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..
Explainable and Discourse Topic-aware Neural Language Understanding
Authors: Yatin Chaudhary, Hinrich Schuetze, Pankaj Gupta
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments over a range of tasks such as language modeling, word sense disambiguation, document classification, retrieval and text generation demonstrate ability of the proposed model in improving language understanding. |
| Researcher Affiliation | Collaboration | 1Corporate Technology, Machine Intelligence (MIC-DE), Siemens AG, Munich, Germany 2CIS, University of Munich (LMU), Munich, Germany. |
| Pseudocode | Yes | Algorithm 1 Computation of combined loss L; Algorithm 2 Utility functions |
| Open Source Code | Yes | Implementation of NCLM is available at: https://github.com/ Yatin Chaudhary/NCLM. |
| Open Datasets | Yes | We present experimental results of language modeling using our proposed models on APNEWS, IMDB and BNC datasets (Lau et al., 2017). We use three labeled datasets: 20Newsgroups (20NS), Reuters (R21578) and IMDB movie reviews (IMDB) (See supplementary for data statistics). |
| Dataset Splits | No | For data statistics and time complexity of experiments refer supplementary. Experimental setup: We follow Wang et al. (2018) for our experimental setup. See supplementary for detailed hyperparameter settings. |
| Hardware Specification | No | No explicit hardware specifications (e.g., specific GPU/CPU models, memory details) used for running experiments were provided in the main text. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., libraries, frameworks, programming language versions) were provided in the main text. |
| Experiment Setup | Yes | We fix the NLM sequence length to 30 and bigger sentences are split into multiple sequences of length less than 30. We initialize the input word embeddings for NLM with 300-dimensional pretrained embeddings extracted from word2vec (Mikolov et al., 2013) model trained on Google News. Models are trained using a learning rate of 1e-3 & batch size of 32. |