ECONET : Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2021)

Abstract: While pre-trained language models (PTLMs) have achieved noticeable success on many NLP tasks, they still struggle for tasks that require event temporal reasoning, which is essential for event-centric applications. We present a continual pre-training approach that equips PTLMs with targeted knowledge about event temporal relations. We design self-supervised learning objectives to recover masked-out event and temporal indicators and to discriminate sentences from their corrupted counterparts (where event or temporal indicators got replaced). By further pre-training a PTLM with these objectives jointly, we reinforce its attention to event and temporal information, yielding enhanced capability on event temporal reasoning. This **E**ffective **CON**tinual pre-training framework for **E**vent **T**emporal reasoning (ECONET) improves the PTLMs’ fine-tuning performances across five relation extraction and question answering tasks and achieves new or on-par state-of-the-art performances in most of our downstream tasks.

## introduction reasoning event temporal relations is crucial for natural language understanding, and facilitates many real-world applications, such as tracking biomedical histories @xcite @xcite , generating stories @xcite , and forecasting social events @xcite . in this work, we study two prominent event temporal reasoning tasks as shown in figure 1 : event relation extraction (ere) @xcite @xcite that predicts temporal relations between a pair of events, and machine reading comprehension (mrc) @xcite where a passage and a question 1 reproduction code, training data and models are available here: https://github.com/pluslabnlp/econet . about event temporal relations is presented, and models need to provide correct answers using the information in a given passage. recent approaches leveraging large pre-trained language models (ptlms) achieved state-of-theart results on a range of event temporal reasoning tasks @xcite @xcite @xcite . despite the progress, vanilla ptlms do not focus on capturing event temporal knowledge that can be used to infer event relations. for example, in figure 1 , an annotator of the qa sample can easily infer from the temporal indicator "following" that "transfer" happens before "preparing the paperwork", but a fine-tuned roberta model predicts that "transfer" has no such relation with the event "preparing the paperwork." plenty of such cases exist in our error analysis on ptlms for event temporal relation-related tasks. we hypothesize that such deficiency is caused by original ptlms' random masks in the pre-training where temporal indicators and event triggers are under-weighted and hence not attended well enough for our downstream tasks. ## method our proposed method aims at addressing the issue in vanilla ptlms that event triggers and temporal indicators are not adequately attended for our downstream event reasoning tasks. to achieve this goal, we propose to replace the random masking in ptlms with a targeted masking strategy designed specifically for event triggers and temporal indicators. we also propose a continual pre-training method with mask prediction and contrastive loss that allows models to effectively learn from the targeted masked samples. the benefits of our method are manifested by stronger fine-tuning performances over downstream ere and mrc tasks. our overall approach econet consists of three components. 1) creating targeted self-supervised training data by masking out temporal indicators and event triggers in the input texts; 2) leveraging mask prediction and contrastive loss to continually train ptlms, which produces an event temporal knowledge aware language model; 3) fine-tuning the enhanced language model on downstream ere and mrc datasets. we will discuss each of these components in the following subsections. ## experimental setup in this section, we describe details of implementing econet, datasets and evaluation metrics, and discuss compared methods reported in section 4. ## results and analysis as shown in ## conclusion and future work in summary, we propose a continual training framework with targeted mask prediction and contrastive loss to enable ptlms to capture event temporal knowledge. extensive experimental results show that both the generator and discriminator components can be helpful to improve fine-tuning performances over 5 commonly used data in event temporal reasoning. the improvements of our methods are much more pronounced in low-resource settings, which points out a promising direction for few-shot learning in this research area.