Entity resolution for noisy ASR transcripts

Raghuvanshi, Arushi; Ramakrishnan, V.; Embar, Varsha; Carroll, Lucien; Raghunathan, Karthik

doi:10.18653/v1/d19-3011

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…Our work is also closely related to two entity-level tasks: entity correction and entity linking. Entity correction [7,8,9,10] aims to tackle errors occurring in Automatic Speech Recognition (ASR) systems. These studies adopt an "entityto-entity" approach for entity correction, however, we take the query as context, and perform contextualized entity correction.…”

Section: Related Workmentioning

confidence: 99%

“…It is a two-layer system, retrieving and re-ranks utterances and NLU hypotheses. Since we do not assume the entities are tagged in utterances, "entity-to-entity" approaches [7,8,9,10] are not appropriate baselines. Besides gUFS-QR, we also consider our two-layer entity correction system without the KG components, which is a similar design as popular entity linking system [11], as a baseline.…”

Section: Evaluation Metrics and Baselinementioning

confidence: 99%

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KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting

Jinglun¹,

Li²,

Jiang³

et al. 2023

Preprint

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Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions. When there is an entity error, it imposes extra challenges for a dialogue system to produce satisfactory responses. In this work, we propose KG-ECO: Knowledge Graph enhanced Entity COrrection for query rewriting, an entity correction system with corrupt entity span detection and entity retrieval/re-ranking functionalities.To boost the model performance, we incorporate Knowledge Graph (KG) to provide entity structural information (neighboring entities encoded by graph neural networks) and textual information (KG entity descriptions encoded by RoBERTa). Experimental results show that our approach yields a clear performance gain over two baselines: utterance level QR and entity correction without utilizing KG information. The proposed system is particularly effective for few-shot learning cases where target entities are rarely seen in training or there is a KG relation between the target entity and other contextual entities in the query.

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Section: Related Workmentioning

confidence: 99%

Section: Evaluation Metrics and Baselinementioning

confidence: 99%

KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting

Jinglun¹,

Li²,

Jiang³

et al. 2023

Preprint

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show abstract

“…Errors encountered in either model do not inform the other; in practice this means incorrect ASR transcriptions might be "correctly" interpreted by the NLU, fail to provide the user's desired response. While work is ongoing in detecting [8], quantifying [9,10], and rectifying [11,12] these ASR driven NLU misclassifications, end-to-end (E2E) approaches are a promising way to address this issue.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Spoken Language Understanding for Generalized Voice Assistants

Saxon¹,

Choudhary²,

McKenna³

et al. 2021

Interspeech 2021

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End-to-end (E2E) spoken language understanding (SLU) systems predict utterance semantics directly from speech using a single model. Previous work in this area has focused on targeted tasks in fixed domains, where the output semantic structure is assumed a priori and the input speech is of limited complexity. In this work we present our approach to developing an E2E model for generalized SLU in commercial voice assistants (VAs). We propose a fully differentiable, transformer-based, hierarchical system that can be pretrained at both the ASR and NLU levels. This is then fine-tuned on both transcription and semantic classification losses to handle a diverse set of intent and argument combinations. This leads to an SLU system that achieves significant improvements over baselines on a complex internal generalized VA dataset with a 43% improvement in accuracy, while still meeting the 99% accuracy benchmark on the popular Fluent Speech Commands dataset. We further evaluate our model on a hard test set, exclusively containing slot arguments unseen in training, and demonstrate a nearly 20% improvement, showing the efficacy of our approach in truly demanding VA scenarios.

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“…Noise generator.3.2 Deep MetaphoneRaghuvanshi et al (2019) revealed that relying only on surface text similarities cannot capture phonetic differences between words. Furthermore,Han et al (2013) showed that sound-related features are effective in text normalization.…”

mentioning

confidence: 99%

Neural text normalization leveraging similarities of strings and sounds

Kawamura¹,

Aoki

Kamigaito

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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We propose neural models that can normalize text by considering the similarities of word strings and sounds. We experimentally compared a model that considers the similarities of both word strings and sounds, a model that considers only the similarity of word strings or of sounds, and a model without the similarities as a baseline. Results showed that leveraging the word string similarity succeeded in dealing with misspellings and abbreviations, and taking into account the sound similarity succeeded in dealing with phonetic substitutions and emphasized characters. So that the proposed models achieved higher F 1 scores than the baseline.

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Entity resolution for noisy ASR transcripts

Cited by 10 publications

References 13 publications

KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting

KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting

End-to-End Spoken Language Understanding for Generalized Voice Assistants

Neural text normalization leveraging similarities of strings and sounds

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