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Found 32 papers, 10 papers with code
AaltoNLP at SemEval-2022 Task 11: Ensembling Task-adaptive Pretrained Transformers for Multilingual Complex NER
The shared task is about multilingual complex named entity recognition.
Lucky 52: How Many Languages Are Needed to Instruction Fine-Tune Large Language Models?
Fine-tuning large language models for multilingual downstream tasks requires a diverse set of languages to capture the nuances and structures of different linguistic contexts effectively.
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?
We furthermore provide evidence through similarity measures and investigation of parameters that this lack of positive influence is due to output separability -- which we argue is of use for machine translation but detrimental elsewhere.
A New Massive Multilingual Dataset for High-Performance Language Technologies
We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive.
MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki
NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled.
MaLA-500: Massive Language Adaptation of Large Language Models
Large language models (LLMs) have advanced the state of the art in natural language processing.
Rethinking Large Language Models in Mental Health Applications
Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications.
Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants.
Content Reduction, Surprisal and Information Density Estimation for Long Documents
We consider two interesting research questions: 1) how is information distributed over long documents, and 2) how does content reduction, such as token selection and text summarization, affect the information density in long documents.
A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge
However, most previous knowledge infusion methods perform empirical knowledge filtering and design highly customized architectures for knowledge interaction with the utterances, which can discard useful knowledge aspects and limit their generalizability to different knowledge sources.
Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health
In the mental health domain, domain-specific language models are pretrained and released, which facilitates the early detection of mental health conditions.
Emotion fusion for mental illness detection from social media: A survey
In this article, we provide a comprehensive survey of approaches to mental illness detection in social media that incorporate emotion fusion.
Towards Interpretable Mental Health Analysis with Large Language Models
The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis.
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection
Adverse drug events (ADEs) are an important aspect of drug safety.
Cross-Modality Gated Attention Fusion for Multimodal Sentiment Analysis
Multimodal sentiment analysis is an important research task to predict the sentiment score based on the different modality data from a specific opinion video.
Automated Clinical Coding: What, Why, and Where We Are?
Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding.
A Unified Review of Deep Learning for Automated Medical Coding
Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents.
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment.
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning
Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of multiple tasks.
Multitask Balanced and Recalibrated Network for Medical Code Prediction
Nevertheless, automated medical coding is still challenging because of the imbalanced class problem, complex code association, and noise in lengthy documents.
Fine-Tuning Pretrained Language Models With Label Attention for Biomedical Text Classification
The massive scale and growth of textual biomedical data have made its indexing and classification increasingly important.
Multitask Recalibrated Aggregation Network for Medical Code Prediction
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement.
Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study
In the clinical application of medical code assignment, diagnosis and procedure codes are inferred from lengthy clinical notes such as hospital discharge summaries.
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.
Medical Code Assignment with Gated Convolution and Note-Code Interaction
However, these methods are still ineffective as they do not fully encode and capture the lengthy and rich semantic information of medical notes nor explicitly exploit the interactions between the notes and codes.
Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text
Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems.
BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis
Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e. g., sentiment analysis, recommender systems, and human-robot interaction.
Ranked #39 on
Emotion Recognition in Conversation
on IEMOCAP
Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment.
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning
Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization.
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
Suicide is a critical issue in modern society.
Learning Private Neural Language Modeling with Attentive Aggregation
Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.
