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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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Greatest papers with code

A Simple Method for Commonsense Reasoning

7 Jun 2018tensorflow/models

For example, it is difficult to use neural networks to tackle the Winograd Schema dataset~\cite{levesque2011winograd}.

COMMON SENSE REASONING

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

A Neural Conversational Model

19 Jun 2015farizrahman4u/seq2seq

We find that this straightforward model can generate simple conversations given a large conversational training dataset.

COMMON SENSE REASONING

Temporal Relational Reasoning in Videos

ECCV 2018 metalbubble/TRN-pytorch

Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species.

ACTION CLASSIFICATION ACTION RECOGNITION IN VIDEOS ACTIVITY RECOGNITION COMMON SENSE REASONING HUMAN-OBJECT INTERACTION DETECTION RELATIONAL REASONING

DKN: Deep Knowledge-Aware Network for News Recommendation

25 Jan 2018hwwang55/DKN

To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation.

CLICK-THROUGH RATE PREDICTION COMMON SENSE REASONING RECOMMENDATION SYSTEMS

Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network

22 Jul 2016braincorp/PVM

These regularities are hard to label for training supervised machine learning algorithms; consequently, algorithms need to learn these regularities from the real world in an unsupervised way.

COMMON SENSE REASONING VISUAL TRACKING

Incorporating Chinese Characters of Words for Lexical Sememe Prediction

ACL 2018 thunlp/SCPapers

However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-of-vocabulary words.

COMMON SENSE REASONING