Papers Relation Classification

Cross-Network Learning with Partially Aligned Graph Convolutional Networks

Graph neural networks have been widely used for learning representations of nodes for many downstream tasks on graph data. Existing models were designed for the nodes on a single graph, which would not be able to utilize…

Relation Classification with Cognitive Attention Supervision

Many current language models such as BERT utilize attention mechanisms to transform sequence representations. We ask whether we can influence BERT’s attention with human reading patterns by using eye-tracking and brain i…

PTR: Prompt Tuning with Rules for Text Classification

Fine-tuned pre-trained language models (PLMs) have achieved awesome performance on almost all NLP tasks. By using additional prompts to fine-tune PLMs, we can further stimulate the rich knowledge distributed in PLMs to b…

Global Context for improving recognition of Online Handwritten Mathematical Expressions

This paper presents a temporal classification method for all three subtasks of symbol segmentation, symbol recognition and relation classification in online handwritten mathematical expressions (HMEs). The classification…

Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition

Deep neural networks such as BERT have made great progress in relation classification. Although they can achieve good performance, it is still a question of concern whether these models recognize the directionality of re…

Relation Classification with Entity Type Restriction

Relation classification aims to predict a relation between two entities in a sentence. The existing methods regard all relations as the candidate relations for the two entities in a sentence. These methods neglect the re…

Representation Iterative Fusion based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction

Joint entity and relation extraction is an essential task in information extraction, which aims to extract all relational triples from unstructured text. However, few existing works consider possible relations informatio…

Discourse Relation Embeddings: Representing the Relations between Discourse Segments in Social Media

Discourse relations are typically modeled as a discrete class that characterizes the relation between segments of text (e.g. causal explanations, expansions). However, such predefined discrete classes limits the universe…

Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System

Text classification is usually studied by labeling natural language texts with relevant categories from a predefined set. In the real world, new classes might keep challenging the existing system with limited labeled dat…

Extracting Temporal Event Relation with Syntax-guided Graph Transformer

Extracting temporal relations (e.g., before, after, and simultaneous) among events is crucial to natural language understanding. One of the key challenges of this problem is that when the events of interest are far away …

Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes

We explore Few-Shot Learning (FSL) for Relation Classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories (none-of-the-above, aka NOTA), we …

``Laughing at you or with you'': The Role of Sarcasm in Shaping the Disagreement Space

Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. Users often use figurative language, such as sarcasm, either as persuasive devices or to attack the opponent by an …

Multi-Task Attentive Residual Networks for Argument Mining

We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any ass…

"Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space

Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. Users often use figurative language, such as sarcasm, either as persuasive devices or to attack the opponent by an …

Structured Prediction as Translation between Augmented Natural Languages

We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, rela…

Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification

Relation classification (RC) task is one of fundamental tasks of information extraction, aiming to detect the relation information between entity pairs in unstructured natural language text and generate structured data i…

Relation Classification via Relation Validation

Syntax-Enhanced Pre-trained Model

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning st…

Towards Incorporating Entity-specific Knowledge Graph Information in Predicting Drug-Drug Interactions

Off-the-shelf biomedical embeddings obtained from the recently released various pre-trained language models (such as BERT, XLNET) have demonstrated state-of-the-art results (in terms of accuracy) for the various natural …

Document-Level Relation Extraction with Reconstruction

In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced t…