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Found 19 papers, 8 papers with code
SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models
Causal information extraction is an important task in natural language processing, particularly in finance domain.
Planning with Language Models Through The Lens of Efficiency
We analyse the cost of using LLMs for planning and highlight that recent trends are profoundly uneconomical.
Improving Neural Ranking Models with Traditional IR Methods
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions.
A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation.
LakeBench: Benchmarks for Data Discovery over Data Lakes
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery.
Generalized Planning in PDDL Domains with Pretrained Large Language Models
We investigate whether LLMs can serve as generalized planners: given a domain and training tasks, generate a program that efficiently produces plans for other tasks in the domain.
A Vision for Semantically Enriched Data Science
The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection.
Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning
We demonstrate the efficiency and usefulness of Serenity's analysis in two applications: code completion and automated machine learning.
Results of SemTab 2022
SemTab 2022 was the fourth edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 21st International Semantic Web Conference (ISWC) and the 17th Ontology Matching (OM) Workshop.
Ranked #2 on
Cell Entity Annotation
on ToughTables-WD
Exploring Code Style Transfer with Neural Networks
In addition to defining style, we explore the capability of a pre-trained code language model to capture information about code style.
A Survey on Semantics in Automated Data Science
Data Scientists leverage common sense reasoning and domain knowledge to understand and enrich data for building predictive models.
Federated Data Science to Break Down Silos [Vision]
Similar to Open Data initiatives, data science as a community has launched initiatives for sharing not only data but entire pipelines, derivatives, artifacts, etc.
A Scalable AutoML Approach Based on Graph Neural Networks
AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner.
Results of SemTab 2021
SemTab 2021 was the third edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 20th International Semantic Web Conference (ISWC) and the 16th Ontology Matching (OM) Workshop.
Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code Understanding
Code understanding is an increasingly important application of Artificial Intelligence.
Learning to Guide a Saturation-Based Theorem Prover
In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more problems).
A Toolkit for Generating Code Knowledge Graphs
We make the toolkit to build such graphs as well as the sample extraction of the 2 billion triples graph publicly available to the community for use.
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search.
Merging datasets through deep learning
Our approach depends on (a) creating a deep learning model that maps surface forms of an entity into a set of vectors such that alternate forms for the same entity are closest in vector space, (b) indexing these vectors using a nearest neighbors algorithm to find the forms that can be potentially joined together.
