Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications, but lack domain-specific knowledge that does not naturally occur in pre-training data.Previous studies augmented PLMs with symbolic knowledge for different downstream NLP tasks.However, knowledge bases (KBs) utilized in these studies are usually large-scale and static, in contrast to small, domain-specific, and modifiable knowledge bases that are prominent in real-world task-oriented dialogue (TOD) systems.In this paper, we showcase the advantages of injecting domain-specific knowledge prior to fine-tuning on TOD tasks.To this end, we utilize light-weight adapters that can be easily integrated with PLMs and serve as a repository for facts learned from different KBs.To measure the efficacy of proposed knowledge injection methods, we introduce Knowledge Probing using Response Selection (KPRS) -a probe designed specifically for TOD models.Experiments 1 on KPRS and the response generation task show improvements of knowledge injection with adapters over strong baselines. * Work performed while at AWS AI Labs 1 https://github.com/amazon-research/ domain-knowledge-injection

Niccolò Campolungo, Tommaso Pasini, Denis Emelin, Roberto Navigli. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

In social settings, much of human behavior is governed by unspoken rules of conduct rooted in societal norms.For artificial systems to be fully integrated into social environments, adherence to such norms is a central prerequisite.To investigate whether language generation models can serve as behavioral priors for systems deployed in social settings, we evaluate their ability to generate action descriptions that achieve predefined goals under normative constraints.Moreover, we examine if models can anticipate likely consequences of actions that either observe or violate known norms, or explain why certain actions are preferable by generating relevant norm hypotheses.For this purpose, we introduce Moral Stories, a crowd-sourced dataset of structured, branching narratives for the study of grounded, goaloriented social reasoning.Finally, we propose decoding strategies that combine multiple expert models to significantly improve the quality of generated actions, consequences, and norms compared to strong baselines.1

Winograd schemas are a well-established tool for evaluating coreference resolution (CoR) and commonsense reasoning (CSR) capabilities of computational models.So far, schemas remained largely confined to English, limiting their utility in multilingual settings.This work presents Wino-X, a parallel dataset of German, French, and Russian schemas, aligned with their English counterparts.We use this resource to investigate whether neural machine translation (NMT) models can perform CoR that requires commonsense knowledge and whether multilingual language models (MLLMs) are capable of CSR across multiple languages.Our findings show Wino-X to be exceptionally challenging for NMT systems that are prone to undesireable biases and unable to detect disambiguating information.We quantify biases using established statistical methods and define ways to address both of these issues.We furthermore present evidence of active cross-lingual knowledge transfer in MLLMs, whereby fine-tuning models on English schemas yields CSR improvements in other languages.1

Word sense disambiguation is a well-known source of translation errors in NMT.We posit that some of the incorrect disambiguation choices are due to models' over-reliance on dataset artifacts found in training data, specifically superficial word co-occurrences, rather than a deeper understanding of the source text.We introduce a method for the prediction of disambiguation errors based on statistical data properties, demonstrating its effectiveness across several domains and model types.Moreover, we develop a simple adversarial attack strategy that minimally perturbs sentences in order to elicit disambiguation errors to further probe the robustness of translation models.Our findings indicate that disambiguation robustness varies substantially between domains and that different models trained on the same data are vulnerable to different attacks. 1