Papers Task 2
“Task 2” 태그가 달린 논문 572편 · 필터 해제
Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning
The prediction of the relationship between the disease with genes and its mutations is a very important knowledge extraction task that can potentially help drug discovery. In this paper, we present our approaches for tri…
Drug DiscoveryMulti-Task LearningNERRelation Extraction+1Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report
This paper reports on the results of the shared tasks of the COIN workshop at EMNLP-IJCNLP 2019. The tasks consisted of two machine comprehension evaluations, each of which tested a system{'}s ability to answer questions…
Reading ComprehensionTask 2Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks
To solve the shared tasks of COIN: COmmonsense INference in Natural Language Processing) Workshop in , we need explore the impact of knowledge representation in modeling commonsense knowledge to boost performance of mach…
Common Sense ReasoningMachine Reading ComprehensionReading ComprehensionTask 2+1SANet:Superpixel Attention Network for Skin Lesion Attributes Detection
The accurate detection of lesion attributes is meaningful for both the computeraid diagnosis system and dermatologists decisions. However, unlike lesion segmentation and melenoma classification, there are few deep learni…
AttributeGeneral ClassificationLesion SegmentationTask 2Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data
In this paper, we describe our method for the ISIC 2019 Skin Lesion Classification Challenge. The challenge comes with two tasks. For task 1, skin lesions have to be classified based on dermoscopic images. For task 2, de…
General ClassificationLesion ClassificationSkin Lesion ClassificationTask 2Detecting Deception in Political Debates Using Acoustic and Textual Features
We present work on deception detection, where, given a spoken claim, we aim to predict its factuality. While previous work in the speech community has relied on recordings from staged setups where people were asked to te…
Deception DetectionFact CheckingMultimodal Deep LearningTask 2BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions
This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019. Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health dis…
Re-RankingRetrievalSentenceTask 2Data Valuation using Reinforcement Learning
Quantifying the value of data is a fundamental problem in machine learning. Data valuation has multiple important use cases: (1) building insights about the learning task, (2) domain adaptation, (3) corrupted sample disc…
Data ValuationDomain AdaptationMeta-Learningreinforcement-learning+3A Sketch-Based System for Semantic Parsing
This paper presents our semantic parsing system for the evaluation task of open domain semantic parsing in NLPCC 2019. Many previous works formulate semantic parsing as a sequence-to-sequence(seq2seq) problem. Instead, w…
RerankingSemantic ParsingTask 2UDPipe at SIGMORPHON 2019: Contextualized Embeddings, Regularization with Morphological Categories, Corpora Merging
We present our contribution to the SIGMORPHON 2019 Shared Task: Crosslinguality and Context in Morphology, Task 2: contextual morphological analysis and lemmatization. We submitted a modification of the UDPipe 2.0, one o…
LemmatizationMorphological AnalysisTask 2Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019
The number of users of social media continues to grow, with nearly half of adults worldwide and two-thirds of all American adults using social networking. Advances in automated data processing, machine learning and NLP p…
Task 2KFU NLP Team at SMM4H 2019 Tasks: Want to Extract Adverse Drugs Reactions from Tweets? BERT to The Rescue
This paper describes a system developed for the Social Media Mining for Health (SMM4H) 2019 shared tasks. Specifically, we participated in three tasks. The goals of the first two tasks are to classify whether a tweet con…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Task 2Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention
This paper describes our system for the first and second shared tasks of the fourth Social Media Mining for Health Applications (SMM4H) workshop. We enhance tweet representation with a language model and distinguish the …
Language ModelingLanguage ModellingTask 2Transfer LearningDetection of Adverse Drug Reaction Mentions in Tweets Using ELMo
This paper describes the models used by our team in SMM4H 2019 shared task. We submitted results for subtasks 1 and 2. For task 1 which aims to detect tweets with Adverse Drug Reaction (ADR) mentions we used ELMo embeddi…
Task 2Cross-Lingual Lemmatization and Morphology Tagging with Two-Stage Multilingual BERT Fine-Tuning
We present our CHARLES-SAARLAND system for the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology, in task 2, Morphological Analysis and Lemmatization in Context. We leverage the multilingual BERT m…
LemmatizationMorphological AnalysisTask 2CBNU System for SIGMORPHON 2019 Shared Task 2: a Pipeline Model
In this paper we describe our system for morphological analysis and lemmatization in context, using a transformer-based sequence to sequence model and a biaffine attention based BiLSTM model. First, a lemma is produced f…
LEMMALemmatizationMorphological AnalysisTask 2Morpheus: A Neural Network for Jointly Learning Contextual Lemmatization and Morphological Tagging
In this study, we present Morpheus, a joint contextual lemmatizer and morphological tagger. Morpheus is based on a neural sequential architecture where inputs are the characters of the surface words in a sentence and the…
DecoderLemmatizationMorphological TaggingSentence+1Multi-Team: A Multi-attention, Multi-decoder Approach to Morphological Analysis.
This paper describes our submission to SIGMORPHON 2019 Task 2: Morphological analysis and lemmatization in context. Our model is a multi-task sequence to sequence neural network, which jointly learns morphological taggin…
DecoderLEMMALemmatizationMorphological Analysis+2THOMAS: The Hegemonic OSU Morphological Analyzer using Seq2seq
This paper describes the OSU submission to the SIGMORPHON 2019 shared task, Crosslinguality and Context in Morphology. Our system addresses the \textit{contextual morphological analysis} subtask of Task 2, which is to pr…
DecoderMorphological AnalysisSentenceTAG+1Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few
This paper presents the UNT HiLT+Ling system for the Sigmorphon 2019 shared Task 2: Morphological Analysis and Lemmatization in Context. Our core approach focuses on the morphological tagging task; part-of-speech tagging…
LemmatizationMorphological AnalysisMorphological InflectionMorphological Tagging+3