Search Results for author:
Found 8 papers, 4 papers with code
Multi-Cultural Commonsense Knowledge Distillation
Despite recent progress, large language models (LLMs) still face the challenge of appropriately reacting to the intricacies of social and cultural conventions.
Extracting Cultural Commonsense Knowledge at Scale
Commonsense knowledge, which is crucial for robust human-centric AI, is covered by a small number of structured knowledge projects.
An Experimental Investigation of Part-Of-Speech Taggers for Vietnamese
We also design a new feature set to measure the performance of the statistical taggers.
Materialized Knowledge Bases from Commonsense Transformers
Starting from the COMET methodology by Bosselut et al. (2019), generating commonsense knowledge directly from pre-trained language models has recently received significant attention.
Refined Commonsense Knowledge from Large-Scale Web Contents
However, they are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and strings for P and O.
Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering
ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., WWW 2021).
Advanced Semantics for Commonsense Knowledge Extraction
Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O.
A hybrid approach to Vietnamese word segmentation
Word segmentation is the very first task for Vietnamese language processing.
