Learning Language Games through Interaction
Learning language through SHRDLURN game interaction, model accuracy improved by 8%.
Key Findings
Methodology
The study uses the SHRDLURN game to simulate Wittgenstein's language games. A semantic parsing model learns language from human feedback. A pragmatics model is introduced to enhance learning efficiency.
Key Results
- The pragmatic model improved online accuracy by 8% among the top 10 players.
- Human strategies using compositionality and avoiding synonyms correlate positively with task performance.
- The pragmatic model accelerates learning for successful players.
Significance
This research offers a new approach to building adaptive natural language interfaces, especially in resource-poor languages and new domains. Interactive learning allows systems to better adapt to user needs, enhancing human-computer collaboration.
Technical Contribution
A new online learning algorithm is proposed, combining a pragmatics model to significantly enhance the speed and accuracy of semantic parsing models. The method effectively learns without a seed lexicon or annotated logical forms.
Novelty
This is the first application of language games in an interactive learning environment, innovatively combining a pragmatics model to significantly enhance learning efficiency.
Limitations
- The model learns slowly initially, requiring substantial human feedback.
- Limited adaptability to complex language structures.
- Game design might restrict language expression diversity.
Future Work
Future work could explore more complex game environments and language structures, further optimizing the pragmatics model to enhance learning efficiency.
AI Executive Summary
In the field of natural language processing, creating adaptive natural language interfaces remains a challenge. Existing methods often require extensive labeled data and struggle to meet personalized user needs.
This paper proposes a new interactive learning framework through the SHRDLURN game, simulating the language learning process in human-computer collaboration. The computer learns language from human feedback using a semantic parsing model, accelerated by a pragmatics model.
Experimental results show that the pragmatics model significantly improves learning efficiency, with an 8% increase in online accuracy among the top 10 players. This research offers new insights into building adaptive natural language interfaces, with significant academic and practical implications.
Deep Analysis
Background
The field of natural language processing has long aimed to build systems capable of understanding and generating human language. Traditional methods rely on large labeled datasets and predefined lexicons, struggling to adapt to dynamic language environments.
Core Problem
Existing natural language interfaces struggle to meet personalized user needs, especially in resource-poor languages and new domains. Enhancing system adaptability through interactive learning is a critical challenge.
Innovation
This paper innovatively applies Wittgenstein's language games concept to an interactive learning environment, combining a pragmatics model to significantly enhance the learning efficiency of semantic parsing models.
Methodology
- �� Use SHRDLURN game to simulate language learning
- �� Semantic parsing model learns from human feedback
- �� Introduce pragmatics model to accelerate learning
- �� Online learning algorithm optimizes parameter updates
Experiments
Experiments were conducted in the SHRDLURN game, collecting interaction data from 100 players. The effectiveness of the method was validated by comparing the performance of pragmatic and non-pragmatic models.
Results
The pragmatic model improved online accuracy by 8% among the top 10 players. Human strategies using compositionality and avoiding synonyms correlate positively with task performance.
Applications
The method can be used to build adaptive natural language interfaces, especially suitable for resource-poor languages and new domains, enhancing human-computer collaboration efficiency.
Limitations & Outlook
The model learns slowly initially, requiring substantial human feedback. Limited adaptability to complex language structures.
Plain Language Accessible to non-experts
Imagine a game where players must input commands to make a computer complete tasks. Initially, the computer doesn't understand human language and must learn through player feedback. As the game progresses, the computer gradually learns to understand the player's commands, becoming more efficient at completing tasks. It's like teaching a child to speak, where the child learns by observing and mimicking adults.
ELI14 Explained like you're 14
Imagine you're playing a block game where you have to tell a computer how to arrange the blocks, but the computer doesn't understand you at first. You have to teach it slowly, using simple words like 'remove red block.' As you interact, the computer starts to understand you, and the game becomes more fun! It's like playing with a new friend, and you both have to learn to understand each other.
Glossary
Semantic Parsing
The process of converting natural language into logical forms, aiding computers in understanding human language.
Used to convert player commands into executable actions by the computer.
Pragmatics
The study of language use, focusing on meaning in actual communication.
Enhances the computer's understanding of player intent.
Compositionality
The property of language where small units combine to form complex expressions.
Players using compositional strategies improve task performance.
Interactive Learning
The process of learning through human-computer interaction.
The computer learns language through interaction with players.
Wittgenstein's Language Games
A theory that language derives its meaning from use, emphasizing its functional nature.
Inspiration for this study's research.
Open Questions Unanswered questions from this research
- 1 How can this method be applied in more complex language environments? Its performance in complex structures needs validation.
- 2 How to reduce the model's reliance on substantial human feedback? Improving initial learning speed is a challenge.
Applications
Immediate Applications
Adaptive Language Interfaces
Can be used to build natural language interfaces that adapt to user needs, enhancing user experience.
Long-term Vision
Intelligent Human-Computer Collaboration
Enhancing human-computer collaboration efficiency through interactive learning, promoting the development of intelligent systems.
Abstract
We introduce a new language learning setting relevant to building adaptive natural language interfaces. It is inspired by Wittgenstein's language games: a human wishes to accomplish some task (e.g., achieving a certain configuration of blocks), but can only communicate with a computer, who performs the actual actions (e.g., removing all red blocks). The computer initially knows nothing about language and therefore must learn it from scratch through interaction, while the human adapts to the computer's capabilities. We created a game in a blocks world and collected interactions from 100 people playing it. First, we analyze the humans' strategies, showing that using compositionality and avoiding synonyms correlates positively with task performance. Second, we compare computer strategies, showing how to quickly learn a semantic parsing model from scratch, and that modeling pragmatics further accelerates learning for successful players.