Text to Speech
Text to speech, or speech synthesis, is the inverse problem of automatic speech recognition. It is a sequence-to-sequence learning task where the input is a sequence of text and the output is a generated audio file, meaning the resulting output sequence is significantly longer than the input sequence.
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Natural language processing in ACM Computing Classification
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Text normalization
Part-of-speech Tagging
Sentiment Analysis
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Historical Perspective: Natural Language Processing
Machine Reading and Comprehension
Minimum Edit Distance
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Period Disambiguation
Features Design for NLP Classification Problems
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Dialogue Systems
Properties of Human Conversation
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Types of NLP Model Paradigms
Types of Training Objectives of Pre-trained LM
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Articulatory Phonetics
Phonetics
Word embedding
A Survey of Data Augmentation Approaches for NLP
Data Augmentation in NLP
Spelling correction and the noisy channel
Constituency
Text Classification
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A Survey of Natural Language Based Financial Forecasting
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization
From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information
Machine Translation (MT)
Temporal Reasoning
Knowledge Graph
Dynamic Neural Network in Natural Language Processing
Label Preservation
Deep Learning Algorithms in Data Augmentation
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Explainable AI for Natural Language Processing
Corpora
Racism in NLP
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios
Low-Resource Scenario in Natural Language Processing
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios
Low-Resource NLP
Continual Learning
Continual Lifelong Learning in Natural Language Processing: A Survey
Object Naming in Language and Vision
A Survey on Hate Speech Detection using Natural Language Processing
Hate Speech Detection using Natural Language Processing
A Survey of Text Games for Reinforcement Learning informed by Natural Language
Natural Language Text Games for Reinforcement Learning
Data-Driven Sentence Simplification: Survey and Benchmark
Deep Learning for Text Style Transfer: A Survey
Text Style Transfer (TST)
Representing Numbers in NLP: a Survey and a Vision
Number representation in NLP
Semantic Textual Similarity (STS)
Paraphrase Identification (PI)
Machine Comprehension (MC)
Sentence Representation Model Categorizations
Automatic Detection of Machine Generated Text: A Critical Survey
Automatic Detection of Machine Generated Text
Fine-grained Financial Opinion Mining: A Survey and Research Agenda
Natural Language Processing in Finance
Phonology / Phonetics
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering
Sentence Pair Modelling
A Survey of Active Learning for Text Classification using Deep Neural Networks
A Survey of Knowledge-Enhanced Text Generation
Knowledge-enhanced Text Generation
The Pollyanna Hypothesis
On Positivity Bias in Negative Reviews
Widely Used English Review Datasets
A Survey on Dialogue Summarization: Recent Advances and New Frontiers
Survey on Dialogue Summarization: Recent Advances and New Frontiers
Potential Biases of Natural Language Processing
The Pre-training and Fine-tuning Paradigm
Tokens and Words in NLP
Distinction and Interchangeability of 'Tokens' and 'Words' in NLP
Code-Switching in NLP and Linguistics
Automatic Speech Recognition
Text to Speech
Training Dataset
Automatic Speech Recognition
Text to Speech
Machine Translation