Measuring Systematic Generalization in Neural Proof Generation with Transformers

We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge encoded in the form of natural language. We investigate systematic generalization abilities on an inductive logical reasoning task in natural language, which involves reasoning over relationships between entities grounded in first-order logical proofs. Specifically, we perform soft theorem-proving by leveraging TLMs to generate logical proofs represented in natural language. We systematically test proof generation capabilities, along with inference capabilities leveraging the generated proofs. We observe length-generalization issues in proof generation and inference when evaluated on longer-than-trained sequences. However, we observe TLMs improve their generalization performance after being exposed to longer, exhaustive proofs. In addition, we discover that TLMs are able to generalize better using backward-chaining proofs compared to their forward-chaining counterparts, while they find it easier to generate forward chaining proofs. We observe that models that are not trained to generate proofs are better at generalizing to problems based on longer proofs. This result suggests that Transformers have efficient, yet not interpretable reasoning strategies internally. These results also highlight the systematic generalization issues in TLMs in the context of logical reasoning, and we believe this work will motivate deeper inspection of their underlying reasoning strategies.Comment: NeurIPS 2020; 17 pages; 9 figures; 6 table

Ethical Challenges in Data-Driven Dialogue Systems

The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.Comment: In Submission to the AAAI/ACM conference on Artificial Intelligence, Ethics, and Societ