Engineering Perspective: Understand Issues, Find Simple Solution
An approach I often take to NLG and indeed AI is to try to understand underying linguistic, NLG, and AI issues, and then to look for simple solutions to these issues.
Language Grounding and Context
Some thoughts on language grounding, especially choosing words to express data, and how this depends on context.
Use Proper Baselines!
Unfortunately I suspect many researchers make their results looks better by using poor baselines. I give some thoughts on this, based on a recent discussion with a PhD student.
Why I do not Want to be a Co-author on Your Paper
Some thoughts about when I feel comfortable being a coauthor on a paper, expressed as a letter to someone who put me on a paper as a co-author without asking me frst,
How Would I Automatically Evaluate NLG Systems?
Some musings on principled and theoretically sound techniques for automatically evaluating NLG systems.
How to Validate Metrics
My advice on how to perform a high-quality validation study, which assesses whether a metric (such as BLEU) correlates well with human evaluations.
Why doesnt BLEU work for NLG?
BLEU works much better for MT systems and NLG systems. In this blog I present some speculations as to why this is the case.
BLEU in Different Languages: Dont use it for German
My structured survey of BLEU suggests that BLEU-human correlations are worse in German than in many other languages. But there are many caveats, so we need to be cautious in interpreting this result.
BLEU-Human Correlation is Increasing: What does this Mean?
The correlation between BLEU and human evaluations of MT systems seems to be increasing over time. Since BLEU has not changed, how is this possible, and what does it mean?
Many Papers on Machine Learning in NLP are Scientifically Dubious
In response to a previous blog, many people expressed concerns to me about the quality of many papers they saw on ML in NLP. I summarise some of these concerns, which are worrying.
