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Found 23 papers, 6 papers with code
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision
Contrastive pre-training on distant supervision has shown remarkable effectiveness for improving supervised relation extraction tasks.
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition
Low resource speech recognition has been long-suffering from insufficient training data.
Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding
Meta learning with auxiliary languages has demonstrated promising improvements for cross-lingual natural language processing.
JaMIE: A Pipeline Japanese Medical Information Extraction System
We present an open-access natural language processing toolkit for Japanese medical information extraction.
OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity Prediction
We propose an ensemble model for predicting the lexical complexity of words and multiword expressions (MWEs).
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation
In this paper, we explore the potential benefits introduced by image SR to ship detection, and propose an end-to-end network named ShipSRDet.
A Hybrid Bandit Framework for Diversified Recommendation
Previous interactive recommendation methods primarily focus on learning users' personalized preferences on the relevance properties of an item set.
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
Temporal relation classification is the pair-wise task for identifying the relation of a temporal link (TLINKs) between two mentions, i. e. event, time and document creation time (DCT).
Reverse Operation based Data Augmentation for Solving Math Word Problems
Automatically solving math word problems is a critical task in the field of natural language processing.
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction
We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets.
Predicting Event Time by Classifying Sub-Level Temporal Relations Induced from a Unified Representation of Time Anchors
Another contribution of this work is to construct a larger event time corpus (256 news documents) with a reasonable Inter-Annotator Agreement (IAA), for the purpose of overcoming the data shortage of the existing event time corpus (36 news documents).
A System for Worldwide COVID-19 Information Aggregation
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education.
Pre-training via Leveraging Assisting Languages for Neural Machine Translation
Sequence-to-sequence (S2S) pre-training using large monolingual data is known to improve performance for various S2S NLP tasks.
Adversarial Training for Commonsense Inference
We propose an AdversariaL training algorithm for commonsense InferenCE (ALICE).
Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases
Applying natural language processing (NLP) to medical and clinical texts can bring important social benefits by mining valuable information from unstructured text.
Pre-training via Leveraging Assisting Languages and Data Selection for Neural Machine Translation
To this end, we propose to exploit monolingual corpora of other languages to complement the scarcity of monolingual corpora for the LOI.
Random Occlusion-recovery for Person Re-identification
As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping multiple cameras or across different time with a single camera.
Inducing Temporal Relations from Time Anchor Annotation
Conventional annotation of judging temporal relations puts a heavy load on annotators.
Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths
In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (Bi-LSTM) along dependency paths (DP).
Parsing Chinese Synthetic Words with a Character-based Dependency Model
Synthetic word analysis is a potentially important but relatively unexplored problem in Chinese natural language processing.
