Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Analogies Explained: Towards Understanding Word Embeddings
Authors: Carl Allen, Timothy Hospedales
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive a probabilistically grounded de๏ฌnition of paraphrasing that we re-interpret as word transformation, a mathematical description of wx is to wy . From these concepts we prove existence of linear relationships between W2V-type embeddings that underlie the analogical phenomenon, identifying explicit error terms. |
| Researcher Affiliation | Academia | 1School of Informatics, University of Edinburgh. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code or state that code is released. |
| Open Datasets | Yes | Values are computed from the text8 corpus (Mahoney, 2011). Mahoney, M. text8 wikipedia dump. http:// mattmahoney.net/dc/textdata.html, 2011. [Online; accessed May 2019]. |
| Dataset Splits | No | The paper discusses data in terms of a text corpus for word embeddings (W2V, Glove) but does not specify any train/validation/test splits for experiments conducted by the authors. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for computations or derivations. |
| Software Dependencies | No | The paper mentions W2V and Glove as models but does not list any specific software or library versions used for its derivations or illustrative examples. |
| Experiment Setup | No | The paper focuses on theoretical derivations and proofs. It does not describe an experimental setup with specific hyperparameters or training configurations for a novel method. |