Papers with machine comprehension
Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis (D19-58)
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| Challenge: | a lack of gold datasets and knowledge about PAS analysis makes it difficult to create accurate PAS analyses. |
| Approach: | They construct a Japanese blog-QA dataset and a reading comprehension QA dataset using crowdsourcing. |
| Outcome: | The proposed method is most effective, pre-training model to acquire domain knowledge and fine-tuning model based on PAS-QA dataset. |
Augmenting Neural Networks with First-order Logic (P19-1)
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| Challenge: | Existing paradigms for training neural networks require large datasets, a paper argues . we present a framework for introducing declarative knowledge to neural networks . |
| Approach: | They propose a framework for introducing declarative knowledge to neural networks . they compile logical statements into graphs that augment a network without extra learnable parameters or manual redesign. |
| Outcome: | The proposed framework improves on three tasks, especially in low-data regimes. |
CNN for Text-Based Multiple Choice Question Answering (P18-2)
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| Challenge: | Existing models for text-based multiple choice question answering are based on a text. |
| Approach: | They propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. |
| Outcome: | The proposed model outperforms several baseline models on the SciQ and TQA datasets. |
Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension (D18-1)
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| Challenge: | Existing QA models rely on learning interaction between document and question . current models require explicit attention to the document before or as it reads it . |
| Approach: | They propose a modular question answering task that enforces complete independence of the document encoder from the question encoder. |
| Outcome: | The proposed model achieves reasonable accuracy but significantly underperforms unconstrained QA models. |
Semantically Equivalent Adversarial Rules for Debugging NLP models (P18-1)
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| Challenge: | Complex machine learning models are often brittle, making different predictions for input instances that are extremely similar semantically. |
| Approach: | They propose to generalize semantically equivalent adversarial rules that induce adversaries on many instances to detect brittle models. |
| Outcome: | The proposed rules generate high-quality local adversaries for more instances than humans and induce four times as many mistakes as human experts. |
CliCR: a Dataset of Clinical Case Reports for Machine Reading Comprehension (N18-1)
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| Challenge: | Currently, machine comprehension datasets are extremely scarce for specialized domains. |
| Approach: | They propose a dataset for machine comprehension in the medical domain using clinical case reports with around 100,000 gap-filling queries about these cases. |
| Outcome: | The proposed dataset uses clinical case reports with around 100,000 gap-filling queries about these cases. |
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. |
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text (P19-1)
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| Challenge: | Experimental results on 3 popular datasets demonstrate the effectiveness of our approach. |
| Approach: | They propose a network to solve the inference problem by decomposing text into a series of attention-based reasoning steps. |
| Outcome: | The proposed network can be used to understand the meanings of given text to answer questions. |
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale (D19-1)
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| Challenge: | Recent advances in representation learning have separated progress in both IR and MC . few studies have examined the relationship between retrieval and comprehension at different levels of granularity for development of MRS systems. |
| Approach: | They propose a simple yet effective pipeline system with consideration on hierarchical semantic retrieval at both paragraph and sentence level and their potential effects on the downstream task. |
| Outcome: | The proposed system achieves state-of-the-art on the leaderboard test sets of both FEVER and HOTPOTQA. |
Using Natural Language Relations between Answer Choices for Machine Comprehension (N19-1)
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| Challenge: | Current approaches to the reading comprehension task quantify the relationship between each question and answer choice independently and pick the highest scoring option. |
| Approach: | They propose a method to leverage natural language relations between answer choices to improve machine comprehension. |
| Outcome: | The proposed model improves the performance of a reading comprehension task by leveraging natural language relations between answer choices. |
CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations (2025.findings-acl)
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| Challenge: | Discourse parsing datasets based on conversations are restricted to a single domain . a lack of discourse structures in audio-based conversations is a challenge . |
| Approach: | They introduce CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse parsing in conversations. |
| Outcome: | The proposed corpus is code-mixed in Hindi and English and annotated with nine discourse relations. |
RankQA: Neural Question Answering with Answer Re-Ranking (P19-1)
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| Challenge: | RankQA extends the conventional two-stage process in neural question answering . RankQ achieves state-of-the-art performance on 3 out of 4 benchmark datasets . |
| Approach: | They propose to extend the conventional two-stage process in neural QA with a third stage that performs an additional answer re-ranking. |
| Outcome: | RankQA outperforms more complex question answering systems by a significant margin on 3 out of 4 benchmark datasets. |
GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation (2022.emnlp-main)
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Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel Weld
| Challenge: | Effective human evaluation of text generation tasks remains an important, open area for research. |
| Approach: | They propose a system for running standardized human evaluations across different generation tasks. |
| Outcome: | The proposed system produces standardized human evaluations across tasks . it crowdsources predictions and ranks systems on leaderboards . the proposed system is not reproducible over time and different annotator populations . |
SCOP: Evaluating the Comprehension Process of Large Language Models from a Cognitive View (2025.acl-long)
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| Challenge: | despite the potential of large language models, it is difficult to fully count on them in real-world scenarios. |
| Approach: | They propose to examine how LLMs perform during the comprehension process from a cognitive perspective. |
| Outcome: | The proposed model analyzes how LLMs perform during the comprehension process from a cognitive perspective. |