Journal of Interesting Negative Results
in Natural Language Processing
Machine Learning

JINR (ISSN 1916-7423) is an electronic journal, with a printed version to be negotiated with a major publisher once we have established a steady presence. The journal will bring to the fore research in Natural Language Processing and Machine Learning that uncovers interesting negative results.

It is becoming more and more obvious that the research community in general, and those who work NLP and ML in particular, are biased towards publishing successful ideas and experiments. Insofar as both our research areas focus on theories "proven" via empirical methods, we are sure to encounter ideas that fail at the experimental stage for unexpected, and often interesting, reasons. Much can be learned by analysing why some ideas, while intuitive and plausible, do not work. The importance of counter-examples for disproving conjectures is already well known. Negative results may point to interesting and important open problems. Knowing directions that lead to dead-ends in research can help others avoid replicating paths that take them nowhere. This might accelerate progress or even break through walls!

We propose this journal as a resource that gives a voice to negative results which stem from intuitive and justifiable ideas, proven wrong through thorough and well-conducted experiments. We also encourage the submission of short papers/communications presenting counter-examples to usually accepted conjectures or to published papers.

The journal's scope encompasses all areas of Natural Language Processing and Machine Learning. Papers published in JINR will meet the highest quality standards, as measured by the originality and significance of the contribution. They will describe research with theoretical and practical significance. All theories and ideas will have to be clearly stated and justified by a deep literature review.

Because of the nature of the journal, there should be good justification for trying out the ideas presented. The experiments reported should be shown in a manner that allows their reproduction. The negative results should be explained and justified, along with the reasons why the idea did not lead to the predicted results. The lessons learned should be clearly stated.

Submissions must be original. They cannot be under review for, or pending publication in, another journal. We will publish work that has previously been reported in conferences or workshops, on the condition that the work has been substantially extended to fit journal submission standards.

The papers are published under the Creative Commons CC BY 4.0 licence.