Challenge: Question Paraphrase Retrieval (QPR) systems can be used to answer rare and noisy reformulations of common questions by mapping them to a set of canonical forms.
Approach: They propose a Question Paraphrase Retrieval (QPR) system that retrieves equivalent questions that result in the same answer as the original question.
Outcome: The proposed system outperforms the standard loss function in NIR with noisy labels on two QPR datasets.

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Adaptive Document Retrieval for Deep Question Answering (D18-1)

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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.
Neural-Driven Search-Based Paraphrase Generation (2021.eacl-main)

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Challenge: Existing non-supervised paraphrase generation models are biased toward specific problems like question answering or image captioning.
Approach: They propose a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance.
Outcome: The proposed algorithms perform well against non-supervised baselines.
ReQA: An Evaluation for End-to-End Answer Retrieval Models (D19-58)

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Challenge: Popular QA benchmarks like SQuAD have driven progress on identifying answer spans within a specific passage . retrieving relevant answers from a huge corpus of documents is still a challenging problem .
Approach: They propose a benchmark for evaluating large-scale sentence-level answer retrieval models . they establish baselines using both neural encoding models and classical retrieval techniques .
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Large-Scale QA-SRL Parsing (P18-1)

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Challenge: a crowd-sourced approach to learning semantic parsers to predict predicateargument structures is open to many researchers.
Approach: They propose a large-scale corpus of Question-Answer driven Semantic Role Labeling annotations . they also propose QA-SRL Bank 2.0, a crowd-sourcing scheme that can be used to train high quality parsers .
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Dense Hierarchical Retrieval for Open-domain Question Answering (2021.findings-emnlp)

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Challenge: Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA) current dense retrievers require splitting documents into short passages that usually contain local, partial and sometimes biased context, and may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result.
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Harvesting Paragraph-level Question-Answer Pairs from Wikipedia (P18-1)

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Challenge: Existing models that only take into account sentence-level information do not generate question-answer pairs.
Approach: They propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism for paragraphlevel question generation.
Outcome: The proposed model outperforms existing models on a Wikipedia article question-answer generation task.
NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets (2020.emnlp-demos)

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Challenge: Existing tools for Question Answering (QA) have challenges that limit their use in practice.
Approach: They propose a library that integrates with existing infrastructure and offers helpful defaults for QA subtasks.
Outcome: NeuralQA integrates well with existing infrastructure and offers helpful defaults for QA subtasks.
Simple and Effective Semi-Supervised Question Answering (N18-2)

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Challenge: Existing deep learning systems for extractive Question Answering are limited and expensive to construct.
Approach: They propose a semi-supervised QA system where end user specifies a set of documents and only a few labelled examples.
Outcome: The proposed system achieves 50% F1 score on SQuAD and TriviaQA with very little labeled data.
Efficient Passage Retrieval with Hashing for Open-domain Question Answering (2021.acl-short)

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Challenge: Open-domain question answering systems often require large memory to run because of the massive size of their passage index.
Approach: They propose a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever to represent the passage index using compact binary codes.
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Retrieval-based Question Answering with Passage Expansion Using a Knowledge Graph (2024.lrec-main)

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Challenge: Recent advances in dense neural retrievers and language models have hindered performance, especially for less common entities and facts.
Approach: They propose a multi-modal passage retrieval model that combines entity features and textual data to improve retrieval precision for less common entities.
Outcome: The proposed model improves retrieval precision on less common entities and facts on common benchmarks.

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