KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense (D19-60)
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| Challenge: | Using Bidirectional Encoder Representations from Transformers(BERT) and external relational knowledge from ConceptNet, we are able to achieve an accuracy of 73.3 % on the official test data. |
| Approach: | They propose a model that uses Bidirectional Encoder Representations from Transformers and ConceptNet to tackle the problem of commonsense inference in natural language processing. |
| Outcome: | The proposed model achieves 73.3 % accuracy on the official test data. |
Similar Papers
Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report (D19-60)
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| Challenge: | The workshop on Commonsense Inference in NLP (COIN) evaluated text understanding systems' ability to draw inferences about facts that are not mentioned in the text, but that are assumed to be common ground. |
| Approach: | They propose to use commonsense knowledge to evaluate systems' ability to answer questions/queries about a text. |
| Outcome: | The proposed tasks evaluated systems in two contexts: Commonsense Inference and Commonsensible Inference. |
IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension (D19-60)
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| Challenge: | Using pre-trained language models, we can model machine comprehension using commonsense reasoning. |
| Approach: | They propose a machine comprehension model that leverages pre-trained language models over commonsense knowledge bases. |
| Outcome: | The proposed model improves on baseline models and other commonsense knowledge bases. |
BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations (D19-60)
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| Challenge: | Experimental results show that our system achieves significant improvements over the baseline systems with 84.2% accuracy on the official test dataset. |
| Approach: | They propose a system to inject more external knowledge into everyday narrations . they use a pre-trained BERT model to fine-tune on a machine reading comprehension dataset . |
| Outcome: | The proposed system achieves significant improvements over baseline systems with 84.2% accuracy on the official test dataset. |
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks (D19-60)
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| Challenge: | Existing approaches to represent knowledge in the low-dimensional space are to leverage large-scale unsupervised text corpus to train fixed or contextual representations. |
| Approach: | They propose to leverage large-scale unsupervised text corpus to train fixed or contextual language representations and to express knowledge into a knowledge graph (KG) they incorporate distributional representations of a KG onto the representations from pre-trained language models, via simply concatenation or multi-head attention. |
| Outcome: | The proposed models outperform the other models on the COIN: COmmonsense INference in Natural Language Processing (COIN) Workshop datasets. |
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (P19-1)
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| Challenge: | Existing studies on commonsense knowledge base construction only store loosely structured open-text descriptions of knowledge. |
| Approach: | They propose a commonsense knowledge base construction model that generates rich commonsensense descriptions in natural language. |
| Outcome: | The proposed models can generate rich and diverse commonsense descriptions in natural language. |
Jeff Da at COIN - Shared Task: BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge (D19-60)
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| Challenge: | Recent studies show that large-scale pre-training models can be effective for large datasets. |
| Approach: | They propose a method of integrating contextual embeddings with commonsense graph embeddINGs by preprocessing knowledge bases and aligning tokens between misaligned tokenization methods. |
| Outcome: | The proposed method achieves higher accuracy than BERT and scores highest without pretraining. |
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing (D19-60)
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| Challenge: | Workshop on Commonsense Inference in Natural Language Processing focuses on commonsense knowledge representation and application in NLP tasks. |
| Approach: | COIN is a workshop on commonsense inference in natural language processing . workshop included two shared tasks on reading comprehension using commonsensense knowledge . |
| Outcome: | the workshop focused on modeling commonsense knowledge and commonsensing in natural language processing tasks. |
Coconut: Contextualized Commonsense Unified Transformers for Graph-Based Commonsense Augmentation of Language Models (2024.findings-acl)
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| Challenge: | Existing studies show that pre-trained language models lack commonsense knowledge . |
| Approach: | They propose a contextualized knowledge prompting scheme to guide the contextualization of structured commonsense knowledge based on large language models. |
| Outcome: | The proposed approach outperforms the state-of-the-art technique by an average of 5.8%. |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning (2020.coling-main)
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| Challenge: | a large amount of pre-defined commonsense knowledge is available for commonsensense reasoning . humans acquire commonsence in their lives, but machines cannot learn commonseense without assistance. |
| Approach: | They propose an AMR-ConceptNet-Pruned (ACP) graph that is pruned from a full integrated graph . they show that the ACP graph interprets the reasoning path and predicts the correct answer . |
| Outcome: | The proposed graph outperforms baseline models in the commonsenseQA task . it shows that the reasoning path can be interpreted with the relations and concepts provided by the graph . |