Applying Commonsense Reasoning with AI
- 8 minutes read - 1615 words
Table of Contents
Commonsense reasoning refers to the ability to make inferences and draw conclusions based on everyday knowledge and understanding of concepts that most people share.
In natural language processing (NLP), commonsense reasoning is paramount, enabling AI systems to understand and interpret text and language as humans do.
This helps improve communication, interaction, and comprehension between humans and machines.
Challenges in Commonsense Reasoning for NLP
There are several challenges in incorporating commonsense reasoning into NLP systems, mainly because of human languages’ inherent complexities and ambiguities. Some of these challenges include:
Ambiguity in language
One significant issue is that human languages are filled with ambiguous words, phrases, and sentences. A single word or expression can have multiple meanings depending on the context in which it’s used, making it difficult for machines to interpret and respond correctly.
Implicit and explicit knowledge
Another challenge in commonsense reasoning is differentiating between implicit and explicit knowledge. Humans can naturally infer and deduce information without being explicitly told, but machines struggle to mimic this skill.
This hinders the power of AI systems to understand complex or nuanced text.
Cultural differences in commonsense knowledge
Cultural differences also challenge commonsense reasoning, as the shared knowledge and understanding of concepts can differ significantly among individuals from different cultural backgrounds. This can lead to misunderstandings and misinterpretations when AI systems try to make sense of text and language.
Approaches to Commonsense Reasoning for NLP
Several methods have been proposed to incorporate commonsense reasoning into NLP systems, including knowledge-based approaches, machine learning-based approaches, and hybrid approaches.
Knowledge-based approaches
These approaches primarily rely on using pre-built or curated databases containing commonsense knowledge.
These databases, sometimes called knowledge graphs, ontologies, or semantic networks, store relationships and facts about various concepts and entities. AI systems then use this information to reason and make inferences about language and text.
Although this approach provides a structured way of handling commonsense reasoning, it can be limited by the breadth and depth of the knowledge available in the databases.
Machine learning-based approaches
Machine learning approaches and intense learning methods like neural networks have become popular due to their ability to learn patterns and generalize from large amounts of data.
These methods can be used to understand representations of words, phrases, and sentences and to capture the context and relationships between them.
However, they often require vast amounts of labeled training data and can struggle to achieve human-like commonsense reasoning due to the limitations of the training data.
Hybrid approaches
Hybrid approaches combine elements of both knowledge-based and machine-learning methods. They integrate curated knowledge bases with learning techniques to combine the strengths of both approaches. This can lead to more effective and robust methods for incorporating commonsense reasoning into NLP systems.
Datasets for Commonsense Reasoning
Several datasets have been developed to help researchers train and test systems for commonsense reasoning in NLP applications. Some prominent datasets include:
Common Sense Explanations (CoS-E)
CoS-E is a dataset consisting of explanations for simple commonsense concepts. It provides human-generated answers for the relationships between concepts, which can be used to train and evaluate AI systems that incorporate illustrations in their reasoning.
Winograd Schema Challenge (WSC)
The Winograd Schema Challenge is a widely-referenced test to evaluate AI systems’ ability to perform commonsense reasoning. It contains multiple-choice questions, each containing a sentence with an ambiguous pronoun that requires commonsense reasoning to resolve correctly.
CommonsenseQA
CommonsenseQA is a dataset containing multiple-choice questions that require commonsense reasoning to answer. It is specifically designed for training and evaluating AI systems that aim to understand and reason about natural language text.
Evaluation Metrics for Commonsense Reasoning
Several evaluation metrics are used to gauge the effectiveness of AI systems in performing commonsense reasoning tasks. Some standard metrics include:
Accuracy
Accuracy is the proportion of correct predictions an AI system makes from the total predictions. It is a commonly-used metric for classification problems.
Precision and Recall
Precision measures the proportion of relevant results retrieved out of all retrieved results. Recall measures the proportion of relevant results retrieved out of all possible relevant results. These metrics are helpful when dealing with imbalanced datasets, where accuracy can be misleading.
F1 score
The F1 score is the harmonic mean of precision and recall, giving equal weight to both metrics. It is a widely-used metric for classification problems when precision and recall are essential.
