Answering questions by learning to rank - Learning to rank by answering questions (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to answer multiple-choice questions with no supporting documents are poor performance. |
| Approach: | They propose a method which can be used to semantically rank documents extracted from Wikipedia . they propose 'semantic ranking' method that latently learns to rank documents by their importance . |
| Outcome: | The proposed model achieves state-of-the-art accuracy on two datasets: ARC Easy and Challenge. |
Similar Papers
Retrieving Support to Rank Answers in Open-Domain Question Answering (2025.emnlp-main)
Copied to clipboard
| Challenge: | a novel question answering architecture retrieves content relevant to the combined pair . previous work on automatic claim verification has shown hallucinations . |
| Approach: | They propose a question-answer architecture that prioritizes supporting evidence . it retrieves paragraphs that directly substantiate the correctness of a with respect to q . |
| Outcome: | The proposed approach can be used by large language models to retrieve explanatory paragraphs that ground their reasoning. |
Training a Ranking Function for Open-Domain Question Answering (N18-4)
Copied to clipboard
| Challenge: | Recent advances in machine reading have inspired researchers to combine Information Retrieval with machine reading to tackle open-domain QA. |
| Approach: | They propose two neural network rankers that assign scores to different passages based on their likelihood of containing the answer to a given question. |
| Outcome: | The proposed models achieve human level performance in open-domain QA compared to reading comprehension-style QA because it is difficult to retrieve the pieces of paragraphs that contain the answer to the question. |
Pre-Training Methods for Question Reranking (2024.eacl-short)
Copied to clipboard
| Challenge: | Existing methods for Question Answering to search for semantically similar questions are not suitable for new questions. |
| Approach: | They propose an unsupervised method for retrieving and ranking questions . they use a question retrieval model and a selection model to rerank questions based on their relevance . |
| Outcome: | The proposed method achieves state-of-the-art performance on QRC and Quora-match datasets . it provides better and cheaper access to answers than the system generated them . |
Neural Ranking with Weak Supervision for Open-Domain Question Answering : A Survey (2023.findings-eacl)
Copied to clipboard
| Challenge: | Neural ranking models require substantial amounts of relevance annotations, which is costly to scale. |
| Approach: | They propose to train a NR model with weak supervision instead of annotations . they use a structured overview of standard WS signals used for training a model . |
| Outcome: | The proposed approach reduces the cost of annotations by using weak supervision instead of a parametric model. |
RankQA: Neural Question Answering with Answer Re-Ranking (P19-1)
Copied to clipboard
| Challenge: | RankQA extends the conventional two-stage process in neural question answering . RankQ achieves state-of-the-art performance on 3 out of 4 benchmark datasets . |
| Approach: | They propose to extend the conventional two-stage process in neural QA with a third stage that performs an additional answer re-ranking. |
| Outcome: | RankQA outperforms more complex question answering systems by a significant margin on 3 out of 4 benchmark datasets. |
Double Retrieval and Ranking for Accurate Question Answering (2023.findings-eacl)
Copied to clipboard
| Challenge: | Recent work shows that answer verification models can improve the state of the art in Question Answering . despite the fact that the supporting candidates are ranked only according to the relevancy with the question, the model still lacks the support needed for other answer candidates. |
| Approach: | They propose a double reranking model that selects the best support for each target answer . they propose 'second neural retrieval stage' to encode question and answer pair as query . |
| Outcome: | The proposed approach improves the state of the art in Question Answering . the proposed model ranked candidates according to relevancy and not the answer . but the proposed approach fails to provide the best support . |
MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing IR techniques contain deficiencies, posing a performance bottleneck . combining diverse approaches to retrieve information is a viable strategy . |
| Approach: | They propose an approach that leverages learning-to-rank techniques to combine heterogeneous IR systems. |
| Outcome: | The proposed method outperforms existing methods on two Retrieval Question Answering tasks. |
A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections (2021.acl-long)
Copied to clipboard
| Challenge: | Question answering systems typically use pipelines that retrieve documents at finer text granularities. |
| Approach: | They propose an architecture for document and snippet ranking that leverages intuition . they modified a natural questions dataset to test their model . |
| Outcome: | The proposed model outperforms pipelines in document retrieval on biomedical data . the proposed model is competitive with the existing model, despite fewer parameters . |
Adaptive Document Retrieval for Deep Question Answering (D18-1)
Copied to clipboard
| Challenge: | Existing methods for deep question answering do not understand the exact interplay between document retrieval and machine comprehension. |
| Approach: | They propose an adaptive document retrieval model that learns the optimal document number, conditional on the size of the corpus and the query. |
| Outcome: | The proposed model outperforms state-of-the-art methods on multiple benchmark datasets and in the context of corpora with variable sizes. |
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Neural ranking models produce the final document scores, but they are often treated as transient information and only the relative orderings are preserved to produce a ranking. |
| Approach: | They propose to exploit large language models (LLMs) to provide relevance and uncertainty signals for these neural text rankers to produce scale-calibrated scores through Monte Carlo sampling of natural language explanations (NLEs). |
| Outcome: | The proposed approach outperforms previous calibration methods and LLM-based methods for ranking, calibration, and query performance prediction tasks. |