Challenge: Compared to standard RC tasks, dialogue reading comprehension (DRC) has raised challenges because of the complex speaker information and noisy dialogue context.
Approach: They propose a new method for dialogue reading comprehension that extracts answers from dialogues by using key-utterances-extracting methods and a Question-Interlocutor Scope Realized Graph.
Outcome: The proposed method achieves state-of-the-art performance against previous works.

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Challenge: Existing approaches for multi-hop reasoning are lacking for local graph reasoning . existing approaches neglect local semantic structures in utterances .
Approach: They propose a question-aware global-to-local graph reasoning approach that expands the canonical Interlocutor-Utterance graph by introducing a query node.
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Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog (N18-1)

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Challenge: Existing approaches to reading comprehension on multiparty dialogs have focused on children's stories or newswire.
Approach: They propose a new corpus and a robust deep learning architecture for a task in reading comprehension on multiparty dialog.
Outcome: The proposed model outperforms the state-of-the-art model on a different genre using bidirectional LSTM, showing a 13.0+% improvement for longer dialogs.
Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension (2021.findings-emnlp)

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Challenge: Existing models for multi-party dialogue machine reading comprehension focus on how to incorporate speaker information into the model, which is usually rare in real scenarios.
Approach: They propose to model speaker and key-utterances using self-supervised prediction tasks and capture salient clues in a long dialogue.
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Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections (2022.emnlp-main)

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Challenge: Existing systems for text comprehension are inadequate for more holistic comprehension of a discourse.
Approach: They propose a new paradigm that captures both discourse and semantic links between sentences in the form of free-form, open-ended questions.
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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.
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Graph-Based Knowledge Integration for Question Answering over Dialogue (2020.coling-main)

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Challenge: Existing approaches for question answering over dialogue did not consider dialogue structure and background knowledge (e.g., relationships between speakers).
Approach: They propose a method which organizes a dialogue as a "relational graph" and uses edges to represent relationships between entities to encode multi-relations knowledge for reasoning.
Outcome: The proposed method is better at tackling complex questions requiring relational reasoning and defending adversarial attacks with distracting sentences.
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.
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On Making Reading Comprehension More Comprehensive (D19-58)

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Challenge: Getting machines to "understand" text is a vast and long-standing problem, made more challenging by the fact that it is not even clear what it means to understand text.
Approach: They propose a question-based approach to machine reading comprehension that uses a natural language question to test a system's comprehension of a passage of text.
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STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension (2022.emnlp-main)

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Challenge: Abstractive dialogue summarization is an important standalone task in natural language processing, but no previous work has explored whether it can be used to boost an NLP system's performance on other important dialogue comprehension tasks.
Approach: They propose a novel type of dialogue summarization task that decomposes and imitates the hierarchical, systematic and structured mental process that human beings usually go through when understanding and analyzing dialogues.
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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 .

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