Knowledge bases are represented as a graph, with nodes consisting of conceptual entities (i.e. dog , running away, excited, etc.) and the pre-defined edges representing the nature of the relations between concepts (IsA, UsedFor, CapableOf, etc.). Commonsense knowledge base completion (CKBC) is a machine learning task motivated by the need to improve the coverage of these resources.
In this formulation of the problem, one is supplied with a list of candidate entityrelation-entity triples, and the task is to distinguish which of the triples express valid commonsense knowledge and which are fictitious (Li et al., 2016). 严格来说，这其实是个分类问题。输入时triple，输出是二分类结果。
现在的一些方法在held-out test set上效果好，但是在新数据上效果差。因此我们提出了不要在某个特定的数据集上训练模型，而是直接利用 the world knowledge of large language models to identify commonsense facts directly。用language model来模拟真实性。
For our masked model, we use BERT-large (Devlin et al., 2018). For sentence
ranking, we use the GPT-2 117M LM (Radford et al., 2019).
Model Graph:
Result:：
Though our approach performs worse on a held-out test set developed by Li et al. (2016), it does so without any previous exposure to the ConceptNet database, ensuring that this performance is not biased.
Summary:
Resource:
Paper information:
Notes:
Knowledge bases are represented as a graph, with nodes consisting of conceptual entities (i.e. dog , running away, excited, etc.) and the pre-defined edges representing the nature of the relations between concepts (IsA, UsedFor, CapableOf, etc.). Commonsense knowledge base completion (CKBC) is a machine learning task motivated by the need to improve the coverage of these resources.
In this formulation of the problem, one is supplied with a list of candidate entityrelation-entity triples, and the task is to distinguish which of the triples express valid commonsense knowledge and which are fictitious (Li et al., 2016). 严格来说，这其实是个分类问题。输入时triple，输出是二分类结果。
现在的一些方法在held-out test set上效果好，但是在新数据上效果差。因此我们提出了不要在某个特定的数据集上训练模型，而是直接利用 the world knowledge of large language models to identify commonsense facts directly。用language model来模拟真实性。
For our masked model, we use BERT-large (Devlin et al., 2018). For sentence ranking, we use the GPT-2 117M LM (Radford et al., 2019).
Model Graph:
Result:：
Though our approach performs worse on a held-out test set developed by Li et al. (2016), it does so without any previous exposure to the ConceptNet database, ensuring that this performance is not biased.
Thoughts:
Next Reading: