Papers with Text normalization

4 papers
Neural Text Normalization with Subword Units (N19-2)

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Challenge: Text normalization (TN) is an important step in conversational systems.
Approach: They frame text normalization as a machine translation task and tackle it with sequence-to-sequence models.
Outcome: The proposed model normalizes written text to its spoken form to facilitate speech recognition and text-to-speech synthesis.
Benefits of Data Augmentation for NMT-based Text Normalization of User-Generated Content (D19-55)

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Challenge: Social media texts are considered important language resources for several NLP tasks, but their use of non-standard words makes it difficult to process and analyze UGC.
Approach: They propose to use a Neural Machine Translation approach to normalize lexical variants to their canonical forms to overcome performance drop in UGC.
Outcome: The proposed approach overcomes a data bottleneck in Dutch, a low-resource language.
Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models (D18-1)

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Challenge: Recent advances in text normalization have limited applications in other languages . a novel approach to text normalizing uses character embeddings and word embedds .
Approach: They propose a sequence-to-sequence model with character-based attention that uses pre-trained word embeddings to model subword information.
Outcome: The proposed model achieves state-of-the-art F1 score on Arabic spelling correction task despite being small and unsuited for the task.
Developing Resources for Automated Speech Processing of Quebec French (2020.lrec-1)

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Challenge: acoustic models for automatic segmentation of Quebec French are not available for all languages . linguistic resources are developed to perform phonetic annotations in Quebec French . physical characteristics of speech can be observed in the production of sounds .
Approach: They propose to use a French lexicon to train automatic QF segmentation models . they adapt existing pronunciation dictionary and acoustic model from existing ones .
Outcome: The proposed tools perform the full process of speech segmentation in Quebec French.

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