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Common Sense Reasoning

45 papers with code ยท 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 without code

Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild

30 Jul 2020

We present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontrolled environment.

COMMON SENSE REASONING HUMAN-OBJECT INTERACTION DETECTION

CS-NET at SemEval-2020 Task 4: Siamese BERT for ComVE

21 Jul 2020

Out of the three subtasks, this paper reports the system description of subtask A and subtask B.

COMMON SENSE REASONING

Understanding Spatial Relations through Multiple Modalities

LREC 2020

Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general.

COMMON SENSE REASONING

Calling Out Bluff: Attacking the Robustness of Automatic Scoring Systems with Simple Adversarial Testing

14 Jul 2020

A significant progress has been made in deep-learning based Automatic Essay Scoring (AES) systems in the past two decades.

COMMON SENSE REASONING NATURAL LANGUAGE UNDERSTANDING

Robustness to Spurious Correlations via Human Annotations

13 Jul 2020

The reliability of machine learning systems critically assumes that the associations between features and labels remain similar between training and test distributions.

COMMON SENSE REASONING

Conversational Word Embedding for Retrieval-Based Dialog System

ACL 2020

Human conversations contain many types of information, e. g., knowledge, common sense, and language habits.

COMMON SENSE REASONING MACHINE TRANSLATION WORD ALIGNMENT

Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models

ACL 2020

Recent models for unsupervised representation learning of text have employed a number of techniques to improve contextual word representations but have put little focus on discourse-level representations.

COMMON SENSE REASONING NATURAL LANGUAGE INFERENCE READING COMPREHENSION UNSUPERVISED REPRESENTATION LEARNING

Temporal Common Sense Acquisition with Minimal Supervision

ACL 2020

Temporal common sense (e. g., duration and frequency of events) is crucial for understanding natural language.

COMMON SENSE REASONING LANGUAGE MODELLING

Language Models as Fact Checkers?

WS 2020

Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data.

COMMON SENSE REASONING LANGUAGE MODELLING OPEN-DOMAIN QUESTION ANSWERING