A Representation Learning Framework for Multi-Source Transfer Parsing
Authors: Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, Ting Liu
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By evaluating on the Google universal dependency treebanks (v2.0), our best models yield an absolute improvement of 6.53% in averaged labeled attachment score, as compared with delexicalized multi-source transfer models. We also significantly outperform the state-of-the-art transfer system proposed most recently. |
| Researcher Affiliation | Collaboration | 1Center for Social Computing and Information Retrieval Harbin Institute of Technology, Harbin, China 2Center for Language and Speech Processing Johns Hopkins University, Baltimore, USA 3Baidu Inc., Beijing, China |
| Pseudocode | No | The paper describes algorithms using text and mathematical equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using existing tools (cdec, word2vec) and adopting an implementation from a previous paper (Guo et al. 2015), but it does not provide a link or explicit statement about the source code for the novel framework described in this paper. |
| Open Datasets | Yes | We use the Google universal treebanks (v2.0) (Mc Donald et al. 2013) for evaluation. The languages we consider include all Indo-European languages presented in the universal treebanks. For both MULTI-SG and MULTI-PROJ, we use the Europarl corpus for EN-{DE, ES, FR, PT, IT, SV} parallel data,4 and the WMT-2011 English news corpora as additional monolingual data.5 |
| Dataset Splits | No | The paper mentions training models and evaluating on test data, but it does not explicitly describe the use of a separate validation set for hyperparameter tuning or early stopping, nor does it provide specific split percentages for such a set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'cdec', 'word2vec', and 'multi-threaded Brown clustering tool' but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | Yes | We use the cross-entropy loss as objection function, and use mini-batch Ada Grad to train the parser. Considering that in practice when we apply our model to a low-resource language, typically we don t have any development data for parameter tuning. So we simply train our parsing models for 20,000 iterations without early-stopping. |