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Found 10 papers, 2 papers with code
Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection
This position paper proposes a systematic approach towards developing a framework to help select the most effective embedding models for natural language processing (NLP) tasks, addressing the challenge posed by the proliferation of both proprietary and open-source encoder models.
DEFT: Data Efficient Fine-Tuning for Pre-Trained Language Models via Unsupervised Core-Set Selection
Recent advances have led to the availability of many pre-trained language models (PLMs); however, a question that remains is how much data is truly needed to fine-tune PLMs for downstream tasks?
RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media
Using this corpus, we introduce the task of retrieving trustworthy evidence relevant to a given claim made on social media.
CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models
We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge bases and generate free-flow textual explanations about various health-related conditions across several dimensions.
Identifying causal relations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021
A cause-effect-tweet dataset was manually labeled and used to train 1) a fine-tuned Bertweet model to detect causal sentences containing a causal association 2) a CRF model with BERT based features to extract possible cause-effect associations.
Template Filling for Controllable Commonsense Reasoning
To enable better controllability, we propose to study the commonsense reasoning as a template filling task (TemplateCSR) -- where the language models fills reasoning templates with the given constraints as control factors.
MIMICause: Representation and automatic extraction of causal relation types from clinical notes
This will enhance healthcare providers' ability to identify aspects of a patient's story communicated in the clinical notes and help make more informed decisions.
Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights
The growing quantity and complexity of data pose challenges for humans to consume information and respond in a timely manner.
Causal BERT : Language models for causality detection between events expressed in text
Therefore, as expected these methods are more geared towards handling explicit causal relationships leading to limited coverage for implicit relationships and are hard to generalize.
Neural Information Retrieval: A Literature Review
A recent "third wave" of Neural Network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing.
