
| Tasks | Hypernym Discovery, Natural Language Inference, Question Answering |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1115/ |
| https://www.aclweb.org/anthology/S18-1115 | |
| PWC | https://paperswithcode.com/paper/semeval-2018-task-9-hypernym-discovery |
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TRAVERSAL at PARSEME Shared Task 2018: Identification of Verbal Multiword Expressions Using a Discriminative Tree-Structured Model
| Title | TRAVERSAL at PARSEME Shared Task 2018: Identification of Verbal Multiword Expressions Using a Discriminative Tree-Structured Model |
| Authors | Jakub Waszczuk |
| Abstract | This paper describes a system submitted to the closed track of the PARSEME shared task (edition 1.1) on automatic identification of verbal multiword expressions (VMWEs). The system represents VMWE identification as a labeling task where one of two labels (MWE or not-MWE) must be predicted for each node in the dependency tree based on local context, including adjacent nodes and their labels. The system relies on multiclass logistic regression to determine the globally optimal labeling of a tree. The system ranked 1st in the general cross-lingual ranking of the closed track systems, according to both official evaluation measures: MWE-based F1 and token-based F1. |
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| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4931/ |
| https://www.aclweb.org/anthology/W18-4931 | |
| PWC | https://paperswithcode.com/paper/traversal-at-parseme-shared-task-2018 |
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Systematic Error Analysis of the Stanford Question Answering Dataset
| Title | Systematic Error Analysis of the Stanford Question Answering Dataset |
| Authors | Marc-Antoine Rondeau, T. J. Hazen |
| Abstract | We analyzed the outputs of multiple question answering (QA) models applied to the Stanford Question Answering Dataset (SQuAD) to identify the core challenges for QA systems on this data set. Through an iterative process, challenging aspects were hypothesized through qualitative analysis of the common error cases. A classifier was then constructed to predict whether SQuAD test examples were likely to be difficult for systems to answer based on features associated with the hypothesized aspects. The classifier{'}s performance was used to accept or reject each aspect as an indicator of difficulty. With this approach, we ensured that our hypotheses were systematically tested and not simply accepted based on our pre-existing biases. Our explanations are not accepted based on human evaluation of individual examples. This process also enabled us to identify the primary QA strategy learned by the models, i.e., systems determined the acceptable answer type for a question and then selected the acceptable answer span of that type containing the highest density of words present in the question within its local vicinity in the passage. |
| Tasks | Common Sense Reasoning, Machine Reading Comprehension, Question Answering, Reading Comprehension |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-2602/ |
| https://www.aclweb.org/anthology/W18-2602 | |
| PWC | https://paperswithcode.com/paper/systematic-error-analysis-of-the-stanford |
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Investigating Effective Parameters for Fine-tuning of Word Embeddings Using Only a Small Corpus
| Title | Investigating Effective Parameters for Fine-tuning of Word Embeddings Using Only a Small Corpus |
| Authors | Kanako Komiya, Hiroyuki Shinnou |
| Abstract | Fine-tuning is a popular method to achieve better performance when only a small target corpus is available. However, it requires tuning of a number of metaparameters and thus it might carry risk of adverse effect when inappropriate metaparameters are used. Therefore, we investigate effective parameters for fine-tuning when only a small target corpus is available. In the current study, we target at improving Japanese word embeddings created from a huge corpus. First, we demonstrate that even the word embeddings created from the huge corpus are affected by domain shift. After that, we investigate effective parameters for fine-tuning of the word embeddings using a small target corpus. We used perplexity of a language model obtained from a Long Short-Term Memory network to assess the word embeddings input into the network. The experiments revealed that fine-tuning sometimes give adverse effect when only a small target corpus is used and batch size is the most important parameter for fine-tuning. In addition, we confirmed that effect of fine-tuning is higher when size of a target corpus was larger. |
| Tasks | Language Modelling, Word Embeddings |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-3408/ |
| https://www.aclweb.org/anthology/W18-3408 | |
| PWC | https://paperswithcode.com/paper/investigating-effective-parameters-for-fine |
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Linguistic Features of Helpfulness in Automated Support for Creative Writing
| Title | Linguistic Features of Helpfulness in Automated Support for Creative Writing |
| Authors | Melissa Roemmele, Andrew Gordon |
| Abstract | We examine an emerging NLP application that supports creative writing by automatically suggesting continuing sentences in a story. The application tracks users{'} modifications to generated sentences, which can be used to quantify their {``}helpfulness{''} in advancing the story. We explore the task of predicting helpfulness based on automatically detected linguistic features of the suggestions. We illustrate this analysis on a set of user interactions with the application using an initial selection of features relevant to story generation. | |
