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. |
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How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks (D18-1)
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| Challenge: | Recent research addresses reading comprehension, where examples consist of (question, passage, answer) tuples. |
| Approach: | They establish sensible baselines for bAbI, SQuAD, CBT, CNN and Who-did-What datasets and compare them to their previous work. |
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Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences (N18-1)
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| Challenge: | Using a dataset of 6,500+ questions, we found that human solvers achieved an F1-score of 88.1%. |
| Approach: | They propose a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. |
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Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure (2020.coling-main)
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| Challenge: | Multiparty dialog applications such as discourse parsing and meeting summarization are now mainstream research. |
| Approach: | They propose to annotate a machine reading comprehension dataset with discourse structure built over multiparty dialog using a modified Segmented Discourse Representation Theory (SDRT) style. |
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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. |
| 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. |
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Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations (2020.acl-main)
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| Challenge: | Span-ConveRT is a light-weight model for dialog slot-filling . we show consistent gains over a span extractor and a BERT-based model . |
| Approach: | They propose a model for dialog slot-filling which frames the task as a turn-based span extraction task. |
| Outcome: | The proposed model is especially useful for few-shot learning scenarios. |
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning (D19-1)
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| Challenge: | Existing reading comprehension benchmarks do not contain complex coreferential phenomena . obtaining questions focused on such phenomena is difficult because of lexical cues . |
| Approach: | They propose to use a crowdsourced dataset to examine the ability of models to resolve coreference among entities in Wikipedia paragraphs. |
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What Makes Reading Comprehension Questions Difficult? (2022.acl-long)
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| Challenge: | a recent study shows that natural language understanding benchmarks are not able to measure future progress . a crowdsourcing approach is needed to collect diverse examples without sacrificing diversity or coverage. |
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DialogSum: A Real-Life Scenario Dialogue Summarization Dataset (2021.findings-acl)
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| Challenge: | Experimental results show unique challenges in dialogue summarization such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense. |
| Approach: | They propose a large-scale labeled dialogue summarization dataset . they use state-of-the-art neural models to analyze spoken dialogue summaries . |
| Outcome: | The proposed dataset can be used to analyze spoken dialogue summarization challenges. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
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Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey (2020.coling-main)
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| Challenge: | In recent years, neural-network based models have been used for a wide range of tasks, including slot filling and intent classification. |
| Approach: | They propose three neural architectures to model slot filling and intent classification . they propose independent models, joint models and transfer learning models that exploit the mutual benefit of the two tasks simultaneously and scale the model to new domains. |
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