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Found 6 papers, 3 papers with code
Efficient Learning of Multiple NLP Tasks via Collective Weight Factorization on BERT
We test our proposed method on finetuning multiple natural language understanding tasks by employing BERT-Large as an instantiation of the Transformer and the GLUE as the evaluation benchmark.
Reasoning over Description Logic-based Contexts with Transformers
One way that the current state of the art measures the reasoning ability of transformer-based models is by evaluating accuracy in downstream tasks like logical question answering or proof generation over synthetic contexts expressed in natural language.
Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]
This is applied to both main steps of ER, i. e., blocking and matching.
A Review of the Role of Causality in Developing Trustworthy AI Systems
This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models.
Multi-granular Legal Topic Classification on Greek Legislation
In this work, we study the task of classifying legal texts written in the Greek language.
Template-Based Question Answering over Linked Geospatial Data
We give a detailed description of the system's architecture, its underlying algorithms, and its evaluation using a set of 201 natural language questions.
