Papers Task 2
“Task 2” 태그가 달린 논문 572편 · 필터 해제
CardiffNLP-Metaphor at SemEval-2022 Task 2: Targeted Fine-tuning of Transformer-based Language Models for Idiomaticity Detection
This paper describes the experiments ran for SemEval-2022 Task 2, subtask A, zero-shot and one-shot settings for idiomaticity detection. Our main approach is based on fine-tuning transformer-based language models as a ba…
Binary ClassificationTask 2kpfriends at SemEval-2022 Task 2: NEAMER - Named Entity Augmented Multi-word Expression Recognizer
We present NEAMER - Named Entity Augmented Multi-word Expression Recognizer. This system is inspired by non-compositionality characteristics shared between Named Entity and Idiomatic Expressions. We utilize transfer lear…
SentenceSentence EmbeddingSentence-EmbeddingTask 2+1daminglu123 at SemEval-2022 Task 2: Using BERT and LSTM to Do Text Classification
Multiword expressions (MWEs) or idiomaticity are common phenomenon in natural languages. Current pre-trained language models cannot effectively capture the meaning of these MWEs. The reason is that two normal words, afte…
Task 2text-classificationText ClassificationZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations
This paper presents our contribution to the SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding.We explore the impact of three different pre-trained multilingual language models in the SubTask…
Adversarial AttackContrastive LearningSentenceTask 2NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity Detection
Idioms are lexically-complex phrases whose meaning cannot be derived by compositionally interpreting their components. Although the automatic identification and understanding of idioms is essential for a wide range of Na…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Natural Language Understanding+5YNU-HPCC at SemEval-2022 Task 2: Representing Multilingual Idiomaticity based on Contrastive Learning
This paper will present the methods we use as the YNU-HPCC team in the SemEval-2022 Task 2, Multilingual Idiomaticity Detection and Sentence Embedding. We are involved in two subtasks, including four settings. In subtask…
Contrastive LearningSentenceSentence EmbeddingSentence-Embedding+2Tesla at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Transformer-based Models with Data Augmentation
This paper describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language (PCL) Detection. For sub-task 1, where the objective is to classify a text as PCL or non-PCL, we use a T5 Model fine-tune…
ClassificationData AugmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1PALI at SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts
This paper describes our system used in the SemEval-2022 Task 7(Roth et al.): Identifying Plausible Clarifications of Implicit and Under-specified Phrases. Semeval Task7 is an more complex cloze task, different than norm…
Contrastive LearningSentenceTask 2Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques
We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques'…
domain classificationDomain GeneralizationTask 2Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model
Current architectures for multi-modality tasks such as visual question answering suffer from their high complexity. As a result, these architectures are difficult to train and require high computational resources. To add…
Question AnsweringTask 2Visual Question AnsweringVisual Question Answering (VQA)Delving into the Pre-training Paradigm of Monocular 3D Object Detection
The labels of monocular 3D object detection (M3OD) are expensive to obtain. Meanwhile, there usually exists numerous unlabeled data in practical applications, and pre-training is an efficient way of exploiting the knowle…
2D Object Detection3D Object DetectionDepth EstimationMonocular 3D Object Detection+4OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection
We propose a multilingual adversarial training model for determining whether a sentence contains an idiomatic expression. Given that a key challenge with this task is the limited size of annotated data, our model relies …
SentenceTask 2HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization
We propose a unified framework that enables us to consider various aspects of contextualization at different levels to better identify the idiomaticity of multi-word expressions. Through extensive experiments, we demonst…
SentenceTask 2ExpertNeurons at FinCausal 2022 Task 2: Causality Extraction for Financial Documents
In this paper describes the approach which we have built for causality extraction from the financial documents that we have submitted for FinCausal 2022 task 2. We proving a solution with intelligent pre-processing and p…
Task 2ATL at FinCausal 2022: Transformer Based Architecture for Automatic Causal Sentence Detection and Cause-Effect Extraction
Automatic extraction of cause-effect relationships from natural language texts is a challenging open problem in Artificial Intelligence. Most of the early attempts at its solution used manually constructed linguistic and…
SentenceTask 2UAlberta at SemEval 2022 Task 2: Leveraging Glosses and Translations for Multilingual Idiomaticity Detection
We describe the University of Alberta systems for the SemEval-2022 Task 2 on multilingual idiomaticity detection. Working under the assumption that idiomatic expressions are noncompositional, our first method integrates …
Task 2TranslationHiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models
This paper describes an approach to detect idiomaticity only from the contextualized representation of a MWE over multilingual pretrained language models. Our experiments find that larger models are usually more effectiv…
Task 2Deep Representations for Time-varying Brain Datasets
Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications. Due to its highly dynamic nature, temporally averaged fMRI (functional magnetic resonance imaging) can…
AttributeGraph Neural NetworkTask 2Transformer-based Architecture for Empathy Prediction and Emotion Classification
This paper describes the contribution of team PHG to the WASSA 2022 shared task on Empathy Prediction and Emotion Classification. The broad goal of this task was to model an empathy score, a distress score and the type o…
ClassificationEmotion ClassificationPredictionTask 2SSNCSE_NLP@TamilNLP-ACL2022: Transformer based approach for Emotion analysis in Tamil language
Emotion analysis is the process of identifying and analyzing the underlying emotions expressed in textual data. Identifying emotions from a textual conversation is a challenging task due to the absence of gestures, vocal…
Emotion RecognitionTask 2