Data Skeptic is a data science podcast exploring machine learning, statistics, artificial intelligence, and other data topics through short tutorials and interviews with domain experts.
Simultaneous Translation at Baidu
While at NeurIPS 2018, Kyle chatted with Liang Huang about his work with Baidu research on simultaneous translation, which was demoed at the conference.
Human vs Machine Transcription
Machine transcription (the process of translating audio recordings of language to text) has come a long way in recent years. But how do the errors made during machine transcription compare to the errors made by a human transcriber? Find out in this episode!
A sequence to sequence (or seq2seq) model is neural architecture used for translation (and other tasks) which consists of an encoder and a decoder. The encoder/decoder architecture has obvious promise for machine translation, and has been successfully applied this way. Encoding an input to a small number of hidden nodes which can effectively be decoded to a matching string requires machine learning to learn an efficient representation of the essence of the strings. In addition to translation, seq2seq models have been used in a number of other NLP tasks such as summarization and image captioning. Related Links tf-seq2seq Describing Multimedia Content using Attention-based Encoder--Decoder Networks Show and Tell: A Neural Image Caption Generator Attend to You: Personalized Image Captioning with Context Sequence Memory Networks
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