| Challenge: | Conversational machine comprehension (CMC) is a research track in conversational AI. |
| Approach: | They propose to synthesize a generic framework for CMC models and highlight differences in recent approaches. |
| Outcome: | The proposed model will be used as a compendium for future research. |
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| Challenge: | Existing models combine previous questions for conversation understanding and only employ recurrent neural networks (RNN) for reasoning. |
| Approach: | They propose a multi-perspective convolutional cube model that integrates 1D and 2D convolutions with recurrent neural networks (RNN) to understand context from different perspectives. |
| Outcome: | The proposed model is based on the Conversational Question Answering (CoQA) dataset and achieves state-of-the-art results. |
A Framework for Evaluation of Machine Reading Comprehension Gold Standards (2020.lrec-1)
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| Challenge: | Existing literature on machine reading comprehension (MRC) data is limited on the data design of gold standards. |
| Approach: | They propose a framework to investigate linguistic features, lexical cues and ambiguity in MRC gold standards. |
| Outcome: | The proposed framework investigates the present linguistic features, required reasoning and background knowledge and factual correctness on the one hand, and the presence of lexical cues as a lower bound for the requirement of understanding on the other. |
Benchmarking Machine Reading Comprehension: A Psychological Perspective (2021.eacl-main)
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| Challenge: | MRC is a task that tests the ability of a machine to read and understand unstructured text. |
| Approach: | They propose a theoretical basis for the design of MRC datasets based on psychology and psychometrics and propose shortcut-proof questions and explanations as a part of the task design. |
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ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension (2022.coling-1)
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| Challenge: | Existing methods require three steps to understand text, but span extraction and question rephrasing steps are not fully exploited. |
| Approach: | They propose a framework for conversational machine reading comprehension based on shared parameter mechanism . experimental results show the proposed framework achieves new state-of-the-art results on the ShARC leaderboard . |
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StuBot: Learning by Teaching a Conversational Agent Through Machine Reading Comprehension (2022.findings-emnlp)
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| Challenge: | StuBot provides adaptive feedback for learning by teaching . |
| Approach: | They propose a text-based conversational agent that provides adaptive feedback for learning by teaching. |
| Outcome: | The proposed agent improves learning performance, immersion, and overall experience by providing adaptive feedback to the users who input the explanation text. |
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension (D19-58)
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| Challenge: | Existing machine comprehension models focus on a single-turn setting and do not account for previous reasoning processes. |
| Approach: | They propose to explicitly model the information gain through the dialogue reasoning . they propose to apply the proposed mechanism to other machine comprehension models . |
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Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension (2023.findings-emnlp)
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Nuo Chen, Hongguang Li, Junqing He, Yinan Bao, Xinshi Lin, Qi Yang, Jianfeng Liu, Ruyi Gan, Jiaxing Zhang, Baoyuan Wang, Jia Li
| Challenge: | Existing benchmarks for conversational machine reading comprehension are inconsistent with real scenarios. |
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Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Existing methods for machine reading comprehension rely on manually defined features and are difficult to generalize to other tasks. |
| Approach: | They propose a Syntax and Frame Semantics model for Machine Reading Comprehension which takes full advantage of syntax and frame semantics to get richer text representation. |
| Outcome: | The proposed model outperforms ten state-of-the-art models on machine reading comprehension tasks. |
Machine Reading Comprehension using Case-based Reasoning (2023.findings-emnlp)
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Dung Thai, Dhruv Agarwal, Mudit Chaudhary, Wenlong Zhao, Rajarshi Das, Jay-Yoon Lee, Hannaneh Hajishirzi, Manzil Zaheer, Andrew McCallum
| Challenge: | Current state-of-the-art machine readers do not support case-based reasoning . |
| Approach: | They propose a method that extracts a set of similar cases from a nonparametric memory and then predicts an answer by selecting the span in the test context that is most similar to the contextualized representations of answers. |
| Outcome: | The proposed method outperforms baselines on NaturalQuestions and NewsQA by 11.5 and 8.4 EM. |
Aspect-based Sentiment Analysis as Machine Reading Comprehension (2022.coling-1)
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| Challenge: | Existing approaches to aspect-based sentiment analysis stack multiple modules and result in severe error propagation. |
| Approach: | They propose a MRC-PrOmpt mOdeL framework where multiple sentiment aspects are elicited by a machine reading comprehension model and their corresponding sentiment polarities are classified in a prompt learning way. |
| Outcome: | The proposed framework significantly outperforms existing state-of-the-art models or achieves comparable performance on widely-used benchmark datasets. |