[NAACL 2021] QAGNN: Question Answering using Language Models and Knowledge Graphs 🤖
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Updated
Mar 12, 2023 - Python
[NAACL 2021] QAGNN: Question Answering using Language Models and Knowledge Graphs 🤖
PaL: Program-Aided Language Models (ICML 2023)
Knowledge-Aware Graph Networks for Commonsense Reasoning (EMNLP-IJCNLP 19)
[ICML2024] Official PyTorch implementation of DoRA: Weight-Decomposed Low-Rank Adaptation
[ICLR 2022 spotlight]GreaseLM: Graph REASoning Enhanced Language Models for Question Answering
A Constrained Text Generation Challenge Towards Generative Commonsense Reasoning
A set of utilities for running few-shot prompting experiments on large-language models
Language Models of Code are Few-Shot Commonsense Learners (EMNLP 2022)
The purpose of this repository is to introduce new dialogue-level commonsense inference datasets and tasks. We chose dialogues as the data source because dialogues are known to be complex and rich in commonsense.
[Paper][ISWC 2021] Zero-shot Visual Question Answering using Knowledge Graph
A Python Commonsense Knowledge Inference Toolkit
Automated Storytelling via Causal, Commonsense Plot Ordering
Data and code for the "Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences" (Emelin et al., 2021) paper.
[CVPR 2022] A large-scale public benchmark dataset for video question-answering, especially about evidence and commonsense reasoning. The code used in our paper "From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering", CVPR2022.
Codes for the WWW2021 paper: DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge (https://arxiv.org/abs/2101.00154).
Experimental Python implementation of the Clarion cognitive architecture
Code Repo for "Differentiable Open-Ended Commonsense Reasoning" (NAACL 2021)
This repository contains the PyTorch implementation of the paper STaCK: Sentence Ordering with Temporal Commonsense Knowledge appearing at EMNLP 2021.
This repository contains the dataset and the pytorch implementations of the models from the paper CIDER: Commonsense Inference for Dialogue Explanation and Reasoning. CIDER has been accepted to appear at SIGDIAL 2021.
Source code for paper on commonsense reasoning for 2020 Annual Conference of the Association for Computational Linguistics (ACL) 2020.
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