Applications of Commonsense Reasoning in NLP
Commonsense reasoning is essential for a range of NLP applications, including:
Question-answering systems
AI systems that can answer questions through commonsense reasoning can provide users with more accurate, relevant, and useful information.
Chatbots and virtual assistants
Incorporating commonsense reasoning into chatbots and virtual assistants enables them to understand better and respond to user queries, making them more effective and user-friendly.
Sentiment analysis
Commonsense reasoning can help improve sentiment analysis by understanding implicit meanings and emotions conveyed by the language used in the text.
Machine translation
Commonsense reasoning can enhance machine translation systems by considering the context and implicit information in the original text, leading to more accurate and coherent translations.
Prompt Examples
Example 1: Crossing the Street
Prompt:
common sense reasoning for:
Jane is walking on a sidewalk and wants to cross the street to get to a store on the other side. She uses common sense reasoning to safely cross the street.
Result:
Jane is walking on a sidewalk and wants to cross the street to get to a store on the other side. She uses common sense reasoning to safely cross the street by considering her knowledge and past experiences to make the best decision.
Find a safe place to cross: Jane knows that it’s best to cross at a designated crosswalk or intersection where drivers are more likely to expect pedestrians. This reduces the risk of accidents and increases her visibility to drivers.
Observe traffic signals and signs: Jane waits for a green light or walk signal before crossing the street, as these signals indicate when it is safe for pedestrians to cross. She also pays attention to any traffic signs that may provide additional information or warnings.
Look both ways: Jane understands the importance of checking for oncoming vehicles before crossing the street. She looks left and right to ensure that no cars, bikes, or other vehicles are approaching from either direction.
Be aware of surroundings: Jane stays alert while crossing the street, continuing to watch for any potential hazards and listening for sounds of approaching vehicles. She also avoids distractions, such as using her phone or wearing headphones, which could impair her ability to detect danger.
Cross with caution: Once Jane has determined that it is safe to cross, she proceeds at a steady pace, remaining vigilant and prepared to react if any unexpected situations arise.
By applying common sense reasoning, Jane is able to make a series of informed decisions that help ensure her safety while crossing the street.
Example 2: Dressing for the Weather
Prompt:
common sense reasoning for:
John wakes up in the morning and wants to decide what to wear for the day. He uses common sense reasoning to choose an appropriate outfit based on the weather conditions.
Result:
John wakes up in the morning and wants to decide what to wear for the day. He uses common sense reasoning to choose an appropriate outfit based on the weather conditions by considering his knowledge and past experiences to make the best decision.
Check the weather: John looks outside the window to observe the current weather conditions. He can also check a weather app or listen to the weather forecast to get more detailed information about the day’s temperature, precipitation, and wind conditions.
Evaluate temperature: Based on the observed or reported temperature, John decides if he needs to dress warmly for a cold day, wear light clothing for a hot day, or choose something in between for a mild day. This helps him stay comfortable throughout the day.
Consider precipitation: If it’s raining or snowing, John knows he should wear a raincoat or waterproof jacket to stay dry. He might also wear waterproof shoes or boots and carry an umbrella to further protect himself from getting wet.
Assess wind conditions: On a windy day, John may choose to wear a windbreaker or a heavier jacket to shield himself from the wind. He might also wear a hat or a scarf to protect his head and neck from the cold breeze.
Plan for activities: John takes into account any planned activities for the day when choosing his outfit. For example, if he’s going to be outdoors for an extended period, he might dress in layers to easily adjust his clothing as needed. If he’s attending a formal event, he will wear appropriate attire that matches the dress code.
By applying common sense reasoning, John is able to make a series of informed decisions that help him choose an outfit that is both comfortable and suitable for the day’s weather conditions and activities.
Conclusion
While significant progress has been made in integrating commonsense reasoning into NLP systems, there remains room for improvement.
Future research directions in this domain include creating more extensive and diverse datasets, developing more advanced machine-learning techniques, and exploring new hybrid approaches.
By enhancing AI systems’ ability to perform commonsense reasoning, we can improve how they understand, interpret, and interact with human languages.
This will contribute to advancing AI alignment and creating more effective, user-friendly chatbots, virtual assistants, and other NLP applications.