ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
Authors: Zuohui Fu, Yikun Xian, Shijie Geng, Yingqiang Ge, Yuting Wang, Xin Dong, Guang Wang, Gerard de Melo7756-7763
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments show that our method outperforms several technically more powerful approaches, especially under challenging low-resource circumstances. The source code is available from https://github.com/zuohuif/ABSent along with relevant datasets. In this section, we extensively evaluate the effectiveness of our ABSent method compared with state-of-the-art approaches on two heterogeneous real-world corpora. |
| Researcher Affiliation | Academia | Department of Computer Science Rutgers University, New Brunswick, NJ, USA {zuohui.fu, sg1309, yingqiang.ge, xd48}@rutgers.edu, siriusxyk@gmail.com, {yw632, gw255}@cs.rutgers.edu, gdm@demelo.org |
| Pseudocode | No | The paper provides mathematical formulations and a diagram of the framework but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available from https://github.com/zuohuif/ABSent along with relevant datasets. |
| Open Datasets | Yes | We evaluate the precision of our approach on the Europarl parallel corpus and on extracted from the Tatoeba service1, which provides translations of commonly used phrases that might be useful to language learners. 1http://tatoeba.org |
| Dataset Splits | No | The paper specifies training and test set sizes, e.g., "160k pairs as the training set and 1,600 pairs as the test set", but does not explicitly mention or detail a validation set split. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions optimizers (Adam) and word vector tools (fastText) but does not provide specific version numbers for any software dependencies like programming languages, frameworks, or libraries. |
| Experiment Setup | Yes | Both generators GX and GY consist of three fully connected layers with hidden sizes of 512, 1024, 512, respectively. Each hidden layer is connected with a Batch Norm layer and the Re LU activation function. The final activation function is tanh. Both discriminators Dreal and Ddom take as input two embeddings, followed by three fully connected layers of sizes 512, 1024, 512 with concatenation. Each hidden layer is connected with a leaky Re LU activation function (0.2), while the output is activated by a sigmoid function. We rely on Adam optimization with an initial learning rate of 0.002 and a batch size of 128. |