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Found 16 papers, 8 papers with code
DeepBlues@LT-EDI-ACL2022: Depression level detection modelling through domain specific BERT and short text Depression classifiers
In addition we can use short text classifiers to extract relevant text from the long text and achieve slightly better accuracy, albeit, trading off with the processing time for extracting such excerpts.
Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation
Hence, both modules establish a learnable connection to solve the semantic gaps between the encoder and the decoder, which leads to a high-performance segmentation model for medical images.
Exploring Best Practices for ECG Signal Processing in Machine Learning
In this work we apply down-sampling, normalization, and filtering functions to 3 different multi-label ECG datasets and measure their effects on 3 different high-performing time-series classifiers.
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach
In our work, we describe a Semi-supervised Learning (SSL) framework which uses an initial supervised learning model that leverages 1) a state-of-the-art large mental health forum text pre-trained language model further fine-tuned on a clinician annotated DSD dataset, 2) a Zero-Shot learning model for DSD, and couples them together to harvest depression symptoms related samples from our large self-curated Depression Tweets Repository (DTR).
FaithDial: A Faithful Benchmark for Information-Seeking Dialogue
The goal of information-seeking dialogue is to respond to seeker queries with natural language utterances that are grounded on knowledge sources.
Named Entity Recognition for Partially Annotated Datasets
The most common Named Entity Recognizers are usually sequence taggers trained on fully annotated corpora, i. e. the class of all words for all entities is known.
On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?
Knowledge-grounded conversational models are known to suffer from producing factually invalid statements, a phenomenon commonly called hallucination.
FREDA: Flexible Relation Extraction Data Annotation
To effectively train accurate Relation Extraction models, sufficient and properly labeled data is required.
A comprehensive empirical analysis on cross-domain semantic enrichment for detection of depressive language
We start with a rich word embedding pre-trained from a large general dataset, which is then augmented with embeddings learned from a much smaller and more specific domain dataset through a simple non-linear mapping mechanism.
STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text
We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task notorious for training data scarcity, i. e. Depression Symptoms Detection (DSD) from text.
Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers.
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding
Dialogue systems powered by large pre-trained language models (LM) exhibit an innate ability to deliver fluent and natural-looking responses.
Building a Competitive Associative Classifier
We use 15 UCI datasets and compare our approach with eight existing systems. The SigD2 and boosted SigDirect (ACboost) ensemble model outperform various state-of-the-art classifiers not only in terms of classification accuracy but also in terms of the number of rules.
WEXEA: Wikipedia EXhaustive Entity Annotation
Building predictive models for information extraction from text, such as named entity recognition or the extraction of semantic relationships between named entities in text, requires a large corpus of annotated text.
Evaluating Coherence in Dialogue Systems using Entailment
Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers.
Augmenting Neural Response Generation with Context-Aware Topical Attention
Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation.
