- Explainable Inference on Sequential Data via Memory-Tracking
- A Neural Conversational Model
- Modeling User Exposure in Recommendation
- VisualWord2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes
- CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
- Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network
- The "something something" video database for learning and evaluating visual common sense
- The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
- Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates
- Temporal Relational Reasoning in Videos
- DKN: Deep Knowledge-Aware Network for News Recommendation
- Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
- Empirical Analysis of Foundational Distinctions in Linked Open Data
- Towards Symbolic Reinforcement Learning with Common Sense
- A Simple Method for Commonsense Reasoning
- Incorporating Chinese Characters of Words for Lexical Sememe Prediction
- NAACL 2018 Tutorial – The Interplay between Lexical Resources and Natural Language Processing
- Deep contextualized word representations for detecting sarcasm and irony
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- pair2vec: CompositionalWord-Pair Embeddings for Cross-Sentence Inference
- COMMONSENSEQA: A Question Answering Challenge Targeting Commonsense Knowledge
- The KNOWREF Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution
- How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG
- Compositional Language Understanding with Text-based Relational Reasoning
- Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs
- Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives
- Image Generation From Small Datasets via Batch Statistics Adaptation
- CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense
- CITE: A Corpus of Image-Text Discourse Relations
- Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation
- Explain Yourself! Leveraging Language Models for Commonsense Reasoning
- Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
- Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure Games
- Improving Neural Story Generation by Targeted Common Sense Grounding
- Visual Question Answering using Deep Learning: A Survey and Performance Analysis
- KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- QASC: A Dataset for Question Answering via Sentence Composition
- CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
- PIQA: Reasoning about Physical Commonsense in Natural Language
- Conversational Word Embedding for Retrieval-Based Dialog System
- ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
- The Sensitivity of Language Models and Humans to Winograd Schema Perturbations
- Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models
- Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers
- Language Models are Few-Shot Learners
- CS-NET at SemEval-2020 Task 4: Siamese BERT for ComVE
- Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild
- Learning Long-term Visual Dynamics with Region Proposal Interaction Networks
- Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation
- Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
- iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering
- Language Models are Unsupervised Multitask Learners
- Improved Word Representation Learning with Sememes
- Extracting Commonsense Properties from Embeddings with Limited Human Guidance
- Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions
- GIST at SemEval-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension task
- An Evaluation of PredPatt and Open IE via Stage 1 Semantic Role Labeling
- Correcting Contradictions
- Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning
- That and There: Judging the Intent of Pointing Actions with Robotic Arms
- Calling Out Bluff: Attacking the Robustness of Automatic Scoring Systems with Simple Adversarial Testing
- 2D Probabilistic Undersampling Pattern Optimization for MR Image Reconstruction
manjunath5496 / common-sense-reasoning-papers Goto Github PK
View Code? Open in Web Editor NEWSir Isaac Newton was asked how he discovered the law of gravity. He replied, "By thinking about it all the time."― Sir Isaac Newton