Papers Task 2
“Task 2” 태그가 달린 논문 572편 · 필터 해제
UAlberta at SemEval-2020 Task 2: Using Translations to Predict Cross-Lingual Entailment
We investigate the hypothesis that translations can be used to identify cross-lingual lexical entailment. We propose novel methods that leverage parallel corpora, word embeddings, and multilingual lexical resources. Our …
Lexical EntailmentTask 2Word EmbeddingsHIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection
We describe our system for Task 5 of SemEval 2020: Modelling Causal Reasoning in Language: Detecting Counterfactuals. Despite deep learning has achieved significant success in many fields, it still hardly drives today{'}…
counterfactualLanguage ModelingLanguage ModellingTask 2Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers
This paper describes Amobee{'}s participation in SemEval-2020 task 7: {``}Assessing Humor in Edited News Headlines{''}, sub-tasks 1 and 2. The goal of this task was to estimate the funniness of human modified news headli…
Language ModelingLanguage ModellingTask 2LMML at SemEval-2020 Task 7: Siamese Transformers for Rating Humor in Edited News Headlines
This paper contains a description of my solution to the problem statement of SemEval 2020: Assessing the Funniness of Edited News Headlines. I propose a Siamese Transformer based approach, coupled with a Global Attention…
Task 2SO at SemEval-2020 Task 7: DeepPavlov Logistic Regression with BERT Embeddings vs SVR at Funniness Evaluation
This paper describes my efforts in evaluating how editing news headlines can make them funnier within the frames of SemEval 2020 Task 7. I participated in both of the sub-tasks: Sub-Task 1 {``}Regression{''} and Sub-task…
regressionTask 2UTFPR at SemEval-2020 Task 7: Using Co-occurrence Frequencies to Capture Unexpectedness
We describe the UTFPR system for SemEval-2020{'}s Task 7: Assessing Humor in Edited News Headlines. Ours is a minimalist unsupervised system that uses word co-occurrence frequencies from large corpora to capture unexpect…
Task 2KFU NLP Team at SMM4H 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug Reactions
This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2020 shared tasks. Specifically, we participated in two tasks. We investigate the use of a language representation model BERT pr…
Cross-Lingual TransferTask 2Transfer LearningIdentifying Medication Abuse and Adverse Effects from Tweets: University of Michigan at #SMM4H 2020
The team from the University of Michigan participated in three tasks in the Social Media Mining for Health Applications (#SMM4H) 2020 shared tasks – on detecting mentions of adverse effects (Task 2), extracting and norma…
Task 2How Far Can We Go with Just Out-of-the-box BERT Models?
Social media have been seen as a promising data source for pharmacovigilance. Howev-er, methods for automatic extraction of Adverse Drug Reactions from social media plat-forms such as Twitter still need further developme…
PharmacovigilanceTask 2Autobots Ensemble: Identifying and Extracting Adverse Drug Reaction from Tweets Using Transformer Based Pipelines
This paper details a system designed for Social Media Mining for Health Applications (SMM4H) Shared Task 2020. We specifically describe the systems designed to solve task 2: Automatic classification of multilingual tweet…
NERQuestion AnsweringTask 2Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules
This paper describes the method we developed for the Task 2 English variation of the Social Media Mining for Health Applications (SMM4H) 2020 shared task. The task was to classify tweets containing adverse effects (AE) a…
Task 2Transfer LearningLITL at SMM4H: An Old-school Feature-based Classifier for Identifying Adverse Effects in Tweets
This paper describes our participation to the SMM4H shared task 2. We designed a rule-based classifier that estimates whether a tweet mentions an adverse effect associated to a medication. Our system addresses English an…
regressionTask 2Sentence Classification with Imbalanced Data for Health Applications
Identifying and extracting reports of medications, their abuse or adverse effects from social media is a challenging task. In social media, relevant reports are very infrequent, causes imbalanced class distribution for m…
ClassificationSentenceSentence ClassificationTask 2HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets
This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fifth Social Media Mining for Health Applications (SMM4H) shared task in 2020. The first…
ClassificationTask 2Approaching SMM4H 2020 with Ensembles of BERT Flavours
This paper describes our solutions submitted to the Social Media Mining for Health Applications (#SMM4H) Shared Task 2020. We participated in the following tasks: Task 1 aimed at classifying if a tweet reports medication…
Task 2Sentence Transformers and Bayesian Optimization for Adverse Drug Effect Detection from Twitter
This paper describes our approach for detecting adverse drug effect mentions on Twitter as part of the Social Media Mining for Health Applications (SMM4H) 2020, Shared Task 2. Our approach utilizes multilingual sentence …
Bayesian OptimizationHyperparameter OptimizationSentenceSentence Embeddings+1The Financial Document Causality Detection Shared Task (FinCausal 2020)
We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. Two sub-tasks are proposed: a binary classi…
Binary ClassificationRelation ExtractionTask 2GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents
This document describes a system for causality extraction from financial documents submitted as part of the FinCausal 2020 Workshop. The main contribution of this paper is a description of the robust post-processing used…
Task 2JDD @ FinCausal 2020, Task 2: Financial Document Causality Detection
This paper describes the approach we built for the Financial Document Causality Detection Shared Task (FinCausal-2020) Task 2: Cause and Effect Detection. Our approach is based on a multi-class classifier using BiLSTM wi…
Task 2Fraunhofer IAIS at FinCausal 2020, Tasks 1 & 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents
The FinCausal 2020 shared task aims to detect causality on financial news and identify those parts of the causal sentences related to the underlying cause and effect. We apply ensemble-based and sequence tagging methods …
Task 2