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Common Sense Reasoning

34 papers with code · Reasoning

Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.

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Latest papers with code

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

23 Jul 2019LCAD-UFES/publications-tabelini-ijcnn-2019

Deep learning has been successfully applied to several problems related to autonomous driving.

AUTONOMOUS DRIVING COMMON SENSE REASONING

3
23 Jul 2019

Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction

ACL 2019 DFKI-NLP/DISTRE

Distantly supervised relation extraction is widely used to extract relational facts from text, but suffers from noisy labels.

COMMON SENSE REASONING LANGUAGE MODELLING RELATION CLASSIFICATION

31
19 Jun 2019

Explain Yourself! Leveraging Language Models for Commonsense Reasoning

ACL 2019 salesforce/cos-e

Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input.

COMMON SENSE REASONING

58
06 Jun 2019

Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation

ACL 2019 wangcunxiang/Sen-Making-and-Explanation

Introducing common sense to natural language understanding systems has received increasing research attention.

COMMON SENSE REASONING LANGUAGE MODELLING

5
02 Jun 2019

CITE: A Corpus of Image-Text Discourse Relations

NAACL 2019 malihealikhani/CITE

This paper presents a novel crowd-sourced resource for multimodal discourse: our resource characterizes inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations.

COMMON SENSE REASONING

6
12 Apr 2019

Image Generation from Small Datasets via Batch Statistics Adaptation

3 Apr 2019apple2373/pytorch-small-dataset-image-generation

To reduce the amount of data required, we propose a new method for transferring prior knowledge of the pre-trained generator, which is trained with a large dataset, to a small dataset in a different domain.

COMMON SENSE REASONING IMAGE GENERATION

10
03 Apr 2019

Language Models are Unsupervised Multitask Learners

Preprint 2019 huggingface/pytorch-pretrained-BERT

Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.

 SOTA for Language Modelling on Text8 (using extra training data)

COMMON SENSE REASONING DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION QUESTION ANSWERING READING COMPREHENSION

11,010
14 Feb 2019

Fake News Detection on Social Media using Geometric Deep Learning

10 Feb 2019kc-ml2/ipam-2019-dgl

One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.

COMMON SENSE REASONING FAKE NEWS DETECTION

0
10 Feb 2019

Compositional Language Understanding with Text-based Relational Reasoning

7 Nov 2018koustuvsinha/clutrr

Neural networks for natural language reasoning have largely focused on extractive, fact-based question-answering (QA) and common-sense inference.

COMMON SENSE REASONING LANGUAGE MODELLING QUESTION ANSWERING RELATIONAL REASONING

21
07 Nov 2018