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Ehud's thoughts and observations about Natural Language Generation

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Accuracy Errors Go Beyond Getting Facts Wrong

Apr 27, 2020 ehudreiter9 Comments

Accuracy errors in NLG texts go far beyond simple factual mistakes, for example they also include misleading use of words and incorrect context/discourse inferences. All of these types of errors are unacceptable in most data-to-text NLG use cases.

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What are Benefits of Physical Conferences?

Apr 21, 2020 ehudreiter1 Comment

The Covid crisis gives us a chance to rethink and change our conference culture. I would like to see fewer large international conferences, and also have these focus on discussion and interaction rather than on oral presentations.

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How can I tell if a paper is scientifically solid?

Apr 6, 2020 ehudreiterLeave a comment

A PhD student recently complained that to me that he was wasting a lot of time reading scientifically dubious papers. I give some suggestions on indicators of poor scientific quality in research papers.

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Lets Use ML for Insights!

Mar 20, 2020 ehudreiterLeave a comment

I’d love to see more people using machine learning to provide insights about NLG problems and related linguistic issues. I personally think this is much more useful than tweaking models to show a 1% increase in state-of-art in a very artificial context.

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Why do we still use 18-year old BLEU?

Mar 2, 2020Mar 2, 2020 ehudreiter2 Comments

NLP technology has changed and advanced over the past two decades, but it often seems that NLG evaluation has not. Why is the 18-year old BLEU metric still so dominant?

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Shared Task on Evaluating Accuracy?

Feb 18, 2020Feb 1, 2021 ehudreiter1 Comment

We’re thinking of organising a shared task on evaluating the accuracy of texts produced by NLG systems. Comments welcome, also let me know if you might participate.

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Do people “cheat” by overfitting test data

Feb 6, 2020Feb 11, 2020 ehudreiter1 Comment

NLP in 2020 is dominated by papers which report small improvements in state-of-art. I suspect that a lot of these improvements are due to overfitting test data, not to genuine scientific advances.

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AI professionals also focus on change management

Jan 27, 2020Jan 27, 2020 ehudreiter2 Comments

If we want to deploy AI in the real world, we need to think about “change management” issues. Eg if users think that AI threatens their jobs or adds extra hassle, then uptake will be slow. This has been a problem for AI and statistical algorithms since the 1950s.

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Amateurs focus on models; professionals focus on data

Jan 14, 2020 ehudreiter1 Comment

There is a military saying that “amateurs discuss tactics, professionals discuss logistics”. Similarly I think AI professionals should focus on data more than models. I suggest four simple initial questions to ask about your data if you want to build an ML system.

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Lessons from 25 Years of Information Extraction

Jan 2, 2020Jan 2, 2020 ehudreiter1 Comment

I really liked Grishman’s recent paper on 25 years of research in information extraction, and summarise a few of the key insights here, about relative progress in different areas of NLP, reluctance of researchers to use complex evaluation techniques, and corpus creation vs rule-writing.

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