Challenge: Existing open domain question answering systems provide a single answer to ambiguous questions.
Approach: They propose a re-ranking approach that takes query-passage relevance and passage-passance correlation into account to retrieve passages that are query-relevant and diverse.
Outcome: The proposed method outperforms state-of-the-art on the AmbigQA dataset.

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Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs (2020.coling-main)

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Challenge: Community-based question answering platforms take time to get useful information from among many answers.
Approach: They propose a method to select a diverse and non-redundant answer set rather than ranking the answers.
Outcome: The proposed method outperforms baseline methods on a Japanese CQA site . it calculates the answer importance and similarity between answers by using BERT .
Joint Passage Ranking for Diverse Multi-Answer Retrieval (2021.emnlp-main)

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Challenge: Existing approaches to multi-answer retrieval cannot reason about the set of passages jointly.
Approach: They propose a joint passage retrieval model focusing on reranking to solve multi-answer retrieval problem.
Outcome: The proposed model outperforms baseline models on three multi-answer datasets.
Retrieving Support to Rank Answers in Open-Domain Question Answering (2025.emnlp-main)

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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.
Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval (2024.acl-long)

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Challenge: Existing methods for retrieving relevant tables are not sufficient as many questions require retrieving multiple tables and joining them through a join plan that cannot be discerned from the user query itself.
Approach: They propose a method that uncovers useful join relations during table retrieval.
Outcome: The proposed method outperforms the state-of-the-art methods for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up 5.4% in accuracy.
Open Domain Question Answering over Tables via Dense Retrieval (2021.naacl-main)

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Challenge: Recent advances in open-domain QA focus on retrieving textual passages . a retriever designed to handle tabular context can improve retrieval quality .
Approach: They propose a tabular-based retrieval model that improves retrieval quality over a BERT-based retriever.
Outcome: The proposed retriever improves retrieval quality with mined hard negatives over a BERT-based retriever.
Open-Domain Question Answering (2020.acl-tutorials)

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Challenge: tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering (QA)
Approach: tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering . focus will shift to cutting- edge models proposed for open- domain QA .
Outcome: The tutorial will cover cutting-edge research in open-domain question answering (QA) it will cover two-stage retriever-reader approaches, dense retriever and end-to-end training, and retriever free methods .
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization (P19-1)

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Challenge: Despite the empirical success of multi-document summarization, most datasets remain small and the cost of hiring hu-1 is prohibitive.
Approach: They propose a novel method for extractive multi-document summarization that measures redundancy between a pair of sentences based on surface form and semantic information.
Outcome: The proposed method outperforms baseline methods on benchmark datasets and is particularly useful for documents created by multiple authors containing redundant yet lexically diverse expressions.
Joint Inference of Retrieval and Generation for Passage Re-ranking (2024.findings-eacl)

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Challenge: Existing methods for re-ranking documents are sparse and do not require training.
Approach: They propose a method that optimizes mutual information between query and passage distributions by integrating cross-encoders and generative models in the re-ranking process.
Outcome: The proposed method outperforms conventional re-rankers and language model scorers in open-domain QA retrieval settings and diverse retrieval benchmarks under zero-shot settings.
Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework (2022.acl-long)

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Challenge: Existing approaches to open-domain question answering use a rerank-then-read framework . existing approaches use reranked evidence to predict multiple valid answers .
Approach: They propose to use a recall-then-verify framework to solve open-domain questions . the framework separates the reasoning process of each answer to make better use of retrieved evidence .
Outcome: The proposed framework predicts significantly more gold answers on open-domain questions than existing systems that use an oracle reranker.
Open-World Evaluation for Retrieving Diverse Perspectives (2025.naacl-long)

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Challenge: Existing retrieval systems only cover diverse perspectives on 33.74% of the examples . existing systems only focus on relevance to the question, ignoring diversity.
Approach: They build a Benchmark for Retrieval Diversity for Subjective questions (BERDS) based on a question and diverse perspectives associated with the question . they evaluate retrievers paired with a corpus to determine whether each document contains a perspective .
Outcome: The proposed approach improves retrieval diversity on complex questions . existing retrieval systems only cover diverse perspectives on 33.74% of the examples .

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