Comparison of Paragram and GloVe Results for Similarity Benchmarks

Jakub Dutkiewicz, Czesław Jędrzejek

Feb 15, 2018 (modified: Oct 27, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: Distributional Semantics Models(DSM) derive word space from linguistic items in context. Meaning is obtained by defining a distance measure between vectors corresponding to lexical entities. Such vectors present several problems. This work concentrates on quality of word embeddings, improvement of word embedding vectors, applicability of a novel similarity metric used ‘on top’ of the word embeddings. In this paper we provide comparison between two methods for post process improvements to the baseline DSM vectors. The counter-fitting method which enforces antonymy and synonymy constraints into the Paragram vector space representations recently showed improvement in the vectors’ capability for judging semantic similarity. The second method is our novel RESM method applied to GloVe baseline vectors. By applying the hubness reduction method, implementing relational knowledge into the model by retrofitting synonyms and providing a new ranking similarity definition RESM that gives maximum weight to the top vector component values we equal the results for the ESL and TOEFL sets in comparison with our calculations using the Paragram and Paragram + Counter-fitting methods. For SIMLEX-999 gold standard since we cannot use the RESM the results using GloVe and PPDB are significantly worse compared to Paragram. Apparently, counter-fitting corrects hubness. The Paragram or our cosine retrofitting method are state-of-the-art results for the SIMLEX-999 gold standard. They are 0.2 better for SIMLEX-999 than word2vec with sense de-conflation (that was announced to be state-of the-art method for less reliable gold standards). Apparently relational knowledge and counter-fitting is more important for judging semantic similarity than sense determination for words. It is to be mentioned, though that Paragram hyperparameters are fitted to SIMLEX-999 results. The lesson is that many corrections to word embeddings are necessary and methods with more parameters and hyperparameters perform better.
  • TL;DR: Paper provides a description of a procedure to enhance word vector space model with an evaluation of Paragram and GloVe models for Similarity Benchmarks.
  • Keywords: language models, vector spaces, word embedding, similarity
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