Roee Aharoni

Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability. Inspired by recent work on evaluating factual consistency in …

TRUE: Re-evaluating Factual Consistency Evaluation

2022 · arXiv (Cornell University)

Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability. Automatic factual consistency evaluation may help alleviate this limitation by accelerating evaluation cycles, filtering inconsistent outputs and augmenting training data. …

Massively Multilingual Neural Machine Translation

2019

Roee Aharoni, Melvin Johnson, Orhan Firat. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.

The Missing Ingredient in Zero-Shot Neural Machine Translation

2019 · arXiv (Cornell University)

Multilingual Neural Machine Translation (NMT) models are capable of translating between multiple source and target languages. Despite various approaches to train such models, they have difficulty with zero-shot translation: translating between language pairs that were …