Challenge: Existing systems for multi-hop reading comprehension decompose compositional questions into simpler sub-questions . authors propose a system that learns to break compositional multi- hop questions into simple singlehop sub-question .
Approach: They propose a system that decomposes a compositional question into simpler sub-questions . they propose recast subquestion generation as a span prediction problem .
Outcome: The proposed system generates as effective as human-authored sub-questions using 400 examples . it also provides explainable evidence for its decision making in the form of sub-questions .

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Compositional Questions Do Not Necessitate Multi-hop Reasoning (P19-1)

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Challenge: a single-hop reasoning model can solve much more of the dataset than previously thought.
Approach: They propose a single-hop BERT-based RC model that achieves 67 F1 . they propose an evaluation setting where humans are not shown all paragraphs .
Outcome: The proposed model achieves 67 F1—comparable to state-of-the-art multi-hop models.
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension (2021.emnlp-main)

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Challenge: Recent approaches to multi-hop Reading Comprehension (RC) have greatly improved its explainability, models ability to explain their own answers.
Approach: They propose to generate a question-focused abstractive summary of input paragraphs and feed it to an RC system.
Outcome: The proposed explanation generates more compact explanations than an extractive explainer with limited supervision while maintaining sufficiency.
Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions? (2021.eacl-main)

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Challenge: Existing models fail to answer a large portion of sub-questions . Existing systems have achieved super-human performance .
Approach: They propose to use a neural decomposition model to generate sub-questions for a multi-hop question and extract the corresponding sub-answers.
Outcome: The proposed model is based on a hotpotQA dataset with a multi-hop question and sub-answers.
Multi-style Generative Reading Comprehension (P19-1)

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Challenge: Current studies on generative reading comprehension (RC) focus on extracting an answer span from textual evidence and natural language generation (NLG).
Approach: They propose a multi-style abstractive summarization model for question answering called Masque.
Outcome: The proposed model achieves state-of-the-art performance on the Q&A and Q& A + NLG tasks of MS MARCO and NarrativeQA.
A Simple and Effective Model for Answering Multi-span Questions (2020.emnlp-main)

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Challenge: Existing models for reading comprehension restrict output space to a set of single contiguous spans . multi-span questions are problematic because they require multiple inputs - a task that requires a sequence tagging problem .
Approach: They propose a simple architecture for answering multi-span questions by casting the task as a sequence tagging problem.
Outcome: The proposed model significantly improves performance on span extraction questions from DROP and Quoref by 9.9 and 5.5 EM points respectively.
Unsupervised Question Decomposition for Question Answering (2020.emnlp-main)

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Challenge: Existing QA systems struggle to answer complex questions because information is scattered in different places.
Approach: They propose an unsupervised algorithm that decomposes hard questions into simpler sub-questions . they propose an algorithm that can be used to generate a final answer from millions of questions .
Outcome: The proposed algorithm decomposes hard questions into simpler sub-questions that existing QA systems can answer.
Low-Resource Generation of Multi-hop Reasoning Questions (2020.acl-main)

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Challenge: Existing methods to generate valid and fluent questions from text are limited and insufficient for training.
Approach: They propose to generate multi-hop reasoning questions from the raw text in a low resource circumstance by deducing over multiple relations on several sentences in the text.
Outcome: The proposed model can be applied to the task of machine reading comprehension and achieve significant performance improvements.
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension (D18-1)

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Challenge: Recent deep-learning based models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span.
Approach: They propose a novel context zoom-in network (ConZNet) that can skip through irrelevant parts of a document and generate an answer using only the relevant regions of text.
Outcome: The proposed architecture outperforms state-of-the-art results by 12.62% (ROUGE-L) relative improvement on the recently proposed and challenging RC dataset ‘NarrativeQA’.
Commonsense for Generative Multi-Hop Question Answering Tasks (D18-1)

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Challenge: Reading comprehension QA tasks have seen a recent surge in popularity, yet most work has focused on fact-finding extractive QA.
Approach: They propose a multi-hop generative task that uses a pointer-generator decoder to synthesize disjoint pieces of information within the context to generate an answer.
Outcome: The proposed model performs better than previous generative models and is competitive with current state-of-the-art span prediction models.
Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering (2022.coling-1)

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Challenge: Existing methods for multi-hop reasoning ignore grounding on supporting facts of each step, which tends to generate inaccurate decompositions.
Approach: They propose an interpretable stepwise reasoning framework that incorporates supporting sentences and questions at each intermediate step and utilizes the inference of the current hop for the next until reasoning out the final result.
Outcome: The proposed model can boost performance and yield a better interpretable reasoning process without decomposition supervision.

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