Neural Sentence Simplification with Semantic Dependency Information
Authors: Zhe Lin, Xiaojun Wan13371-13379
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate SDISS on three benchmark datasets and it outperforms a number of strong baseline models on the SARI and FKGL metrics. Human evaluation also shows SDISS can produce simplified sentences with better quality. |
| Researcher Affiliation | Academia | Zhe Lin, Xiaojun Wan Wangxuan Institute of Computer Technology, Peking University Center for Data Science, Peking University The MOE Key Laboratory of Computational Linguistics, Peking University {linzhe, wanxiaojun}@pku.edu.cn |
| Pseudocode | No | The paper provides model architecture descriptions and mathematical equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/ L-Zhe/SDISS. |
| Open Datasets | Yes | We evaluate our SDISS model on three benchmark datasets: Newsela, Wiki Small and Wiki Large. Newsela (Xu, Callison-Burch, and Napoles 2015)... Wiki Large (Zhang and Lapata 2017)... Wiki Small was built by Zhu, Bernhard, and Gurevych (2010)... |
| Dataset Splits | Yes | Zhang and Lapata (2017) provide standard splits and the train/dev/test sets contain 94,208/1,129/1,076 sentence pairs, respectively. (for Newsela) [...] Wiki Large... with 296,402/2,000/359 complex-simple sentence pairs for train/dev/test sets. [...] Wiki Small... with 88,837/205/100 pairs provided by Zhang and Lapata (2017) as train/dev/test sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU models, or cloud computing instances. |
| Software Dependencies | No | The paper mentions using the 'Stanford Core NLP tool (Manning et al. 2014)' but does not specify a version number for it or for any other software dependencies. |
| Experiment Setup | Yes | We set the dimensions of word embedding and hidden units dmodel to 256. For multi-head attention, we set the number of heads to 4. The number of layers for graph encoder, sentence encoder and decoder are all set to 6. We set λ to 5 for Newsela, and 10 for Wiki Small and Wiki Large. We set tmax to 15 for Newsela and 20 for Wiki Small, and tmin is 0 for both datasets. For Wiki Large, because of its complexity, we set tmin to 5 and tmax to 50. |