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Found 36 papers, 12 papers with code
Improving Event Duration Question Answering by Leveraging Existing Temporal Information Extraction Data
However, the amount of training data for tasks like duration question answering, i. e., McTACO, is very limited, suggesting a need for external duration information to improve this task.
JaMIE: A Pipeline Japanese Medical Information Extraction System with Novel Relation Annotation
In this paper, we first propose a novel relation annotation schema for investigating the medical and temporal relations between medical entities in Japanese medical reports.
Ethical implications of ChatGPT in higher education: A scoping review
This scoping review explores the ethical challenges of using ChatGPT in education, focusing particularly on issues related to higher education.
Potential Societal Biases of ChatGPT in Higher Education: A Scoping Review
In this scoping review, we clarify the ways in which biases related to GAI in higher education settings have been discussed in recent academic publications and identify what type of potential biases are commonly reported in this body of literature.
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) between two mentions, i. e. event, time, and document creation time (DCT).
Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise
In the zero-shot setting of four Chinese legal tasks, our method improves accuracy by 33. 3\% compared to the direct generation by GPT-4.
Pushing the Limits of ChatGPT on NLP Tasks
In this work, we propose a collection of general modules to address these issues, in an attempt to push the limits of ChatGPT on NLP tasks.
MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting
Large language models (LLMs) have achieved impressive performance on various reasoning tasks.
Comprehensive Solution Program Centric Pretraining for Table-and-Text Hybrid Numerical Reasoning
Numerical reasoning over table-and-text hybrid passages, such as financial reports, poses significant challenges and has numerous potential applications.
GPT-RE: In-context Learning for Relation Extraction using Large Language Models
In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e. g., GPT-3), they still lag significantly behind fully-supervised baselines (e. g., fine-tuned BERT) in relation extraction (RE).
Textual Enhanced Contrastive Learning for Solving Math Word Problems
Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information.
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction
Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models.
ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision
To address this problem, we propose a novel search algorithm with combinatorial strategy \textbf{ComSearch}, which can compress the search space by excluding mathematically equivalent equations.
Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems
To solve Math Word Problems, human students leverage diverse reasoning logic that reaches different possible equation solutions.
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision
Contrastive pre-training on distant supervision has shown remarkable effectiveness in 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.
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.
