RINK: Reader-Inherited Evidence Reranker for Table-and-Text Open Domain Question Answering
Authors: Eunhwan Park, Sung-Min Lee, Dearyong Seo, Seonhoon Kim, Inho Kang, Seung-Hoon Na
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on OTT-QA, a largescale table-and-text open-domain question answering dataset, show that the proposed RINK armed with our pretraining procedure makes improvements over the baseline reranking method and leads to state-of-the-art performance. |
| Researcher Affiliation | Collaboration | Eunhwan Park1, Sung-Min Lee1, Daeryong Seo3, Seonhoon Kim2*, Inho Kang3, Seung-Hoon Na1 1 Jeonbuk National University 2 Coupang 3 Naver Corporation |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | Experimental results on OTT-QA, a largescale table-and-text open-domain question answering dataset, show that the proposed RINK armed with our pretraining procedure makes improvements over the baseline reranking method and leads to state-of-the-art performance. |
| Dataset Splits | Yes | Table 1 shows the detailed statistics of OTT-QA (Chen et al. 2021)...Train Dataset 41,469 Developement Dataset 2,214 Test Dataset 2,158 |
| Hardware Specification | Yes | All experiments were conducted using eight NVIDIA Quadro RTX A6000 GPUs. |
| Software Dependencies | No | The paper mentions 'Ro BERTa-base' and 'T5-base' models but does not provide specific version numbers for general software dependencies like programming languages or libraries (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We used a batch size of 16 and learning rates of 5 10 5 and 1 10 4 to train the BERT and T5, respectively. The Adam W optimizer was used for training. |