Papers Relation Classification
“Relation Classification” 태그가 달린 논문 476편 · 필터 해제
CogALex 2.0: Impact of Data Quality on Lexical-Semantic Relation Prediction
Predicting lexical-semantic relations between word pairs has successfully been accomplished by pre-trained neural language models. An XLM-RoBERTa-based approach, for instance, achieved the best performance differentiatin…
Hypernym DiscoveryRelationRelation ClassificationRelation PredictionPrompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation
In relation triplet extraction (RTE), recognizing unseen relations for which there are no training instances is a challenging task. Efforts have been made to recognize unseen relations based on question-answering models …
ClassificationQuestion AnsweringRelationRelation Classification+6Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples
Knowledge-enhanced language representation learning has shown promising results across various knowledge-intensive NLP tasks. However, prior methods are limited in efficient utilization of multilingual knowledge graph (K…
Knowledge GraphsLanguage ModelingLanguage ModellingLogical Reasoning+9One General Teacher for Multi-Data Multi-Task: A New Knowledge Distillation Framework for Discourse Relation Analysis
Automatically identifying the discourse relations can help many downstream NLP tasks such as reading comprehension. It can be categorized into explicit and implicit discourse relation recognition (EDRR and IDRR). Due to…
Knowledge DistillationMulti-Task LearningReading ComprehensionRelation+1Generative Prompt Tuning for Relation Classification
Prompt tuning is proposed to better tune pre-trained language models by filling the objective gap between the pre-training process and the downstream tasks. Current methods mainly convert the downstream tasks into masked…
ClassificationLanguage ModelingLanguage ModellingMasked Language Modeling+3SERC: Syntactic and Semantic Sequence based Event Relation Classification
Temporal and causal relations play an important role in determining the dependencies between events. Classifying the temporal and causal relations between events has many applications, such as generating event timelines,…
ClassificationNatural Language InferenceQuestion AnsweringRelation+2The DISRPT 2021 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification
In 2021, we organized the second iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task (Discourse Relation Parsing and Treebanking). Adding to th…
Connective DetectionDiscourse ParsingRelationRelation ClassificationA Unified Approach to Discourse Relation Classification in nine Languages
This paper presents efforts to solve the shared task on discourse relation classification (disrpt task 3). The intricate prediction task aims to predict a large number of classes from the Rhetorical Structure Theory (RST…
ClassificationRelationRelation ClassificationSentence+1A Data Bootstrapping Recipe for Low-Resource Multilingual Relation Classification
Relation classification (sometimes called ‘extraction’) requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is challenging for Indian languages, because they ar…
ClassificationRelationRelation ClassificationCVAE-based Re-anchoring for Implicit Discourse Relation Classification
Training implicit discourse relation classifiers suffers from data sparsity. Variational AutoEncoder (VAE) appears to be the proper solution. It is because ideally VAE is capable of generating inexhaustible varying sampl…
Binary ClassificationClassificationData AugmentationImplicit Discourse Relation Classification+3KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained Relationships
Interactions between entities in knowledge graph (KG) provide rich knowledge for language representation learning. However, existing knowledge-enhanced pretrained language models (PLMs) only focus on entity information a…
Entity LinkingEntity TypingLanguage ModelingLanguage Modelling+4Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph
Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion. Previous work studied this task in the context of peer review and rebuttal, and decomposed it into a sequence l…
Argument Pair Extraction (APE)RelationRelation ClassificationSentenceTowards Realistic Few-Shot Relation Extraction
In recent years, few-shot models have been applied successfully to a variety of NLP tasks. Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpa…
ClassificationFew-Shot LearningFew-Shot Relation ClassificationRelation+2Distilling Relation Embeddings from Pretrained Language Models
Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among o…
Knowledge GraphsLanguage ModelingLanguage ModellingRelation+1Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction
Event time is one of the most important features for event-event temporal relation extraction. However, explicit event time information in text is sparse. For example, only about 20% of event mentions in TimeBank-Dense h…
RelationRelation ClassificationRelation ExtractionTemporal Relation Classification+1REBEL: Relation Extraction By End-to-end Language generation
Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, …
Entity LinkingJoint Entity and Relation ExtractionRelationRelation Classification+2A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classification
Relation classification (sometimes called 'extraction') requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is challenging for Indian languages, because they ar…
ClassificationRelationRelation ClassificationInconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning
Standard few-shot relation classification (RC) is designed to learn a robust classifier with only few labeled data for each class. However, previous works rarely investigate the effects of a different number of classes (…
Contrastive LearningFew-Shot LearningFew-Shot Relation ClassificationRelation+1On the Relation between Syntactic Divergence and Zero-Shot Performance
We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting. While previous work suggests such a relation, i…
Cross-lingual zero-shot dependency parsingDependency ParsingRelationRelation Classification+1Generate Triggers in Neural Relation Extraction
In the relation extraction task, the relationship between two entities is determined by some specific words in their source text. These words are called relation triggers, which are the evidence to explain the relationsh…
DecoderRelationRelation ClassificationRelation Extraction