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Ehud Reiter's Blog

Ehud's thoughts about Natural Language Generation. Also see my book on NLG.

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Author: ehudreiter

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How do Users React to NLG?

Jul 21, 2017Jul 21, 2017 ehudreiterLeave a comment

Some obervations on how people react to NLG systems (which is a very different issue than scientific evaluation).

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A Consumer Perspective on Evaluation

Jul 12, 2017Jul 12, 2017 ehudreiterLeave a comment

What happens if we think of evaluations as a way of helping users choose the best NLP tech for their needs?

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My PhD Students: Where Are They Now (June 2017)

Jun 28, 2017Jul 16, 2018 ehudreiter1 Comment

A summary of students who have gotten PhDs under my supervision.

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Exciting NLG Research Topics (June 2017)

Jun 20, 2017Jun 20, 2017 ehudreiterLeave a comment

A visitor recently asked me what NLG research topics I found most interesting and exciting. Great question, I’ve written here an expanded version of what I told him.

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Response to Goldberg’s Blog on Deep Learning for NLG

Jun 19, 2017Jan 3, 2018 ehudreiter13 Comments

My response to Goldberg’s “adversarial review” of some research on using deep learning in NLG

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Study Design for Systematic Review of BLEU Validity: Comments Welcome!

Jun 13, 2017Jun 13, 2018 ehudreiter5 Comments

I’m planning to do a systematic review of the validity of BLEU, and am very keen to get comments and suggestions on study design from others!

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Cycling through Northern England

Jun 6, 2017Jun 7, 2017 ehudreiter1 Comment

A short travelogue about a holiday cycling trip I did in May 2017 (nothing to do with NLG!)

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PhD Studentship: Advanced Data Storytelling

May 19, 2017May 19, 2017 ehudreiter1 Comment

I’m looking for a PhD student to work on Advanced Data Storytelling!

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Regression to Mean

May 18, 2017May 18, 2017 ehudreiter1 Comment

Some explanation and advice about regression to mean, which is a statistical phenomena that can impact NLG evaluations.

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You Need to Understand your Corpora! The Weathergov Example

May 9, 2017May 9, 2017 ehudreiter8 Comments

People who use corpora to build NLG systems need to understand what is in the corpora. The widely used Weathergov corpus, for example, probably contains computer-generated texts rather than human-written texts. So learning from it is essentially reverse-engineering a rule-based NLG system.

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