This is the blog of the Statistical NLP Group at the Department of Computational Linguistics, Heidelberg University. Our research addresses various aspects of the problem of the confusion of languages, by means of statistical learning for natural language processing.
We blog about pitfalls in methodologies, recent advances and important problems in this field of research.
Discussing good, bad and ugly practices of reinforcement learning in neural machine translation.
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Tags: BLEU, expected reward, exposure bias, future challenges, monolingual data, neural machine translation, policy gradient, reinforcement learning, sampling
Categories: opinion piece, paper discussion
This blog post gives an overview of the paper “Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs”.
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Tags: low resource scenario, multi-task modeling, neural machine translation
Categories: new publication
Introducing Joey NMT, a minimalist neural machine translation framework for novices built on Pytorch.
This post presents a summary of my PhD thesis. I explored how to learn from feedback given to model outputs when the collection of direct supervision signals...
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Tags: machine translation, reinforcement learning, semantic parsing
Categories: overview, PhD thesis
How can we give RL agents that learn from human feedback a possible advantage to succeed in this difficult learning scenario?
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Tags: human factor, neural machine translation, reinforcement learning
Categories: opinion piece, paper discussion
How can we train semantic parsers if neither question-parse nor question-answer pairs can be collected?
This post explains the need for the score function gradient estimator trick and how it works.
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Tags: expected reward, exposure bias, MRT, neural machine translation, policy gradient, REINFORCE, sampling, score function gradient estimator, semantic parsing
Categories: explanation