mlsanityData to 14 Oct 2026

Towards Relation Extraction From Speech

arXiv 2210.08759 ↗First posted 17 Oct 2022cs.CL cs.MMcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 23 of its 34 references that are in the map. First posted on arXiv 17 Oct 2022.

Builds on

23 of 34 references.

References · 23

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  4. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  5. 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 561
  6. 2022Self-Supervised Speech Representation Learning: A Reviewcited by 260
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  10. 2018Towards end-to-end spoken language understandingcited by 92
  11. 2020Speech Translation and the End-to-End Promise: Taking Stock of Where We Arecited by 77
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11 more
  1. 2020TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linkingcited by 72
  2. 2020Dialogue-Based Relation Extractioncited by 68
  3. 2021A Survey on Spoken Language Understanding: Recent Advances and New Frontierscited by 53
  4. 2020More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extractioncited by 52
  5. 2021PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extractioncited by 40
  6. 2021Curriculum-Meta Learning for Order-Robust Continual Relation Extractioncited by 34
  7. 2020Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trendscited by 28
  8. 2019Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Modelcited by 24
  9. 2021End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021cited by 16
  10. 2022M-Adapter: Modality Adaptation for End-to-End Speech-to-Text Translationcited by 15
  11. 2022Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extractioncited by 7

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Cited by year

Citing papers per year: 2022: 2; 2023: 3; 2024: 3; 2025: 2.

10 papers.

  1. 2023Editing Language Model-based Knowledge Graph Embeddingscited by 23
  2. 2023Continual Multimodal Knowledge Graph Constructioncited by 15
  3. 2023CopyNE: Better Contextual ASR by Copying Named Entitiescited by 14
  4. 2022Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Studycited by 5
  5. 2025Event Extraction in Large Language Modelcited by 5
  6. 2024Towards Event Extraction from Speech with Contextual Cluescited by 3
  7. 2024Double Mixture: Towards Continual Event Detection from Speechcited by 1
  8. 2024wav2graph: A Framework for Supervised Learning Knowledge Graph from Speechcited by 1
  9. 2025Continual Speech Learning with Fused Speech Featurescited by 1
  10. 2022Mining Word Boundaries in Speech as Naturally Annotated Word Segmentation Datacited by 0