Asynchronous Multi-grained Graph Network For Interpretable Multi-hop Reading Comprehension
Authors: Ronghan Li, Lifang Wang, Shengli Wang, Zejun Jiang
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed model on the Hotpot QA dataset and achieve top competitive performance in distractor setting compared with other published models. Extensive experiments show that the asynchronous update mechanism can effectively form interpretable reasoning chains at different granularity levels. Table 1 shows published and unpublished models on both development and blind test set3 of Hotpot QA. |
| Researcher Affiliation | Academia | Ronghan Li , Lifang Wang , Shengli Wang and Zejun Jiang School of Computer Science and Engineering, Northwestern Polytechnical University, Xi an, China {lrh000, wsljsj}@mail.nwpu.edu.cn, {wanglf,claud}@nwpu.edu.cn |
| Pseudocode | No | The paper describes an algorithm for asynchronous message propagation and provides mathematical formulations, but it does not include a clearly labeled pseudocode block or algorithm figure. |
| Open Source Code | No | The paper mentions implementing experiments based on Huggingface and DFGN's open-source implementation with footnotes linking to their repositories, but it does not provide an explicit statement or link for the open-source code of their proposed AMGN model. |
| Open Datasets | Yes | We evaluate our method on Hotpot QA [Yang et al., 2018], which is a prevalent benchmark for multi-hop MRC. |
| Dataset Splits | Yes | For each question, the Distractor setting contains two gold paragraphs with ground-truth answers and supporting facts and eight negative paragraphs as distractors... We finetune on the training set for 8 epochs, with batch size as 32. Table 1 shows published and unpublished models on both development and blind test set3 of Hotpot QA. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Huggingface" and the "official open-source implementation of DFGN" and "Roberta-large" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We use Roberta-large for paragraph selection and set K = 3. Since Hotpot QA only requires two-hop reasoning, a two-step graph update is conducted thus T is 2. We finetune on the training set for 8 epochs, with batch size as 32. For optimization, We use BERTAdam with an initial learning rate of 2e 5. |