Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, Junji Tomita
| 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. |
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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. |
Multi-hop Reading Comprehension through Question Decomposition and Rescoring (P19-1)
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| 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 . |
M3: A Multi-View Fusion and Multi-Decoding Network for Multi-Document Reading Comprehension (2022.emnlp-main)
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| Challenge: | Existing methods for multi-document reading comprehension cannot make full of the advantages of both approaches. |
| Approach: | They propose a multi-view fusion and multi-decoding method that integrates multiple documents for answering questions. |
| Outcome: | The proposed method improves on two mainstream multi-document reading comprehension datasets. |
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction (P19-1)
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Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata, Atsushi Otsuka, Itsumi Saito, Hisako Asano, Junji Tomita
| Challenge: | Question answering (QA) using textual sources for purposes such as reading comprehension has attracted much attention. |
| Approach: | They propose a Query Focused Extractor model for evidence extraction and multi-task learning with the QA model. |
| Outcome: | The proposed model achieves state-of-the-art evidence extraction score on hotpotQA and FEVER, which is a recognizing textual entailment task on a large textual database. |
AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents (2021.eacl-demos)
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| Challenge: | Existing systems that generate and answer questions in a question-and-answer format can facilitate reading comprehension. |
| Approach: | They propose a system that integrates question answering and question generation tasks to produce a list of Q&A items for a text. |
| Outcome: | The proposed system generates a catalog of Q&A items for a text. |
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning (D19-58)
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| Challenge: | Existing methods focus on improving in-domain performance, leaving open the question of how they can generalize to out-of-domain and unseen RC tasks. |
| Approach: | They propose a multi-task learning framework that learns the shared representation across different tasks and builds on a large pre-trained language model and fine-tuned on multiple RC datasets. |
| Outcome: | The proposed framework improves the BERT-Large baseline by 8.39 and 7.22 respectively. |
SkillQG: Learning to Generate Question for Reading Comprehension Assessment (2023.findings-acl)
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| Challenge: | Existing question generation systems focus on the literal nature of questions and rarely consider comprehension types of the generated questions. |
| Approach: | They propose a question generation framework with controllable comprehension types for machine reading comprehension models. |
| Outcome: | Empirical results show that SkillQG outperforms baselines in quality, relevance, and skill-controllability while showing a performance boost in downstream question answering task. |
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. |
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. |
Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation (2022.aacl-main)
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| Challenge: | Open-Domain Generative Question Answering has achieved impressive performance in English . combining document-level retrieval with answer generation can generate complete sentences . |
| Approach: | They propose an open-domain approach that combines document retrieval with answer generation to generate complete sentences in English . they propose a cross-lingual generative model that exploits passages written in multiple languages . |
| Outcome: | The proposed model outperforms answer sentence selection baselines for all 5 languages and monolingual pipelines for three out of five languages. |