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..
Capturing Greater Context for Question Generation
Authors: Luu Anh Tuan, Darsh Shah, Regina Barzilay9065-9072
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our hypothesis of using a controllable context to generate questions on three different QA datasets SQu AD, MS MARCO, and News QA. Our method strongly outperforms existing state-of-the-art models by an average absolute increase of 1.56 Rouge, 0.97 Meteor and 0.81 Bleu scores over the previous best reported results on all three datasets. |
| Researcher Affiliation | Academia | Luu Anh Tuan, Darsh J Shah, Regina Barzilay Computer Science and Arti๏ฌcial Intelligence Lab, MIT EMAIL |
| Pseudocode | No | The paper describes the model architecture and decoding process with equations and textual descriptions, but does not include formal pseudocode or an algorithm block. |
| Open Source Code | Yes | 1Our code and data are available at https://github.com/vivisimo/Question Generation |
| Open Datasets | Yes | We evaluate our model on 3 question answering datasets: SQu AD (Rajpurkar et al. 2016), MS Marco (Bajaj et al. 2016) and News QA (Trischler et al. 2016). These form a comprehensive set of datasets to evaluate question generation. |
| Dataset Splits | Yes | Table 1: Description of the evaluation datasets. l D , l Q and l A stand for average length of document, question and answer respectively. Dataset Train Dev Test l D l Q l A SQu AD-1 87,488 5,267 5,272 126 11 3 SQu AD-2 77,739 9,749 10,540 127 11 3 MS Marco 51,000 6,000 7,000 60 6 15 News QA 76,560 4,341 4,292 583 8 5 |
| Hardware Specification | No | The paper does not mention any specific hardware (GPU/CPU models, memory, etc.) used for training or experiments. |
| Software Dependencies | No | The paper mentions software components like "Bidirectional LSTM", "Adam optimizer", "GloVe vectors", and "NLTK" but does not provide specific version numbers for any of these dependencies. |
| Experiment Setup | Yes | We use a one-layer Bidirectional LSTM with hidden dimension size of 512 for the encoder and decoder. Our entire model is trained end-to-end, with batch size 64, maximum of 200k steps, and Adam optimizer with a learning rate of 0.001 and L2 regularization set to 10 6. We initialize our word embeddings with frozen pre-trained Glo Ve vectors (Pennington, Socher, and Manning 2014). Text is lowercased and tokenized with NLTK. We tune the step of biattention used in encoder from {1, 2, 3} on the development set. During decoding, we used beam search with the beam size of 10, and stopped decoding when every beam in the stack generates the < EOS > token. |