Towards Event Extraction from Speech with Contextual Clues
CoRR(2024)
摘要
While text-based event extraction has been an active research area and has
seen successful application in many domains, extracting semantic events from
speech directly is an under-explored problem. In this paper, we introduce the
Speech Event Extraction (SpeechEE) task and construct three synthetic training
sets and one human-spoken test set. Compared to event extraction from text,
SpeechEE poses greater challenges mainly due to complex speech signals that are
continuous and have no word boundaries. Additionally, unlike perceptible sound
events, semantic events are more subtle and require a deeper understanding. To
tackle these challenges, we introduce a sequence-to-structure generation
paradigm that can produce events from speech signals in an end-to-end manner,
together with a conditioned generation method that utilizes speech recognition
transcripts as the contextual clue. We further propose to represent events with
a flat format to make outputs more natural language-like. Our experimental
results show that our method brings significant improvements on all datasets,
achieving a maximum F1 gain of 10.7
https://github.com/jodie-kang/SpeechEE.
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