DESED : Dialogue-based Explanation for Sentence-level Event Detection (2022)

Abstract: Many recent sentence-level event detection efforts focus on enriching sentence semantics, e.g., via multi-task or prompt-based learning. Despite the promising performance, these methods commonly depend on label-extensive manual annotations or require domain expertise to design sophisticated templates and rules. This paper proposes a new paradigm, named dialogue-based explanation, to enhance sentence semantics for event detection. By saying dialogue-based explanation of an event, we mean explaining it through a consistent information-intensive dialogue, with the original event description as the start utterance. We propose three simple dialogue generation methods, whose outputs are then fed into a hybrid attention mechanism to characterize the complementary event semantics. Extensive experimental results on two event detection datasets verify the effectiveness of our method and suggest promising research opportunities in the dialogue-based explanation paradigm.

## introduction event detection (ed) is a crucial task in information extraction, which aims to identify event triggers (words or phrases that indicate events) and classify triggers into predefined event types. for example, we can identify the trigger weddings and classify it into marry event type from the text "giuliani regularly officiated at weddings while in office". sentence-level event detection plays a dominant role in event detection and is significant for various downstream nlp tasks. however, it is usually challenging to accurately detect events in a single sentence due to the limited information. therefore, most prior methods on sentence-level event detection make improvements by enhancing sentence semantics, being divided into two categories. the first category mainly involves leveraging other information extraction tasks (e.g., named entity recognition and relation extraction) via multi-task learning @xcite @xcite . however, these efforts highly depend on task-specific annotation, costing a vast amount of human effort. the other popular line of research exploits pretrained language models (plms), e.g., via prompt-based learning @xcite @xcite . mrc-based methods, which treat a task as a machine reading comprehension task @xcite @xcite , can also be regarded as a weaker version of prompt-based learning. one common bottleneck among these methods lies in their reliance on domain expertise and human efforts to devise sophisticated templates and rules. to enhance sentence semantics more effectively and efficiently, this work proposes to use generative models to generate contextual information for a sentence in the form of a dialogue, which consists of multiple utterances between different roles on a particular topic. as two motivation examples, figure 1 shows two real-world dialogues. in the utterance from user_1 in the first example, models are easily induced by arrested and protesting thus identifying fired as an attack event, but the subsequent utterances serve as original sentence: giuliani regularly officiated at weddings while in office. ## methodology in this section, we present our dialogue-based explanation for sentence-level event deteiction. ## conclusion in this paper, we propose a new paradigm, dialoguebased explanation, to enhance sentence semantics for sentence-level event detection. we propose three conceptually simple methods to generate dialogues for given original sentences, which concentrate on casual dialogues, focused dialogues and domain-specific dialogues respectively. to make effective use of generated dialogues, we design hybrid attention mechanisms at different levels of granularity. extensive experiments and analyses show that desed has promising performance on event detection. in the future, we are interested in generating dialogue-based explanation in a more controllable way and extending dialogue-based explanation to other tasks.