Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning (2022)

Abstract: Commonsense reasoning systems should be able to generalize to diverse reasoning cases. However, most state-of-the-art approaches depend on expensive data annotations and overfit to a specific benchmark without learning how to perform general semantic reasoning. To overcome these drawbacks, zero-shot QA systems have shown promise as a robust learning scheme by transforming a commonsense knowledge graph (KG) into synthetic QA-form samples for model training. Considering the increasing type of different commonsense KGs, this paper aims to extend the zero-shot transfer learning scenario into multiple-source settings, where different KGs can be utilized synergetically. Towards this goal, we propose to mitigate the loss of knowledge from the interference among the different knowledge sources, by developing a modular variant of the knowledge aggregation as a new zero-shot commonsense reasoning framework. Results on five commonsense reasoning benchmarks demonstrate the efficacy of our framework, improving the performance with multiple KGs.

## introduction the ability to understand natural language through commonsense reasoning is one of the core focuses in the field of natural language processing. to measure and study the different aspects of commonsense reasoning, several datasets are developed, such as socialiqa @xcite , com-monsenseqa @xcite , and physi-caliqa @xcite , each requiring different type of commonsense knowledge (e.g., social, taxonomic, causal, declarative, etc) to select the correct answer. while large-scale neural systems @xcite @xcite have shown human-level accuracy on these benchmarks, recent studies @xcite also criticize that these models solve individual datasets, rather than learning how to perform general semantic reasoning. to this end, @xcite suggested zero-shot evaluation as a genuine measure for the reasoning capability of the machine. inspired by this new metric, in this work, we focus on building unsupervised zero-shot multiplechoice qa systems. that is, we target an arbitrary commonsense reasoning task where conventional approaches (that rely heavily on task-specific supervision) are not applicable to such zero-shot learning scenarios. to learn qa models without expensive annotation efforts, recent works @xcite @xcite propose to generate a synthetic qa dataset using a commonsense kg such as atomic @xcite and conceptnet @xcite . such an approach mostly focuses only on one specific type of reasoning relations (e.g., if-then relation, or declarative relation), neglecting the fact that real-world qa systems require simultaneously considering different types of reasoning abilities (e.g., declarative and social, or causal and physical reasoning; @xcite . to consider different types of reasoning, this paper extends ideas from the aforementioned zeroshot learning to the multi-source case such that it benefits from different types of commonsense knowledge on individual kgs. for example, atomic @xcite focuses on social commonsense while conceptnet @xcite ) contains conceptual knowledge. a practical approach is multi-task learning (mtl; @xcite , which learns a shared encoder for different synthetic qa datasets from multiple kgs. despite its effectiveness, mtl scheme suffers from interference among different kgs, which results in forgetting previously learned knowledge when trained on new kg which has different kinds of knowledge @xcite @xcite . to address these limitations, we propose a novel, modularized framework that aims to learn multiple expert models for kgs, then conduct zero-shot fusion to allow collaboration among kgs. for this purpose, we leverage adapterfusion @xcite where multiple tiny modules between transformer blocks called adapters @xcite can be combined after independent training, thus allowing a continual integration of the adapters without retraining the entire framework. specifically, we treat the adapters as different kg-specific experts, and combine them using an attention-like fusion module. to improve the fusion of adapters, we suggest a kg-alignment adapter that guides to the apt expert adapters. here, we use kgs in three different synthetic supervision training: (1) kg-specific qa datasets to train the kg-specific expert adapters, (2) a kg classification datasets to train the kg-alignment adapter, and (3) a balanced mixture of kg-specific qa datasets to train the fusion module. our modularized method alleviates the interference between different kgs, which is the pitfall of mtl from our empirical observation, and thus combines multiple kgs into a synergetic zero-shot framework. our contributions are: (1) we suggest a simple, yet effective kg modularization strategy for the use of multiple kgs in commonsense reasoning. (2) we then explore the use of adapterfusion @xcite for better knowledge aggregation based on the kg modularization in zero-shot setting. we believe that such modularized transfer learning is critical to using different knowledge sources synergetically against interference between them. (3) in extensive experiments on various commonsense reasoning benchmarks, our framework achieves significant improvements over baselines using a single kg, even using multiple kgs, which implies the robustness in commonsense reasoning. ## modularized zero-shot framework in our setup, we repurpose synthetic qa generation @xcite for the task of knowledgedriven zero-shot learning for commonsense reasoning, i.e., we transform a kg into multiple (q i , a i ) pairs where q i is a natural language question and a @xmath0,1 , ..., a i,m } is the set of options with m answer candidates. specifically, given a triple (e head , r, e tail ) in a kg, where e head , e tail and r denote head/tail entity and relation respectively, we transform e head and r into a natural language question q i using templates. for the option set a i , we use the combination of the correct answer e tail and m -1 distractors which are tail entities from other triples sampled randomly @xcite . details are described in appendix b. formally, we denote (q i , a i ) as one qa sample. the goal is to learn a qa model from the synthetic qa sample. in a downstream task (e.g., reasoning benchmarks such as socialiqa and com-monsenseqa), we need to predict answers given non-synthetic test samples (q test , a test ). a dataset with n k samples for kg k as follows: for effective use of multiple kgs at once with less interference, we present a modularized framework, which is a novel approach to knowledge transfer for the zero-shot setting as shown in figure 1. as a modular approach, we train the multiple kg-specific adapters (expert adapters) with each dataset from kg. based on these pre-trained adapters, we use a zero-shot fusion method to aggregate knowledge of each adapter leveraging adapterfusion @xcite as a base architecture with the balanced mixture of each kg dataset. further, for better knowledge fusion, we suggest a kg-alignment aware adapter (kg-classifier adapter) as a guide for detecting alignment with given sample in zero-shot reasoning. here, we utilize kg classification dataset by verifying the synthetic qas. algorithm 1 in appendix outlines the overall process of our proposed framework. we summarize the notations in appendix a. ## experiments in this section we evaluate the efficacy of our framework on five commonsense reasoning tasks. we denote kg-classifier adapter by kg-c adapter. ## conclusion despite the existence of various types of commonsense kgs, utilizing multiple kgs has not been explored enough in the commonsense reasoning field. motivated by this, this paper proposes a modularized transfer learning framework to fuse the knowledge from multiple kgs efficiently for zero-shot commonsense reasoning. our framework consists of kg modularization for expert adapter, zero-shot fusion and kg-classifier adapter. extensive experiments show that our framework obtains strong improvements over mtl on five commonsense reasoning benchmarks. in the future, our work can be extended to adapt our methods to further various multiple kgs with studies of appropriate scale for kg modularization. in addition, based on our hypothesis that the existence of an optimal combination, we can explore the study for the optional use of modularized kg experts for the best transfer learning.