Addressing Ambiguity in Semantic Parsing

Zero and Few-shot Semantic Parsing with Ambiguous Inputs

#Introduction

Natural language is often unclear and can have multiple meanings, yet this characteristic is usually overlooked in tasks related to understanding language, which often assume that a sentence has only one right interpretation. This leads to challenges when trying to interpret ambiguous statements accurately. We aim to address this by presenting a new framework and dataset designed specifically to handle ambiguity in semantic parsing. Our work explores how different systems for understanding language respond to ambiguous inputs, introducing new ways to measure their effectiveness.

#Ambiguity in Language

Ambiguity is a common phenomenon in human communication. When people speak or write, they may use words or phrases that can lead to different interpretations. This feature is often a result of speakers trying to convey their messages efficiently. Instead of providing a long explanation for every idea, speakers often rely on listeners to interpret what they mean based on context and common knowledge. For example, when someone says, “I saw the man with the telescope,” it may not be clear whether they mean they used a telescope to see the man or that the man had a telescope.

In everyday interactions, people can use their knowledge of the world and the situation to clarify these Ambiguities. However, language models, or systems designed to understand and interpret language, lack the experiences and contextual awareness that humans possess. This creates difficulty as these systems may misinterpret ambiguous statements.

#Semantic Parsing

Semantic parsing involves converting natural language into a formal representation that captures its meaning. This process is crucial for enabling machines to understand and act upon human language. However, most current methods are designed with the assumption that each input has a single, correct output. This oversight does not reflect the reality of how language is used, where many phrases can have several valid meanings.

To address this gap, we have created a new framework that includes datasets focused on five well-known types of linguistic ambiguity. By testing different parsing systems with this new framework, we can analyze how well they manage ambiguity in language.

#The Need for New Metrics

To fully evaluate how language models deal with ambiguity, we introduce three new metrics. Through our research, we find that larger pre-trained language models often struggle to understand the range of possible meanings unless they receive specific instructions. However, when these models encounter examples that demonstrate ambiguity, they perform better at interpreting the input.

Our findings highlight the importance of considering ambiguity in semantic parsing tasks. Including it in the evaluation process can provide a more accurate picture of how well a model can handle the multiple interpretations often found in everyday language.

#Framework Overview

Our framework for studying ambiguity in semantic parsing consists of templates that can create both ambiguous and clear sentences. For each type of ambiguity, we can generate many examples. Each ambiguous sentence is linked to two different possible meanings, allowing us to test how well different models can recognize and interpret the confusion present in the language.

The ambiguity types we focus on include:

1. Prepositional Phrase Attachment
2. Scope and Inverse Scope
3. Pronominal Coreference
4. Conjunctions

These types have been selected because they are widely recognized and documented in linguistic research.

#Testing Models with Ambiguity

Using our framework, we can evaluate several language models by asking them to interpret ambiguous sentences. Our approach involves two main testing scenarios: zero-shot and mixed prompt settings.

In the zero-shot setting, models are given the necessary instructions to produce both interpretations of an ambiguous statement, but they do not see any examples of that ambiguity. The challenge for the model here is to creatively combine various structures to create valid interpretations.

In the mixed prompt setting, we provide the models with a combination of examples that support conflicting interpretations. This is designed to mimic real-world situations where the training data might not be clear-cut or might contain multiple interpretations.

#Results and Analysis

#Zero-shot Parsing Results

When we evaluate the models in the zero-shot setting, we discover that smaller models often struggle to accurately interpret ambiguous sentences. Larger models show some ability to predict at least one of the interpretations, but it is rare for them to successfully capture both meanings. For instance, while a model like GPT-3.5 performs relatively well, smaller models might struggle to predict the meanings correctly.

#Mixed Prompt Results

In the mixed prompt setting, we observe that models perform better at recognizing the ambiguity when there is a direct example paired with conflicting evidence in the prompt. For several types of ambiguity, models appear to successfully capture the intended meanings by reflecting the distribution provided in the input prompts.

#Implications of Findings

These results suggest that language models are better equipped to handle ambiguity when examples exist in the input data. This raises an important point: if ambiguity is included in the training data, models can learn to respond to it more effectively. Therefore, by explicitly including ambiguity in training and evaluation processes, we can improve the performance of models on real-world tasks.

#Limitations

While our findings are promising, they also highlight some limitations in current models. Many still have difficulty handling ambiguous inputs without clear examples. Additionally, being trained primarily on data that assigns one interpretation may hinder a model's ability to handle multiple valid meanings.

The research indicates a need for a broader approach to annotation and evaluation in natural language processing. Instead of strict single-label outputs, future work could benefit from multiple interpretations and considerations of ambiguity.

#Conclusion

Our study emphasizes the importance of recognizing and addressing ambiguity in natural language. By introducing a new framework for semantic parsing that considers multiple interpretations, we hope to improve the performance of language systems in real-world situations. The findings suggest that when ambiguity is recognized and included in training data, models can learn to manage it more effectively. As we continue to advance research in this area, our aim is to develop better tools and methods for understanding the complexity of human language.