Conditional Random Field Autoencoders for Unsupervised Structured Prediction
Authors: Waleed Ammar, Chris Dyer, Noah A. Smith
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then show competitive results with instantiations of the framework for two canonical tasks in natural language processing: part-of-speech induction and bitext word alignment, and show that training the proposed model can be substantially more efficient than a comparable feature-rich baseline. We evaluate the effectiveness of CRF autoencoders for learning from unlabeled examples in POS induction and word alignment. Fig. 3 compares predictions of the CRF autoencoder model in seven languages to those of a featurized first-order HMM model [3] and a standard (feature-less) first-order HMM, using V-measure [37]. AER for variants of each model (forward, reverse, and symmetrized) are shown in Table 1 (left). |
| Researcher Affiliation | Academia | Waleed Ammar Chris Dyer Noah A. Smith School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA {wammar,cdyer,nasmith}@cs.cmu.edu |
| Pseudocode | No | The paper describes the model and learning process using textual descriptions and mathematical equations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'cdec [13]' as a machine translation system and provides its URL, but this is a third-party tool used by the authors, not the source code for their own methodology. There is no explicit statement or link providing access to the authors' implementation code. |
| Open Datasets | Yes | We evaluate the effectiveness of CRF autoencoders for learning from unlabeled examples in POS induction and word alignment. We found reconstructing Brown clusters [5] of tokens instead of their surface forms to improve POS induction. We consider an intrinsic evaluation on a Czech-English dataset of manual alignments. For POS induction, the paper mentions evaluation in seven languages and compares to a featurized first-order HMM model [3], implying the use of established benchmark datasets typically used in these tasks and referenced by the provided citations. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or explicit mention of a validation set) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Ada Grad [12]' and 'L-BFGS' as optimizers and 'cdec [13]' as a decoder, but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In the experiments below, we apply a squared L2 regularizer for the CRF parameters λ, and a symmetric Dirichlet prior for categorical parameters θ. We experimented with Ada Grad [12] and L-BFGS. In POS induction, |Y| is a constant, the number of syntactic classes which we configure to 12 in our experiments. We defer the detailed experimental setup to Appendix A. |