P seudo R easoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population (2022)

Abstract: Commonsense Knowledge Base (CSKB) Population aims at reasoning over unseen entities and assertions on CSKBs, and is an important yet hard commonsense reasoning task. One challenge is that it requires out-of-domain generalization ability as the source CSKB for training is of a relatively smaller scale (1M) while the whole candidate space for population is way larger (200M). We propose PseudoReasoner, a semi-supervised learning framework for CSKB population that uses a teacher model pre-trained on CSKBs to provide pseudo labels on the unlabeled candidate dataset for a student model to learn from. The teacher can be a generative model rather than restricted to discriminative models as previous works.In addition, we design a new filtering procedure for pseudo labels based on influence function and the student model’s prediction to further improve the performance. The framework can improve the backbone model KG-BERT (RoBERTa-large) by 3.3 points on the overall performance and especially, 5.3 points on the out-of-domain performance, and achieves the state-of-the-art. The codes will be made public on acceptance. Codes and data are available at https://github.com/HKUST-KnowComp/PseudoReasoner.

## introduction commonsense knowledge are the common agreements by most people on daily entities, which are crucial for intelligent systems to act sensibly in the real world @xcite . endowing natural language understanding systems with the ability to draw commonsense reasoning remains an important yet challenging task. throughout the development of automated commonsense understanding, commonsense knowledge base (cskb) is an important form of automatic commonsense reasoning system to store knowledge sources for drawing inferences. with expert-curated relations and human annotations, cskbs such as conceptnet @xcite , as those human-annotated cskbs are sparse and usually of a small scale and coverage, reasoning tasks on cskb such as cskb completion @xcite @xcite and cskb population @xcite are defined with the goal of either adding new edges/assertions within the training knowledge base (cskb completion), or adding new edges/assertions from outside of cskbs (cskb population). a visualized comparison between the two tasks is shown in figure 1 . different from cskb completion, which adopts a close-world assumption and assume all knowledge are in-domain, the population task deals with unseen entities and requires a more out-ofdistribution reasoning ability. in this paper, we study commonsense reasoning in the context of cskb population. in this task, four mainstream cskbs, conceptnet, atomic, atomic 20 20 , and glucose are aligned together as the labeled dataset. aser @xcite , a large-scale eventuality (events, activities, and states) knowledge graph is aligned with cskbs and serves as unlabeled candidates for populating commonsense knowledge. human annotations on held-out dev/test sets sampled from both cskbs and aser are provided as the evaluation set. there are two major challenges remaining unsolved for cskb population. first, the scale of the annotated training set (conceptnet, atomic, and glucose) is approximately 1m samples, too small compared with 200m of the actual candidate space to perform population (aser). second, as inherently only ground-truth (positive) examples are provided by cskbs, the randomly sampled negative examples in the task are less informative and may lead the model to overfit artifacts of the dataset. a supervised learning model finetuned on such annotated training set is hard to be generalized to out-of-domain knowledge space, as shown in @xcite and also to address the above challenges, we propose pseudoreasoner, a semi-supervised learning framework that uses a pre-trained commonsense teacher model to automatically label the unlabeled candidates to serve as pseudo labels, such that the student model can be further finetuned with pseudo labels to improve out-of-domain commonsense reasoning ability. in fact, pre-trained language models finetuned on commonsense knowledge bases have shown to perform generalizable commonsense reasoning on downstream tasks to some extent. for example, leveraging commonsense knowledge generated by comet @xcite , a language model finetuned on atomic, can improve the performance on commonsense qa @xcite @xcite . different from the text generation paradigm as in previous works, here we leverage the commonsense language model as a teacher model for labeling unlabeled candidates. to further improve the quality of pseudo labels, we use both influence function @xcite and the student model's prediction to select highly confident pseudo exam-ples. our contribution is three-fold: ## methods in this section, we present the details of the framework of pseudoreasoner. a sketch illustration of the model is presented in figure 2 . to sum up, the procedure of pseudoreasoner can be summarized into the following steps: triples with high/low plausibility scores within pre-defined intervals are given label 1/0 (section 4.1.2). ## analysis and discussions in this section, we discuss the ablation study on model components, the comparisons with semisupervised learning baselines, diversity analysis, and discussions about why pseudoreasoner works. ## conclusion in this paper, we propose a semi-supervised learning framework for cskb population based on pseudo labels. using a teacher model and a special filtering mechanism on pseudo labels, we achieve the state-of-the-art of cskb population in terms of both in-domain and out-of-domain performance. experiments also show that our cskb population benefits more from high-quality novel knowledge than other semi-supervised learning techniques such as noise and consistency training. this work brings a new perspective of improving out-of-domain generalizable commonsense reasoning ability on cskbs.