Learning P hysical C ommon S ense as K nowledge G raph C ompletion via BERT D ata A ugmentation and C onstrained T ucker F actorization (2020)

Abstract: Physical common sense plays an essential role in the cognition abilities of robots for human-robot interaction. Machine learning methods have shown promising results on physical commonsense learning in natural language processing but still suffer from model generalization. In this paper, we formulate physical commonsense learning as a knowledge graph completion problem to better use the latent relationships among training samples. Compared with completing general knowledge graphs, completing a physical commonsense knowledge graph has three unique characteristics: training data are scarce, not all facts can be mined from existing texts, and the number of relationships is small. To deal with these problems, we first use a pre-training language model BERT to augment training data, and then employ constrained tucker factorization to model complex relationships by constraining types and adding negative relationships. We compare our method with existing state-of-the-art knowledge graph embedding methods and show its superior performance.

## introduction physical common sense means understanding the physical properties of objects and how they can be manipulated @xcite . empowering natural language processing (nlp) methods with physical common sense is important when dealing with tasks that are related to the physical world, such as physical commonsense reasoning @xcite , grounded verb semantics @xcite , and the more general human-robot interaction problem. generally, there are currently three methods of learning physical common sense: manual annotation, text mining, and machine learning. manual annotation is difficult for human annotators due to inconsistent perceptions and the challenge of enumerating all physical facts. mining text data is also challenging because some physical facts are not written in texts explicitly. machine learning is a promising method to discover new physical facts using existing data. @xcite formulate physical commonsense learning as three separate machine learning tasks: 1) given an object and a property, predicting whether they follow an objectproperty (op) relationship, e.g., an apple is edible; 2) given an object and an affordance, predicting whether they follow an object-affordance (oa) relationship, e.g., he drove the car; and 3) given an affordance and a property, predicting whether they follow an affordance-property (ap) relationship, e.g., if you can eat something, then it is edible. however, it is difficult for a machine learning model to generalize through the use of the latent relationships among samples. for example, even if we have a training sample an apple is edible, it is hard to say that the trained model can generalize to predict a testing sample an apple is red correctly. in this paper, we propose to model physical commonsense learning as a knowledge graph completion problem to better use the latent relationships among samples. an knowledge graph can be represented as a 3-way binary tensor, and each entry is in triple form (e h , r, e t ) @xcite , where e h denotes the head entity, e t denotes the tail entity, r denotes the relationship between e h and e t , (e h , r, e t ) = 1 denotes the fact is true in the training data, and (e h , r, e t ) = 0 denotes the fact does not exist or is false in the training data. the goal of knowledge graph completion is to predict the real value of (e h , r, e t ) when it is missing or its label is wrong in the training data. in terms of physical common sense, entities come from the set of all objects, properties, and affordances, and relationships come from the set of op, oa, and ap. compared with general knowledge graphs such as dbpedia @xcite and freebase @xcite , a physical commonsense knowledge graph has at least three characteristics: 1) training facts are scarce. for example, when labeling the properties of an object, people usually name the ones that are easiest to think of but cannot enumerate all properties. 2) not all facts can be mined from existing texts. for example, the relationships between affordances and properties usually do not appear in texts explicitly and need to be reasoned. the contributions of this paper are: 1) we formulate physical commonsense learning as a knowledge graph completion problem, 2) we propose a novel pipeline that combines pre-training models and knowledge graph embedding to learn physical common sense, and experiment results show its superior performance. ## method our method consists of two components: 1) we first augment all pairs of op and oa tasks using bert; 2) with the training data of op, oa, and ap as input, we use constrained tucker factorization to de-noise and complete the knowledge graph. in particular, we use typed constraints to reduce the solution space and add negative relationships to leverage negative training samples. ## experiments to evaluate the method, we conducted experiments with a physical commonsense dataset @xcite on triple classification and link prediction. to simplify the problem, we only used the situated op, oa, and ap data, which contains 80 objects, 50 properties, and 504 affordances. the statistics are shown in mult, complex, simple, and tucker foot_0 . we optimized equation 2 with adam in pytorch and did not optimize the regularization explicitly. λ was set to 0.1 through a 5-fold cross validation. d e and d r were set to 200 by default. ## conclusion in this paper, we formulate physical commonsense learning as a knowledge graph completion problem. we first use bert to augment training data of op and oa, and then employ constrained tucker factorization to complete the knowledge graph. we constrain types to reduce the solution space and add negative relationships to leverage negative training samples. compared with typical knowledge graph embedding methods, our results show good performance on triple classification and link prediction. our method also has the potential to be a generic approach to benefit performance on the knowledge graph completion problem.