| Tasks | Grammatical Error Correction, Text Generation |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-1502/ |
| https://www.aclweb.org/anthology/W18-1502 | |
| PWC | https://paperswithcode.com/paper/linguistic-features-of-helpfulness-in |
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A Taxonomy for In-depth Evaluation of Normalization for User Generated Content
| Title | A Taxonomy for In-depth Evaluation of Normalization for User Generated Content |
| Authors | Rob van der Goot, Rik van Noord, Gertjan van Noord |
| Abstract | |
| Tasks | Grammatical Error Correction, Lexical Normalization, Machine Translation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1109/ |
| https://www.aclweb.org/anthology/L18-1109 | |
| PWC | https://paperswithcode.com/paper/a-taxonomy-for-in-depth-evaluation-of |
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Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods
| Title | Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods |
| Authors | Vivi Nastase, Julian Hitschler |
| Abstract | |
| Tasks | Grammatical Error Correction, Keyword Extraction, Machine Translation, Optical Character Recognition, Relation Extraction |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1113/ |
| https://www.aclweb.org/anthology/L18-1113 | |
| PWC | https://paperswithcode.com/paper/correction-of-ocr-word-segmentation-errors-in |
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A Computational Exploration of Exaggeration
| Title | A Computational Exploration of Exaggeration |
| Authors | Enrica Troiano, Carlo Strapparava, G{"o}zde {"O}zbal, Serra Sinem Tekiro{\u{g}}lu |
| Abstract | Several NLP studies address the problem of figurative language, but among non-literal phenomena, they have neglected exaggeration. This paper presents a first computational approach to this figure of speech. We explore the possibility to automatically detect exaggerated sentences. First, we introduce HYPO, a corpus containing overstatements (or hyperboles) collected on the web and validated via crowdsourcing. Then, we evaluate a number of models trained on HYPO, and bring evidence that the task of hyperbole identification can be successfully performed based on a small set of semantic features. |
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| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1367/ |
| https://www.aclweb.org/anthology/D18-1367 | |
| PWC | https://paperswithcode.com/paper/a-computational-exploration-of-exaggeration |
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FooTweets: A Bilingual Parallel Corpus of World Cup Tweets
| Title | FooTweets: A Bilingual Parallel Corpus of World Cup Tweets |
| Authors | Henny Sluyter-G{"a}thje, Pintu Lohar, Haithem Afli, Andy Way |
| Abstract | |
| Tasks | Machine Translation, Sentiment Analysis |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1422/ |
| https://www.aclweb.org/anthology/L18-1422 | |
| PWC | https://paperswithcode.com/paper/footweets-a-bilingual-parallel-corpus-of |
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Morphology Injection for English-Malayalam Statistical Machine Translation
| Title | Morphology Injection for English-Malayalam Statistical Machine Translation |
| Authors | Sreelekha S, Pushpak Bhattacharyya |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1413/ |
| https://www.aclweb.org/anthology/L18-1413 | |
| PWC | https://paperswithcode.com/paper/morphology-injection-for-english-malayalam |
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An Evaluation Framework for Multimodal Interaction
| Title | An Evaluation Framework for Multimodal Interaction |
| Authors | Nikhil Krishnaswamy, James Pustejovsky |
| Abstract | |
| Tasks | Gesture Recognition, Motion Capture |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1335/ |
| https://www.aclweb.org/anthology/L18-1335 | |
| PWC | https://paperswithcode.com/paper/an-evaluation-framework-for-multimodal |
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3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare
| Title | 3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare |
| Authors | Abhijit Kundu, Yin Li, James M. Rehg |
| Abstract | We present a fast inverse-graphics framework for instance-level 3D scene understanding. We train a deep convolutional network that learns to map image regions to the full 3D shape and pose of all object instances in the image. Our method produces a compact 3D representation of the scene, which can be readily used for applications like autonomous driving. Many traditional 2D vision outputs, like instance segmentations and depth-maps, can be obtained by simply rendering our output 3D scene model. We exploit class-specific shape priors by learning a low dimensional shape-space from collections of CAD models. We present novel representations of shape and pose, that strive towards better 3D equivariance and generalization. In order to exploit rich supervisory signals in the form of 2D annotations like segmentation, we propose a differentiable Render-and-Compare loss that allows 3D shape and pose to be learned with 2D supervision. We evaluate our method on the challenging real-world datasets of Pascal3D+ and KITTI, where we achieve state-of-the-art results. |
| Tasks | 3D Object Reconstruction, Autonomous Driving, Object Reconstruction, Scene Understanding |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Kundu_3D-RCNN_Instance-Level_3D_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Kundu_3D-RCNN_Instance-Level_3D_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/3d-rcnn-instance-level-3d-object |
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