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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deterministic Attention for Sequence-to-Sequence Constituent Parsing
Authors: Chunpeng Ma, Lemao Liu, Akihiro Tamura, Tiejun Zhao, Eiichiro Sumita
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When tested on the standard WSJ treebank, the deterministic attention model produced significant improvements over probabilistic attention models, delivering a 90.6 F-score on the test set, using ensembling but without requiring pre-training, tri-training, or POS-tagging. |
| Researcher Affiliation | Academia | 1Machine Intelligence and Translation Laboratory, Harbin Institute of Technology, Harbin, China 2ASTREC, National Institute of Information and Communications Technology (NICT), Kyoto, Japan |
| Pseudocode | Yes | Table 3: Formal representation of the bottom-up linearization method. σ can be empty. For sh action, i Xj may also be empty, in which case the stack should be 0XX1 after the sh action is implemented. |
| Open Source Code | No | We implemented the deterministic attention mechanism based on an open-source sequence-to-sequence toolkit nematus1. |
| Open Datasets | Yes | All experiments were conducted using the WSJ part of the Penn Treebank. Following previous studies such as that of Watanabe and Sumita (2015), we used Sections 2-21, 22 and 23 as the training set, development set and testing set, respectively. |
| Dataset Splits | Yes | Following previous studies such as that of Watanabe and Sumita (2015), we used Sections 2-21, 22 and 23 as the training set, development set and testing set, respectively. |
| Hardware Specification | No | The paper does not specify the hardware used for the experiments (e.g., CPU, GPU, memory, or specific computing infrastructure). |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | Yes | We used only one hidden layer with 256 units, set the word embedding dimension as 512, and used dropout for regularization, following the configuration of Vinyals et al. (2015). Pre-training was not implemented. Instead, the word embedding matrix and other network parameters were initialized randomly. For decoding, we used a beam search strategy with a fixed beam size of 10. |