Natural Language Inference in Context -- Investigating Contextual Reasoning over Long Texts
ConTRoL dataset for contextual reasoning over long texts challenges current models.
Key Findings
Methodology
The study introduces the ConTRoL dataset, focusing on contextual reasoning over long texts. The dataset comprises 8,325 'context-hypothesis' pairs, covering complex reasoning types like logical reasoning. It is sourced from police recruitment verbal reasoning tests, offering higher quality than crowdsourced data.
Key Results
- BART model achieves 56.34% accuracy on ConTRoL, significantly lower than human performance at 87.06%.
- Existing models perform poorly on long text reasoning tasks, especially in information integration and logical reasoning.
- Human performance on ConTRoL far exceeds current models, highlighting deficiencies in handling complex reasoning tasks.
Significance
This study reveals the limitations of existing natural language inference models in contextual reasoning over long texts through the ConTRoL dataset, advancing research in complex reasoning tasks. The dataset can also test downstream tasks like factual correctness in summaries.
Technical Contribution
The introduction of the ConTRoL dataset provides a new benchmark in the field of natural language inference, particularly for long texts and complex reasoning tasks. It surpasses traditional sentence-level inference by emphasizing the necessity of multi-paragraph reasoning.
Novelty
ConTRoL is the first dataset focusing on contextual reasoning over long texts, filling gaps in reasoning complexity and text length, offering a more challenging test environment.
Limitations
- Existing models perform poorly on long text reasoning tasks, especially in information integration and logical reasoning.
- The complexity of the dataset may increase model training time.
Future Work
Future research could explore improving model performance in long text reasoning, particularly in information integration and logical reasoning. New methods could be developed to handle longer texts and more complex reasoning tasks.
AI Executive Summary
Natural Language Inference (NLI) is a fundamental task in natural language processing, typically conducted at the sentence level. However, existing datasets fall short in testing contextual reasoning over long texts. To address this, researchers introduce the ConTRoL dataset, focusing on complex reasoning tasks over long texts. The dataset comprises 8,325 'context-hypothesis' pairs, covering various reasoning types like logical reasoning, sourced from police recruitment verbal reasoning tests, offering higher quality than crowdsourced data.
Experimental results show that state-of-the-art language models perform significantly worse than humans on ConTRoL, particularly in information integration and logical reasoning. This indicates substantial deficiencies in current models when handling complex reasoning tasks.
The introduction of the ConTRoL dataset provides a new benchmark in the field of natural language inference, advancing research in complex reasoning tasks. Future research could explore improving model performance in long text reasoning, particularly in information integration and logical reasoning.
Deep Analysis
Background
Natural Language Inference (NLI) is a fundamental task in natural language processing, typically used to test the semantic representation capabilities of language models. Existing NLI datasets are mostly sentence-level, suitable for testing semantic representation but inadequate for testing contextual reasoning over long texts.
Core Problem
Existing NLI datasets are inadequate for testing contextual reasoning over long texts, unable to fully evaluate model performance in complex reasoning tasks.
Innovation
The ConTRoL dataset focuses on contextual reasoning over long texts, filling gaps in reasoning complexity and text length.
Methodology
- �� Introduce the ConTRoL dataset, focusing on contextual reasoning over long texts.
- �� The dataset comprises 8,325 'context-hypothesis' pairs, covering various reasoning types.
- �� Sourced from police recruitment verbal reasoning tests, offering higher quality than crowdsourced data.
Experiments
Experiments use various state-of-the-art language models like BERT, RoBERTa, and BART to evaluate their performance on the ConTRoL dataset. Results show these models perform poorly on long text reasoning tasks.
Results
BART model achieves 56.34% accuracy on ConTRoL, significantly lower than human performance at 87.06%. Existing models perform poorly in information integration and logical reasoning.
Applications
The ConTRoL dataset can be used to test downstream tasks like factual correctness in summaries, advancing research in complex reasoning tasks.
Limitations & Outlook
Existing models perform poorly on long text reasoning tasks, especially in information integration and logical reasoning. The complexity of the dataset may increase model training time.
Plain Language Accessible to non-experts
Imagine you're watching a long TV series with many characters and plots. To understand the whole story, you need to remember each character's relationships and plot developments. This is like the task in the ConTRoL dataset: models need to find clues in long texts to infer the correct conclusion.
ELI14 Explained like you're 14
Imagine you're playing a complex puzzle game. Each level has many clues, and you need to piece them together to find the truth. That's the task of the ConTRoL dataset: making computers as smart as you, able to find answers in long stories!
Glossary
ConTRoL
A dataset for ConTextual Reasoning over Long Texts, focusing on reasoning over long texts.
Used to test model reasoning capabilities in long texts.
Natural Language Inference
The task of determining whether a hypothesis can be reasonably inferred from a premise.
Used to evaluate language model semantic understanding.
Logical Reasoning
The process of inferring conclusions through logical relationships.
A type of reasoning in the ConTRoL dataset.
Contextual Reasoning
The process of inferring based on contextual information.
The core task of the ConTRoL dataset.
BART
A pre-trained model for sequence-to-sequence tasks, combining bidirectional and autoregressive Transformers.
Performed better than other models on the ConTRoL dataset.
Open Questions Unanswered questions from this research
- 1 Existing models perform poorly on long text reasoning tasks, especially in information integration and logical reasoning.
- 2 How to improve model performance in complex reasoning tasks remains an open question.
Applications
Immediate Applications
Summary Fact Checking
The ConTRoL dataset can be used to test model performance in summary fact-checking tasks.
Long-term Vision
Complex Reasoning Tasks
Advance research in natural language processing for complex reasoning tasks, developing more powerful language models.
Abstract
Natural language inference (NLI) is a fundamental NLP task, investigating the entailment relationship between two texts. Popular NLI datasets present the task at sentence-level. While adequate for testing semantic representations, they fall short for testing contextual reasoning over long texts, which is a natural part of the human inference process. We introduce ConTRoL, a new dataset for ConTextual Reasoning over Long texts. Consisting of 8,325 expert-designed "context-hypothesis" pairs with gold labels, ConTRoL is a passage-level NLI dataset with a focus on complex contextual reasoning types such as logical reasoning. It is derived from competitive selection and recruitment test (verbal reasoning test) for police recruitment, with expert level quality. Compared with previous NLI benchmarks, the materials in ConTRoL are much more challenging, involving a range of reasoning types. Empirical results show that state-of-the-art language models perform by far worse than educated humans. Our dataset can also serve as a testing-set for downstream tasks like Checking Factual Correctness of Summaries.