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LMs4Implicit-Knowledge-Generation
Code for equipping pretrained language models (BART, GPT-2, XLNet) with commonsense knowledge for generating implicit knowledge statements between two sentences, by (i) finetuning the models on corpora enriched with implicit information; and by (ii) constraining models with key concepts and commonsense knowledge paths connecting them.IKAT-EN
English version of IKAT: A corpus consisting of high-quality human annotations of missing and implied information in argumentative texts. The data is further annotated with semantic clause types and commonsense knowledge relations.Moralization
CO-NNECT
This repository contains our path generation framework Co-NNECT, in which we combine two models for establishing knowledge relations and paths between concepts from sentences, as a form of explicitation of implicit knowledge: COREC-LM (COmmonsense knowledge RElation Classification using Language Models), a relation classification system that we developed for classifying commonsense knowledge relations; and COMET, a target prediction system developed by Bosselut et al., 2019.IKAT-DE
German version of IKAT: A corpus consisting of high-quality human annotations of missing and implied information in argumentative texts. The data is further annotated with semantic clause types and commonsense knowledge relations.Python4NLP
RNN_for_Clause_Classification
This is a Classifier for situation entity types as described in Becker et al., 2017. These clause types depend on a combination of syntactic-semantic and contextual features. We explore this task in a deeplearning framework, where tuned word representations capture lexical, syntactic and semantic features.Love Open Source and this site? Check out how you can help us