A Latent Variable Model for Learning Distributional Relation Vectors
Authors: Jose Camacho-Collados, Luis Espinosa-Anke, Shoaib Jameel, Steven Schockaert
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that our relation vectors outperform those from existing methods. |
| Researcher Affiliation | Academia | 1School of Computer Science and Informatics, Cardiff University, United Kingdom 2School of Computing, University of Kent, United Kingdom |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | These pre-trained relation embeddings, along with the code to generate them, are available at https://github.com/pedrada88/relative. |
| Open Datasets | Yes | To learn the relation vectors we use the English Wikipedia dump of January 2018, as in Joshi et al. [2019]. We evaluate our model on graded lexical entailment using the Hyper Lex dataset [Vuli c et al., 2017]. As test sets we used Diff Vec [Vylomova et al., 2016] and BLESS [Baroni and Lenci, 2011]. We specifically used the following standard datasets11: (1) 20news [Lang, 1995]; (2) reuters [Lewis et al., 2004]; (3) bbc12 [Greene and Cunningham, 2006]; and (4) ohsumed. |
| Dataset Splits | Yes | For both protocols, training and test partitions are available. (Hyper Lex) BLESS includes noun-noun relations such as hypernymy, meronymy, and co-hyponymy, including 13,258 and 6,629 instances for training and testing, respectively. (BLESS) using 10-fold cross-validation in the case of Diff Vec. For bbc, which does not include train-test splits, we performed 10-fold cross-validation. |
| Hardware Specification | Yes | learning relation vectors for all pairs in the vocabulary took around a day on a standard desktop computer on CPU. The Titan Xp used for this research was donated by the NVIDIA Corporation. |
| Software Dependencies | No | The paper mentions '300-dimensional Fast Text word embeddings [Bojanowski et al., 2017]' and 'Model developed in Keras: https://github.com/fchollet/keras.' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The number of iterations of the EM algorithm (see Section 3.2) is set to three, which we empirically found sufficient for the model to converge. |