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Found 8 papers, 6 papers with code
Investigating the Impact of ASR Errors on Spoken Implicit Discourse Relation Recognition
We present an empirical study investigating the influence of automatic speech recognition (ASR) errors on the spoken implicit discourse relation recognition (IDRR) task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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PhoGPT: Generative Pre-training for Vietnamese
The base model, PhoGPT-4B, with exactly 3. 7B parameters, is pre-trained from scratch on a Vietnamese corpus of 102B tokens, with an 8192 context length, employing a vocabulary of 20480 token types.
XPhoneBERT: A Pre-trained Multilingual Model for Phoneme Representations for Text-to-Speech
We present XPhoneBERT, the first multilingual model pre-trained to learn phoneme representations for the downstream text-to-speech (TTS) task.
A High-Quality and Large-Scale Dataset for English-Vietnamese Speech Translation
In this paper, we introduce a high-quality and large-scale benchmark dataset for English-Vietnamese speech translation with 508 audio hours, consisting of 331K triplets of (sentence-lengthed audio, English source transcript sentence, Vietnamese target subtitle sentence).
PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation
We introduce a high-quality and large-scale Vietnamese-English parallel dataset of 3. 02M sentence pairs, which is 2. 9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15.
PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing
We present the first multi-task learning model -- named PhoNLP -- for joint Vietnamese part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing.
WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets.
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings
It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR).
