Papers Task 2
“Task 2” 태그가 달린 논문 572편 · 필터 해제
DSC-IIT ISM at WNUT-2020 Task 2: Detection of COVID-19 informative tweets using RoBERTa
Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative con…
Task 2Linguist Geeks on WNUT-2020 Task 2: COVID-19 Informative Tweet Identification using Progressive Trained Language Models and Data Augmentation
Since the outbreak of COVID-19, there has been a surge of digital content on social media. The content ranges from news articles, academic reports, tweets, videos, and even memes. Among such an overabundance of data, it …
ArticlesData AugmentationTask 2Transfer LearningNLPRL at WNUT-2020 Task 2: ELMo-based System for Identification of COVID-19 Tweets
The Coronavirus pandemic has been a dominating news on social media for the last many months. Efforts are being made to reduce its spread and reduce the casualties as well as new infections. For this purpose, the informa…
Binary ClassificationManagementTask 2SU-NLP at WNUT-2020 Task 2: The Ensemble Models
In this paper, we address the problem of identifying informative tweets related to COVID-19 in the form of a binary classification task as part of our submission for W-NUT 2020 Task 2. Specifically, we focus on ensemblin…
Binary ClassificationClassificationTask 2IDSOU at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
We introduce the IDSOU submission for the WNUT-2020 task 2: identification of informative COVID-19 English Tweets. Our system is an ensemble of pre-trained language models such as BERT. We ranked 16th in the F1 score.
Task 2ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents
Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions? We present Gapformer, which effectively classifies content as informative or not. It reform…
Graph ClassificationLanguage ModelingLanguage ModellingTask 2NIT_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 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 English t…
Task 2#GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related Tweets
In this system paper, we present a transformer-based approach to the detection of informativeness in English tweets on the topic of the current COVID-19 pandemic. Our models distinguish informative tweets, i.e. tweets co…
InformativenessTask 2Not-NUTs at WNUT-2020 Task 2: A BERT-based System in Identifying Informative COVID-19 English Tweets
As of 2020 when the COVID-19 pandemic is full-blown on a global scale, people’s need to have access to legitimate information regarding COVID-19 is more urgent than ever, especially via online media where the abundance o…
Task 2UET at WNUT-2020 Task 2: A Study of Combining Transfer Learning Methods for Text Classification with RoBERTa
This paper reports our approach and the results of our experiments for W-NUT task 2: Identification of Informative COVID-19 English Tweets. In this paper, we test out the effectiveness of transfer learning method with st…
Task 2text-classificationText ClassificationTransfer LearningSunBear at WNUT-2020 Task 2: Improving BERT-Based Noisy Text Classification with Knowledge of the Data domain
This paper proposes an improved custom model for WNUT task 2: Identification of Informative COVID-19 English Tweet. We improve experiment with the effectiveness of fine-tuning methodologies for state-of-the-art language …
Language ModelingLanguage ModellingTask 2text-classification+1ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings
This paper presents Iswara’s participation in the WNUT-2020 Task 2 “Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings”,which tries to classify whether a certain tweet is considered …
Task 2Word EmbeddingsBERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task
This paper presents our submission to the WMT2020 Shared Task on Quality Estimation (QE). We participate in Task and Task 2 focusing on sentence-level prediction. We explore (a) a black-box approach to QE based on pre-tr…
SentenceTask 2NICT Kyoto Submission for the WMT’20 Quality Estimation Task: Intermediate Training for Domain and Task Adaptation
This paper describes the NICT Kyoto submission for the WMT’20 Quality Estimation (QE) shared task. We participated in Task 2: Word and Sentence-level Post-editing Effort, which involved Wikipedia data and two translation…
Domain AdaptationLanguage ModelingLanguage ModellingMachine Translation+4Tencent submission for WMT20 Quality Estimation Shared Task
This paper presents Tencent’s submission to the WMT20 Quality Estimation (QE) Shared Task: Sentence-Level Post-editing Effort for English-Chinese in Task 2. Our system ensembles two architectures, XLM-based and Transform…
Machine TranslationSentenceTask 2TranslationWNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets. We describe how we construct a corpus of 10K Tweets and organize the development and evalua…
Task 2Text ClassificationCXP949 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…
ClassificationGeneral ClassificationLanguage ModelingLanguage Modelling+3InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction
Identifying informative tweets is an important step when building information extraction systems based on social media. WNUT-2020 Task 2 was organised to recognise informative tweets from noise tweets. In this paper, we …
Task 2NutCracker at WNUT-2020 Task 2: Robustly Identifying Informative COVID-19 Tweets using Ensembling and Adversarial Training
We experiment with COVID-Twitter-BERT and RoBERTa models to identify informative COVID-19 tweets. We further experiment with adversarial training to make our models robust. The ensemble of COVID-Twitter-BERT and RoBERTa …
Task 2Text ClassificationBaseline System of Voice Conversion Challenge 2020 with Cyclic Variational Autoencoder and Parallel WaveGAN
In this paper, we present a description of the baseline system of Voice Conversion Challenge (VCC) 2020 with a cyclic variational autoencoder (CycleVAE) and Parallel WaveGAN (PWG), i.e., CycleVAEPWG. CycleVAE is a nonpar…
Generative Adversarial NetworkTask 2Voice Conversion