- fairly straightforward extension of existing retrofitting work - would be nice to see some additional baselines (e.g. character embeddings) - General Discussion: The paper describes "morph-fitting", a type of retrofitting for vector spaces that focuses specifically on incorporating morphological constraints into the vector space. The framework is based on the idea of "attract" and "repel" constraints, where attract constraints are used to pull morphological variations close together (e.g. look/looking) and repel constraints are used to push derivational antonyms apart (e.g. responsible/irresponsible). They test their algorithm on multiple different vector spaces and several language, and show consistent improvements on intrinsic evaluation (SimLex-999, and SimVerb-3500). They also test on the extrinsic task of dialogue state tracking, and again demonstrate measurable improvements over using morphologically-unaware word embeddings.
I think this is a very nice paper. It is a simple and clean way to incorporate linguistic knowledge into distributional models of semantics, and the empirical results are very convincing. I have some questions/comments below, but nothing that I feel should prevent it from being published.
- Comments for Authors 1) I don't really understand the need for the morph-simlex evaluation set. It seems a bit suspect to create a dataset using the same algorithm that you ultimately aim to evaluate. It seems to me a no-brainer that your model will do well on a dataset that was constructed by making the same assumptions the model makes. I don't think you need to include this dataset at all, since it is a potentially erroneous evaluation that can cause confusion, and your results are convincing enough on the standard datasets.
2) I really liked the morph-fix baseline, thank you for including that. I would have liked to see a baseline based on character embeddings, since this seems to be the most fashionable way, currently, to side-step dealing with morphological variation. You mentioned it in the related work, but it would be better to actually compare against it empirically.
3) Ideally, we would have a vector space where morphological variants are just close together, but where we can assign specific semantics to the different inflections. Do you have any evidence that the geometry of the space you end with is meaningful. E.g. does "looking" - "look" + "walk" = "walking"? It would be nice to have some analysis that suggests the morphfitting results in a more meaningful space, not just better embeddings.
- fairly straightforward extension of existing retrofitting work - would be nice to see some additional baselines (e.g. character embeddings) -