Papers Task 2
“Task 2” 태그가 달린 논문 572편 · 필터 해제
KU-CST at the SIGMORPHON 2020 Task 2 on Unsupervised Morphological Paradigm Completion
We present a model for the unsupervised dis- covery of morphological paradigms. The goal of this model is to induce morphological paradigms from the bible (raw text) and a list of lemmas. We have created a model that spl…
LEMMATask 2Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa
This paper presents our approach for task 2 and task 3 of Social Media Mining for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate adverse drug reaction (ADR) tweets from nonADR tweets and is treated…
Binary ClassificationGeneral ClassificationLanguage ModelingLanguage Modelling+2Automated Optical Multi-layer Design via Deep Reinforcement Learning
Optical multi-layer thin films are widely used in optical and energy applications requiring photonic designs. Engineers often design such structures based on their physical intuition. However, solely relying on human exp…
Deep Reinforcement LearningPhysical Intuitionreinforcement-learningReinforcement Learning+2The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2
We describe the NYU-CUBoulder systems for the SIGMORPHON 2020 Task 0 on typologically diverse morphological inflection and Task 2 on unsupervised morphological paradigm completion. The former consists of generating morph…
LEMMAMorphological InflectionTask 2Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds
This technical report describes two methods that were developed for Task 2 of the DCASE 2020 challenge. The challenge involves an unsupervised learning to detect anomalous sounds, thus only normal machine working conditi…
Anomaly DetectionTask 2Unsupervised Anomaly DetectionTeam RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning
Detecting meaningful events in an untrimmed video is essential for dense video captioning. In this work, we propose a novel and simple model for event sequence generation and explore temporal relationships of the event s…
Dense CaptioningDense Video CaptioningTask 2Video CaptioningDescription and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
In this paper, we present the task description and discuss the results of the DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring. The goal of anomalous sound detectio…
Task 2Spoken dialect identification in Twitter using a multi-filter architecture
This paper presents our approach for SwissText & KONVENS 2020 shared task 2, which is a multi-stage neural model for Swiss German (GSW) identification on Twitter. Our model outputs either GSW or non-GSW and is not meant …
Dialect IdentificationTask 2An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events
In times of crisis, identifying the essential needs is a crucial step to providing appropriate resources and services to affected entities. Social media platforms such as Twitter contain vast amount of information about …
Task 2BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features based Ensemble Learning
Propaganda spreads the ideology and beliefs of like-minded people, brainwashing their audiences, and sometimes leading to violence. SemEval 2020 Task-11 aims to design automated systems for news propaganda detection. Tas…
ArticlesEnsemble LearningPropaganda detectionTask 2The SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion
In this paper, we describe the findings of the SIGMORPHON 2020 shared task on unsupervised morphological paradigm completion (SIGMORPHON 2020 Task 2), a novel task in the field of inflectional morphology. Participants we…
LEMMATask 2The IMS-CUBoulder System for the SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion
In this paper, we present the systems of the University of Stuttgart IMS and the University of Colorado Boulder (IMS-CUBoulder) for SIGMORPHON 2020 Task 2 on unsupervised morphological paradigm completion (Kann et al., 2…
Task 2Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfactual Statement Analysis
In this paper, we explore strategies to detect and evaluate counterfactual sentences. We describe our system for SemEval-2020 Task 5: Modeling Causal Reasoning in Language: Detecting Counterfactuals. We use a BERT base m…
counterfactualGeneral ClassificationLanguage ModelingLanguage Modelling+1Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors
In this system demonstration paper, we present an open-source web-based application with a responsive design for modular semantic frame annotation (SFA). Besides letting experienced and inexperienced users do suggestion-…
Active LearningTask 2CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media
We describe the third edition of the CheckThat! Lab, which is part of the 2020 Cross-Language Evaluation Forum (CLEF). CheckThat! proposes four complementary tasks and a related task from previous lab editions, offered i…
Fact CheckingTask 2ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard
Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, Chinese has more than 6000 commonly used characters and Chinesecharacte…
Line DetectionTask 2SemEval-2013 Task 2: Sentiment Analysis in Twitter
In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hindered by the lack of suitable datasets, com…
Sentiment AnalysisTask 2Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction
Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision. Most prior works adopt a selective attention mechanism over sentences in a bag to deno…
Entity EmbeddingsRelationRelation ClassificationRelation Extraction+2GLMNet: Graph Learning-Matching Networks for Feature Matching
Recently, graph convolutional networks (GCNs) have shown great potential for the task of graph matching. It can integrate graph node feature embedding, node-wise affinity learning and matching optimization together in a …
Graph LearningGraph MatchingTask 2A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models
We present a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI), which links them to concept unique identifiers (CUIs) in a knowledge base, for the PharmaCoNER shared task on …
Entity Embeddingsnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+2