Papers Text Normalization

Text normalization using memory augmented neural networks

We perform text normalization, i.e. the transformation of words from the written to the spoken form, using a memory augmented neural network. With the addition of dynamic memory access and storage mechanism, we present a…

Text Normalization Infrastructure that Scales to Hundreds of Language Varieties

Evaluating historical text normalization systems: How well do they generalize?

We highlight several issues in the evaluation of historical text normalization systems that make it hard to tell how well these systems would actually work in practice---i.e., for new datasets or languages; in comparison…

Improving Text Normalization by Optimizing Nearest Neighbor Matching

Text normalization is an essential task in the processing and analysis of social media that is dominated with informal writing. It aims to map informal words to their intended standard forms. Previously proposed text nor…

DeepNorm-A Deep Learning Approach to Text Normalization

This paper presents an simple yet sophisticated approach to the challenge by Sproat and Jaitly (2016)- given a large corpus of written text aligned to its normalized spoken form, train an RNN to learn the correct normali…

Automatically Extracting Variant-Normalization Pairs for Japanese Text Normalization

Social media texts, such as tweets from Twitter, contain many types of non-standard tokens, and the number of normalization approaches for handling such noisy text has been increasing. We present a method for automatical…

Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels

In this study, we investigated the effectiveness of augmented data for encoder-decoder-based neural normalization models. Attention based encoder-decoder models are greatly effective in generating many natural languages.…

Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German

The goal of this work is to design a machine translation (MT) system for a low-resource family of dialects, collectively known as Swiss German, which are widely spoken in Switzerland but seldom written. We collected a si…

Noisy Uyghur Text Normalization

Uyghur is the second largest and most actively used social media language in China. However, a non-negligible part of Uyghur text appearing in social media is unsystematically written with the Latin alphabet, and it cont…

Learning attention for historical text normalization by learning to pronounce

Automated processing of historical texts often relies on pre-normalization to modern word forms. Training encoder-decoder architectures to solve such problems typically requires a lot of training data, which is not avail…

Mainstreaming August Strindberg with Text Normalization

Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization

Text normalization techniques based on rules, lexicons or supervised training requiring large corpora are not scalable nor domain interchangeable, and this makes them unsuitable for normalizing user-generated content (UG…

Japanese Text Normalization with Encoder-Decoder Model

Text normalization is the task of transforming lexical variants to their canonical forms. We model the problem of text normalization as a character-level sequence to sequence learning problem and present a neural encoder…

DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets

In this paper, we describe the DeepNNNER entry to The 2nd Workshop on Noisy User-generated Text (WNUT) Shared Task {\#}2: Named Entity Recognition in Twitter. Our shared task submission adopts the bidirectional LSTM-CNN …

Keynote Lecture 2: Neural (and other Machine Learning) Approaches to Text Normalization

RACAI Entry for the IWSLT 2016 Shared Task

Spoken Language Translation is currently a hot topic in the research community. This task is very complex, involving automatic speech recognition, text-normalization and machine translation. We present our speech transla…

RNN Approaches to Text Normalization: A Challenge

This paper presents a challenge to the community: given a large corpus of written text aligned to its normalized spoken form, train an RNN to learn the correct normalization function. We present a data set of general tex…

Minimally Supervised Written-to-Spoken Text Normalization

In speech-applications such as text-to-speech (TTS) or automatic speech recognition (ASR), \emph{text normalization} refers to the task of converting from a \emph{written} representation into a representation of how the …

Evaluating Informal-Domain Word Representations With UrbanDictionary

Existing corpora for intrinsic evaluation are not targeted towards tasks in informal domains such as Twitter or news comment forums. We want to test whether a representation of informal words fulfills the promise of elid…

Context Tailoring for Text Normalization