Natural Language Reasoning, A Survey

Natural Language Reasoning, A Survey
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Original Paper
 
Abstract:
This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically. Conceptually, we provide a distinct definition for natural language reasoning in NLP, based on both philosophy and NLP scenarios, discuss what types of tasks require reasoning, and introduce a taxonomy of reasoning. Practically, we conduct a comprehensive literature review on natural language reasoning in NLP, mainly covering classical logical reasoning, natural language inference, multi-hop question answering, and commonsense reasoning. The paper also identifies and views backward reasoning, a powerful paradigm for multi-step reasoning, and introduces defeasible reasoning as one of the most important future directions in natural language reasoning research. We focus on single-modality unstructured natural language text, excluding neuro-symbolic techniques and mathematical reasoning.
 

Summary Notes

Simplifying Natural Language Reasoning in AI

The field of Natural Language Processing (NLP) is advancing quickly, especially with innovations like transformers and pre-trained language models (PLMs).
However, there's a noticeable gap in natural language reasoning (NLR), impacting complex AI applications and the depth of interaction between humans and computers.
This blog post breaks down NLR, explains why PLMs are critical, and looks at key methods and benchmarks driving NLR forward.

What is Natural Language Reasoning?

Natural Language Reasoning is AI's ability to understand and process human language through logical reasoning. It involves:
  • Defining Reasoning Tasks: Beyond understanding language, NLR uses logic to conclude, predict, or create new information.
  • Recognition Challenges: Distinguishing between reasoning and non-reasoning tasks is tricky, blurring the lines between comprehension and complex reasoning.
  • Common Misconceptions: It's incorrect to view all NLP tasks as reasoning tasks. While many involve reasoning, they don't all require deep logical processing.

The Importance of PLMs in Reasoning

Pre-trained language models have transformed AI's language understanding and generation capabilities, playing a crucial role in NLR:
  • Language Understanding: PLMs are key for tasks that demand reasoning, thanks to their nuanced language understanding.
  • Reasoning Abilities: These models are great at deductive, inductive, and abductive reasoning, drawing on extensive training data.
  • Complex Tasks: By integrating knowledge and reasoning, PLMs can address more sophisticated reasoning challenges.

Approaches to Natural Language Reasoning

Several methodologies are prominent in NLR:
  • End-to-End Reasoning: Directly solving tasks with PLMs, skipping intermediate steps.
  • Structured Reasoning: Mimicking human reasoning patterns either forward or backward to reach conclusions.
  • Interpretability: Ensuring AI systems can explain their reasoning is essential for trust and understanding.

Measuring NLR: Benchmarks and Datasets

Various benchmarks and datasets help evaluate AI reasoning capabilities:
  • Logical Reasoning: Tests the model's logical step-following or inference-making ability.
  • Multi-hop Question Answering: Requires pulling from multiple information sources to answer questions.
  • Commonsense Reasoning: Focuses on processing and applying everyday knowledge.

Looking Ahead: The Future of NLR

Despite progress, challenges and questions remain:
  • Open Questions: How can we improve PLMs' reasoning abilities? Which new methodologies will enhance reasoning?
  • Research Directions: Future work includes exploring multi-modal reasoning, enhancing model robustness, and broadening reasoning abilities.
  • Applications: The goal is creating AI that handles complex, real-world reasoning tasks as effortlessly as humans.

Conclusion

Advancing natural language reasoning in AI is a work in progress. Although PLMs have established a solid base, defining, understanding, and implementing reasoning in NLP still require significant effort. For AI engineers, keeping up with these developments and pushing the boundaries of what's possible is crucial. The future of NLP hinges on our ability to close the natural language reasoning gap, opening up new possibilities for complex applications and richer human-computer interactions.

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