CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text (1908.06177v2)

Abstract: The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way. In this work, we introduce a diagnostic benchmark suite, named CLUTRR, to clarify some key issues related to the robustness and systematicity of NLU systems. Motivated by classic work on inductive logic programming, CLUTRR requires that an NLU system infer kinship relations between characters in short stories. Successful performance on this task requires both extracting relationships between entities, as well as inferring the logical rules governing these relationships. CLUTRR allows us to precisely measure a model's ability for systematic generalization by evaluating on held-out combinations of logical rules, and it allows us to evaluate a model's robustness by adding curated noise facts. Our empirical results highlight a substantial performance gap between state-of-the-art NLU models (e.g., BERT and MAC) and a graph neural network model that works directly with symbolic inputs---with the graph-based model exhibiting both stronger generalization and greater robustness.

• The paper introduces a benchmark suite to evaluate inductive reasoning and systematic generalization in NLU through inferring unstated kinship relations.
• Empirical results reveal graph-based models outperform text-based counterparts, achieving near-perfect scores on novel logical constructs.
• The study highlights the need for hybrid architectures that combine structured logic with neural language models to enhance reasoning robustness.

An Expert Review of "CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text"

The paper "CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text" addresses the ongoing challenge in natural language understanding (NLU) systems regarding their ability to generalize systematically and robustly. It introduces a diagnostic benchmark suite, CLUTRR, designed to evaluate these capabilities by focusing specifically on a model's ability to perform inductive reasoning to infer kinship relations within short textual narratives. This paper is motivated by foundational work in inductive logic programming and systematically aims to measure systematic generalization and robustness—a task widely unmet by existing NLU systems.

CLUTRR generates semi-synthetic narratives involving familial relations, presenting tasks that require a system to deduce relationships that are implied rather than stated directly. This challenges models not only to extract explicit relationships from text but also to apply underlying logical rules to infer unseen relations. The benchmark explicitly targets systematic generalization by testing models on previously unencountered combinations of logical constructs and tests robustness by incorporating controlled noise into the narratives.

Empirical evaluations were conducted using state-of-the-art NLU models, such as BERT and MAC, alongside a Graph Attention Network (GAT) model that has direct access to symbolic representations of input data. Results revealed a significant performance discrepancy: the GAT model outperformed the text-based models in terms of both generalization and robustness. This suggests that the graph-based model's structured access to data provides it with an advantage in navigating the logical complexity inherent in the task.

Specific findings of this research highlight:

• A marked performance gap in generalization between text-based models and the GAT model, with the latter achieving near-perfect scores on tasks involving unseen logical clauses of moderate complexity.
• The difficulty text-based models have in parsing and reasoning through unseen narratives, highlighting the need for mechanisms that facilitate stronger linguistic and logical generalization.
• The GAT's robustness to irrelevant and disconnected noise, but its vulnerability to structural changes involving cycles, indicating the need for enhancements in processing complex graph structures.

The operational implications of CLUTRR are noteworthy. For practitioners and researchers, it provides a rigorous benchmark explicitly tailored to test logical reasoning in NLU, thus offering a diagnostic tool to gauge, and potentially guide improvements in, machine reasoning capabilities. Theoretically, the benchmark reinforces the importance of structured reasoning in achieving robust AI systems, paving the way for novel research pathways—especially in marrying symbolic representations with neural models for comprehensive language understanding.

Looking forward, this paper suggests exciting avenues for future developments in AI. Enhancements in integrating structured reasoning within traditional NLU architectures could mitigate the current limitations identified. Moreover, this work encourages the investigation of hybrid models that effectively combine the statistical power of large pre-trained models with the systematic reasoning capabilities inherent in symbolic logic processing.

In conclusion, CLUTRR serves as a compelling resource for probing the logical reasoning capabilities of language understanding models. With the provided benchmark, the authors have set the stage for advancements towards AI systems that not only understand language superficially but also reason with the depth and precision akin to human cognition. This work is an essential contribution, bringing systematic logical reasoning to the forefront of AI development.