Danilo Neves Ribeiro


pdf bib
Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels
Danilo Neves Ribeiro | Jack Goetz | Omid Abdar | Mike Ross | Annie Dong | Kenneth Forbus | Ahmed Mohamed
Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning

Frame semantic parsing is an important component of task-oriented dialogue systems. Current models rely on a significant amount training data to successfully identify the intent and slots in the user’s input utterance. This creates a significant barrier for adding new domains to virtual assistant capabilities, as creation of this data requires highly specialized NLP expertise. In this work we propose OpenFSP, a framework that allows for easy creation of new domains from a handful of simple labels that can be generated without specific NLP knowledge. Our approach relies on creating a small, but expressive, set of domain agnostic slot types that enables easy annotation of new domains. Given such annotation, a matching algorithm relying on sentence encoders predicts the intent and slots for domains defined by end-users. Experiments on the TopV2 dataset shows that our model trained on these simple labels have strong performance against supervised baselines.

pdf bib
Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)
Bhavana Dalvi Mishra | Greg Durrett | Peter Jansen | Danilo Neves Ribeiro | Jason Wei
Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)


pdf bib
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Danilo Neves Ribeiro | Shen Wang | Xiaofei Ma | Rui Dong | Xiaokai Wei | Henghui Zhu | Xinchi Chen | Peng Xu | Zhiheng Huang | Andrew Arnold | Dan Roth
Findings of the Association for Computational Linguistics: NAACL 2022

Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations, called entailment trees, were recently suggested as a way to explain the reasoning behind a QA system’s answer. In order to better generate such entailment trees, we propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR). Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises. The IRGR model iteratively searches for suitable premises, constructing a single entailment step at a time. Contrary to previous approaches, our method combines generation steps and retrieval of premises, allowing the model to leverage intermediate conclusions, and mitigating the input size limit of baseline encoder-decoder models. We conduct experiments using the EntailmentBank dataset, where we outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300% gain in overall correctness.