Towards Relation Extraction From Speech
This work addresses a gap in extracting semantic relationships from spoken language, which is incremental as it adapts existing text-based methods to speech with new datasets and models.
The paper tackles the problem of relation extraction directly from speech, addressing error propagation from automatic speech recognition (ASR) and exploring end-to-end methods, with results showing that their proposed SpeechRE model outperforms pipeline approaches on a crowd-sourced test dataset.
Relation extraction typically aims to extract semantic relationships between entities from the unstructured text. One of the most essential data sources for relation extraction is the spoken language, such as interviews and dialogues. However, the error propagation introduced in automatic speech recognition (ASR) has been ignored in relation extraction, and the end-to-end speech-based relation extraction method has been rarely explored. In this paper, we propose a new listening information extraction task, i.e., speech relation extraction. We construct the training dataset for speech relation extraction via text-to-speech systems, and we construct the testing dataset via crowd-sourcing with native English speakers. We explore speech relation extraction via two approaches: the pipeline approach conducting text-based extraction with a pretrained ASR module, and the end2end approach via a new proposed encoder-decoder model, or what we called SpeechRE. We conduct comprehensive experiments to distinguish the challenges in speech relation extraction, which may shed light on future explorations. We share the code and data on https://github.com/wutong8023/SpeechRE.