Rachel Bawden

Survey of Low-Resource Machine Translation

2021 · arXiv (Cornell University)

We present a survey covering the state of the art in low-resource machine translation research. There are currently around 7000 languages spoken in the world and almost all language pairs lack significant resources for training …

Exploring Inline Lexicon Injection for Cross-Domain Transfer in Neural Machine Translation

2024 · HAL (Le Centre pour la Communication Scientifique Directe)

International audience

Self-Retrieval from Distant Contexts for Document-Level Machine Translation

2025

International audience

Explicit Learning and the LLM in Machine Translation

2025

This study explores an LLM's ability to learn new languages using explanations found in a grammar book-a process we term "explicit learning."To rigorously assess this ability, we design controlled translation experiments between English and constructed …

Evaluating Discourse Phenomena in Neural Machine Translation

2018

Rachel Bawden, Rico Sennrich, Alexandra Birch, Barry Haddow. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Multitask Prompted Training Enables Zero-Shot Task Generalization

2021 · arXiv (Cornell University)

Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning …

Evaluating Discourse Phenomena in Neural Machine Translation

2017 · Zurich Open Repository and Archive (University of Zurich)

For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with …

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

2022 · arXiv (Cornell University)

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed …