Shuly Wintner — accepted AI-conference papers

2022

Speaker Information Can Guide Models to Better Inductive Biases: A Case Study On Predicting Code-Switching

Alissa Ostapenko, Shuly Wintner, Melinda Fricke, Yulia Tsvetkov

ACL 2022long

Natural language processing (NLP) models trained on people-generated data can be unreliable because, without any constraints, they can learn from spurious correlations that are not relevant to the task. We hypothesize that enriching models with speaker information in a controlled, educated way can g…

Cited by 9 Source ↗PDF ↗code ↗Scholar ↗

2021

Machine Translation into Low-resource Language Varieties

Sachin Kumar, Antonios Anastasopoulos, Shuly Wintner, Yulia Tsvetkov

ACL 2021short

State-of-the-art machine translation (MT) systems are typically trained to generate “standard” target language; however, many languages have multiple varieties (regional varieties, dialects, sociolects, non-native varieties) that are different from the standard language. Such varieties are often low…

Cited by 39 Source ↗PDF ↗code ↗Scholar ↗