Summary DROP A Reading Comprehension Benchmark for Discrete Reasoning arxiv.org
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Researchers have developed a new reading comprehension benchmark called DROP that combines neural methods with symbolic reasoning to test discrete reasoning.
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Key Points
- Researchers have introduced a new English reading comprehension benchmark called DROP.
- DROP focuses on discrete reasoning over paragraphs and aims to push for a more comprehensive analysis of paragraph understanding.
- The dataset consists of 96,567 questions from various categories, with an emphasis on sports game summaries and history passages.
- Baseline systems performed poorly on the DROP dataset, with the best performing system achieving only 32.7% F1.
- A new model called NAQANet achieved 47.0% F1 on the dataset, showing promise in combining neural methods with symbolic reasoning.
- Complex types of reasoning, such as arithmetic operations and counting, were particularly difficult for the models.
- The results highlight the need for further research in combining neural methods with symbolic reasoning and improving information extraction for semantic parsing tasks.
Summaries
19 word summary
Researchers have introduced DROP, a reading comprehension benchmark focusing on discrete reasoning and combining neural methods with symbolic reasoning.
111 word summary
Researchers have introduced a new English reading comprehension benchmark called DROP. It focuses on discrete reasoning over paragraphs and requires systems to resolve references in a question and perform operations such as addition, counting, or sorting. The dataset consists of 96,567 questions from various categories, with an emphasis on sports game summaries and history passages. The best performing system achieved 32.7% F1, while a new model called NAQANet achieved 47.0% F1, showing promise in combining neural methods with symbolic reasoning. The DROP dataset highlights challenges in arithmetic operations and counting, emphasizing the need for further research in combining neural methods with symbolic reasoning and improving information extraction for semantic parsing tasks.
132 word summary
Researchers have introduced a new English reading comprehension benchmark called DROP, which focuses on discrete reasoning over paragraphs. DROP requires systems to resolve references in a question and perform operations such as addition, counting, or sorting. The dataset consists of 96,567 questions from various categories, with an emphasis on sports game summaries and history passages. Baseline systems were evaluated on the DROP dataset, with the best performing system achieving 32.7% F1 and human performance at 96.4%. A new model called NAQANet achieved 47.0% F1, showing promise in combining neural methods with symbolic reasoning. Complex types of reasoning, such as arithmetic operations and counting, posed challenges for the models. The DROP dataset emphasizes the need for further research in combining neural methods with symbolic reasoning and improving information extraction for semantic parsing tasks.
295 word summary
Researchers have introduced a new English reading comprehension benchmark called DROP, which focuses on discrete reasoning over paragraphs. The goal of this benchmark is to push the field towards a more comprehensive analysis of paragraph understanding. Unlike previous datasets, DROP requires systems to resolve references in a question and perform discrete operations over the content of paragraphs, such as addition, counting, or sorting. The dataset was constructed through crowdsourcing, with passages collected from Wikipedia and challenging questions created by crowd workers. The dataset consists of 96,567 questions from various categories, with an emphasis on sports game summaries and history passages. The answers to the questions are required to be spans in the passage or question, numbers, or dates.
Baseline systems were evaluated on the DROP dataset, including semantic parsing models, SQuAD-style reading comprehension models, and heuristic baselines. The best performing system achieved only 32.7% F1 on the dataset, while human performance was 96.4%. A new model called NAQANet was also introduced, which combines neural reading comprehension with limited numerical reasoning. This model achieved 47.0% F1 on the dataset, showing promise in combining neural methods with symbolic reasoning.
The performance of all tested models on the DROP dataset was significantly lower compared to other reading comprehension datasets, highlighting the challenges posed by this benchmark. Error analysis revealed that complex types of reasoning, such as arithmetic operations and counting, were particularly difficult for the models. Semantic parsing baselines performed poorly due to limitations in information extraction and spuriousness of logical forms used for training.
In conclusion, the DROP dataset presents a challenging benchmark for reading comprehension that requires comprehensive paragraph understanding and discrete reasoning. The results highlight the need for further research in combining neural methods with symbolic reasoning and improving information extraction for semantic parsing tasks.