Papers Task 2
“Task 2” 태그가 달린 논문 572편 · 필터 해제
NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder
In order to provide an explanation of machine learning models, causality detection attracts lots of attention in the artificial intelligence research community. In this paper, we explore the cause-effect detection in fin…
DecoderTask 2ISIKUN at the FinCausal 2020: Linguistically informed Machine-learning Approach for Causality Identification in Financial Documents
This paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of…
Binary ClassificationTask 2UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
In this paper, we describe our method for the detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time pe…
Change DetectionTask 2From Protocol to Screening: A Hybrid Learning Approach for Technology-Assisted Systematic Literature Reviews
In the medical domain, a Systematic Literature Review (SLR) attempts to collect all empirical evidence, that fit pre-specified eligibility criteria, in order to answer a specific research question. The process of prepari…
Learning-To-RankRetrievalSystematic Literature ReviewTAR+1NIT COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets
This paper presents the model submitted by the NIT_COVID-19 team for identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared task addresses the problem of automatically identifying whether an Engli…
Task 2Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples
Unsupervised anomalous sound detection is concerned with identifying sounds that deviate from what is defined as 'normal', without explicitly specifying the types of anomalies. A significant obstacle is the diversity and…
Anomaly DetectionBinary ClassificationDiversityTask 2Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles
Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis. Automated summarization of documents, or g…
Abstractive Text SummarizationArticlesDocument SummarizationMachine Translation+8Transformer-based Multi-Aspect Modeling for Multi-Aspect Multi-Sentiment Analysis
Aspect-based sentiment analysis (ABSA) aims at analyzing the sentiment of a given aspect in a sentence. Recently, neural network-based methods have achieved promising results in existing ABSA datasets. However, these dat…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)SentenceSentiment Analysis+1TATL at WNUT-2020 Task 2: A Transformer-based Baseline System for Identification of Informative COVID-19 English Tweets
As the COVID-19 outbreak continues to spread throughout the world, more and more information about the pandemic has been shared publicly on social media. For example, there are a huge number of COVID-19 English Tweets da…
Task 2NHK_STRL at WNUT-2020 Task 2: GATs with Syntactic Dependencies as Edges and CTC-based Loss for Text Classification
The outbreak of COVID-19 has greatly impacted our daily lives. In these circumstances, it is important to grasp the latest information to avoid causing too much fear and panic. To help grasp new information, extracting i…
Graph AttentionSentenceTask 2text-classification+1NLP North at WNUT-2020 Task 2: Pre-training versus Ensembling for Detection of Informative COVID-19 English Tweets
With the COVID-19 pandemic raging world-wide since the beginning of the 2020 decade, the need for monitoring systems to track relevant information on social media is vitally important. This paper describes our submission…
Task 2Siva at WNUT-2020 Task 2: Fine-tuning Transformer Neural Networks for Identification of Informative Covid-19 Tweets
Social media witnessed vast amounts of misinformation being circulated every day during the Covid-19 pandemic so much so that the WHO Director-General termed the phenomenon as “infodemic.” The ill-effects of such misinfo…
MisinformationTask 2IIITBH at WNUT-2020 Task 2: Exploiting the best of both worlds
In this paper, we present IIITBH team’s effort to solve the second shared task of the 6th Workshop on Noisy User-generated Text (W-NUT)i.e Identification of informative COVID-19 English Tweets. The central theme of the t…
Task 2CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets - RoBERTa Ensembles and The Continued Relevance of Handcrafted Features
This paper presents our submission to Task 2 of the Workshop on Noisy User-generated Text. We explore improving the performance of a pre-trained transformer-based language model fine-tuned for text classification through…
ClassificationLanguage ModelingLanguage ModellingTask 2+2DATAMAFIA at WNUT-2020 Task 2: A Study of Pre-trained Language Models along with Regularization Techniques for Downstream Tasks
This document describes the system description developed by team datamafia at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. This paper contains a thorough study of pre-trained language models o…
Binary ClassificationTask 2UPennHLP at WNUT-2020 Task 2 : Transformer models for classification of COVID19 posts on Twitter
Increasing usage of social media presents new non-traditional avenues for monitoring disease outbreaks, virus transmissions and disease progressions through user posts describing test results or disease symptoms. However…
Task 2UIT-HSE at WNUT-2020 Task 2: Exploiting CT-BERT for Identifying COVID-19 Information on the Twitter Social Network
Recently, COVID-19 has affected a variety of real-life aspects of the world and led to dreadful consequences. More and more tweets about COVID-19 has been shared publicly on Twitter. However, the plurality of those Tweet…
Task 2Emory at WNUT-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification
This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: “Identifi- cation of Informative COVID-19 English Tweet”. Our system explores three recent Transformer- based deep learning models pre…
Task 2CSECU-DSG at WNUT-2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID-19 English Tweets
COVID-19 pandemic has become the trending topic on twitter and people are interested in sharing diverse information ranging from new cases, healthcare guidelines, medicine, and vaccine news. Such information assists the …
Decision MakingSentenceSentence EmbeddingSentence-Embedding+2IRLab@IITBHU at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets using BERT
This paper reports our submission to the shared Task 2: Identification of informative COVID-19 English tweets at W-NUT 2020. We attempted a few techniques, and we briefly explain here two models that showed promising res…
Task 2