Papers with question representation
GoG: Relation-aware Graph-over-Graph Network for Visual Dialog (2021.findings-acl)
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| Challenge: | Experimental results show that our model outperforms the strong baseline in both generative and discriminative settings by a significant margin. |
| Approach: | They propose a relation-aware graph-over-graph network (GoG) for visual dialog . their model outperforms the strong baseline in both generative and discriminative settings . |
| Outcome: | The proposed model outperforms baseline models in both generative and discriminative settings by a significant margin. |
Conversational Question Answering with Language Models Generated Reformulations over Knowledge Graph (2024.findings-acl)
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| Challenge: | State-of-the-art ConvQA methods struggle with inexplicit question-answer pairs, which can degrade ConvQ performance. |
| Approach: | They propose a reinforcement learning based model, CoRnNet, which utilizes question reformulations generated by large language models to improve ConvQA performance. |
| Outcome: | The proposed model outperforms state-of-the-art ConvQA models by using question reformulations generated by large language models (LLMs). |
Question Condensing Networks for Answer Selection in Community Question Answering (P18-1)
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| Challenge: | Community question answering (CQA) is a subtask of community question answering . previous researches ignored the difference between the two parts and concatenated them as the question representation . |
| Approach: | They propose a question condensing network that makes use of the subject-body relationship of community questions. |
| Outcome: | The proposed model outperforms existing models on two CQA datasets. |
An Interpretable Reasoning Network for Multi-Relation Question Answering (C18-1)
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| Challenge: | Existing models for multi-relation question answering require elaborated analysis and reasoning over multiple fact triples in knowledge base. |
| Approach: | They propose a model that employs an interpretable hop-by-hop reasoning process for question answering . it decides which part of an input question should be analyzed at each hop and then drives next-hop thinking . |
| Outcome: | The proposed model yields state-of-the-art results on two datasets. |
Few-Shot Question Answering by Pretraining Span Selection (2021.acl-long)
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| Challenge: | Pretraining models with recurring span selection are effective, but perform poorly in a few-shot setting. |
| Approach: | They propose recurring span selection scheme that asks model to select correct span in passage with multiple sets of recurring recurrings. |
| Outcome: | The proposed model achieves 72.7 F1 on multiple benchmarks while maintaining competitive performance in the high-resource setting. |
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space (2020.emnlp-main)
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| Challenge: | Existing data augmentation techniques for natural language processing tasks are difficult to design. |
| Approach: | They propose a controllable rewriting based question data augmentation method for machine reading comprehension, question generation and question-answering natural language inference tasks. |
| Outcome: | The proposed method generates high-quality, high-quality question data samples on machine reading comprehension, question generation, and question-answering natural language inference tasks. |
EEE-QA: Exploring Effective and Efficient Question-Answer Representations (2024.lrec-main)
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| Challenge: | Current approaches to question answering rely on pre-trained language models like RoBERTa. |
| Approach: | They propose a pooling approach that embeds all answer candidates with the question . they also propose enabling cross-reference between answer choices . |
| Outcome: | The proposed methods improve throughput and memory efficiency with little sacrifice in performance. |