End-to-end automatic speech recognition (ASR) systems have made significant progress in general scenarios. However, it remains challenging to transcribe contextual named entities (NEs) in the contextual ASR scenario. Previous approaches have attempted to address this by utilizing the NE dictionary. These approaches treat entities as individual tokens and generate them token-by-token, which may result in incomplete transcriptions of entities. In this paper, we treat entities as indivisible wholes and introduce the idea of copying into ASR. We design a systematic mechanism called CopyNE, which can copy entities from the NE dictionary. By copying all tokens of an entity at once, we can reduce errors during entity transcription, ensuring the completeness of the entity. Experiments demonstrate that CopyNE consistently improves the accuracy of transcribing entities compared to previous approaches. Even when based on the strong Whisper, CopyNE still achieves notable improvements.

CopyNE: Better Contextual ASR by Copying Named Entities

Spoken Named Entity Recognition (NER) aims to extract entities from speech. 
The extracted entities can help voice assistants better understand user's questions and instructions.
However, current Chinese Spoken NER datasets are laboratory-controlled data that are collected by reading existing texts in quiet environments, rather than natural spoken data, and the texts used for reading are also limited in topics. 
These limitations obstruct the development of Spoken NER in more natural and common real-world scenarios.
To address this gap, we introduce a 
real-world Chinese Spoken NER dataset (RWCS-NER), encompassing open-domain daily conversations and task-oriented intelligent cockpit instructions. 
We compare several mainstream pipeline approaches on RWCS-NER. The results indicate that the current methods, affected by Automatic Speech Recognition (ASR) errors, do not perform satisfactorily in real settings.
Aiming to enhance Spoken NER in real-world scenarios, we propose two approaches: self-training-asr and mapping then distilling (MDistilling).
Experiments show that both approaches can achieve significant improvements, particularly MDistilling.
Even compared with GPT4.0, MDistilling still reaches better results.
We believe that our work will advance the field of Spoken NER in real-world settings.

Chinese Spoken Named Entity Recognition in Real-world Scenarios: Dataset and Approaches

It is a presentation of our work "Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing" at COLING 2022. We paper proposes to cast end-to-end span-based SRL as a word-based graph parsing task. The major challenge is how to represent spans at the word level. Borrowing ideas from research on Chinese word segmentation and named entity recognition, 
we propose and compare four different schemata of graph representation, i.e., BES, BE, BIES, and BII, among which we find that the \emph{BES} schema performs the best. We further gain interesting insights through detailed analysis. Moreover, we propose a simple constrained Viterbi procedure to ensure the legality of the output graph according to the constraints of the SRL structure. We conduct experiments on two widely used benchmark datasets, i.e., CoNLL05 and CoNLL12. Results show that our word-based graph parsing approach achieves consistently better performance than previous results, under all settings of end-to-end and predicate-given, without and with pre-trained language models (PLMs). More importantly, our model can parse 669/252 sentences per second, without and with PLMs respectively.

Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Shilin Zhou

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CopyNE: Better Contextual ASR by Copying Named Entities

Chinese Spoken Named Entity Recognition in Real-world Scenarios: Dataset and Approaches

Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing

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