Integrating External Event Knowledge for Script Learning

Abstract

Script learning aims to predict the subsequent event according to the existing event chain. Recent studies focus on event co-occurrence to solve this problem. However, few studies integrate external event knowledge to solve this problem. With our observations, external event knowledge can provide additional knowledge like temporal or causal knowledge for understanding event chain better and predicting the right subsequent event. In this work, we integrate event knowledge from ASER (Activities, States, Events and their Relations) knowledge base to help predict the next event. We propose a new approach consisting of knowledge retrieval stage and knowledge integration stage. In the knowledge retrieval stage, we select relevant external event knowledge from ASER. In the knowledge integration stage, we propose three methods to integrate external knowledge into our model and infer final answers. Experiments on the widely-used Multi-Choice Narrative Cloze (MCNC) task show our approach achieves state-of-the-art performance compared to other methods.

Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning

“…First, using external knowledge, especially external event graph knowledge, increases the accuracy of models. For example, the knowledge infusion approach proposed by Lv et al (2020b) outperforms the RoBERTa model without any knowledge. Second, our approach is superior to the retrieval and integration approach, RoBERTa + Knwl, and achieves new state-of-the-art accuracy (i.e.…”

“…Consequently, if they try to model the inferential relations between events, they have to generate all possible triples for each event by traversing all relations and enlarging beam-search size (Bosselut et al, 2019a). And the generated triple must be re-encoded into latent space for the integration (Lv et al, 2020b), not to mention generative models not always reliable.…”

“…Method ACC (%) w/o external knowledge Random 20.00 PMI (Chambers and Jurafsky, 2008) 30.52 Bigram (Jans et al, 2012) 29.67 Word2vec (Le and Mikolov, 2014) 42.23 Event-Comp (Granroth-Wilding and Clark, 2016) 49.57 PairLSTM (Wang et al, 2017) 50.83 SGNN (Li et al, 2018) 52.45 RoBERTa base (Lv et al, 2020b) 56.23 w/ external knowledge SGNN + Int&Senti (Ding et al, 2019) 56.03 RoBERTa base + Knwl. (Lv et al, 2020b) 58 Datasets and Knowledge Graph. Following prior works (Lv et al, 2020b) for script reasoning, we evaluate our proposed approach on the dataset published by Li et al (2018), which is widely used for the MCNC task.…”

“…Script reasoning (Chambers and Jurafsky, 2008;Li et al, 2018;Lv et al, 2020b) aims at determining the subsequent event or plausible ending for an event chain in a script. For example, a tourism script consist of ["Emily took a plane", "Emily arrived at Oahu", "Emily went to Waimea Bay"], and the subsequent event is more likely to be "Emily surfed" than "Emily skied".…”

“…Recently, Lv et al (2020b) propose to leverage a large-scale eventuality knowledge graph (KG), ASER (Zhang et al, 2020), for script reasoning via adopting the "retrieval and integration" paradigm. Given an event chain, this paradigm first retrieves relevant triple facts from the eventuality KG and then integrates them into a script reasoning model.…”