Search Results for author: George Foster

Found 37 papers, 6 papers with code

A Natural Diet: Towards Improving Naturalness of Machine Translation Output

no code implementations Findings (ACL) 2022 Markus Freitag, David Vilar, David Grangier, Colin Cherry, George Foster

In this work we propose a method for training MT systems to achieve a more natural style, i. e. mirroring the style of text originally written in the target language.

Diversity Machine Translation +2

Bilingual Methods for Adaptive Training Data Selection for Machine Translation

no code implementations AMTA 2016 Boxing Chen, Roland Kuhn, George Foster, Colin Cherry, Fei Huang

In this paper, we propose a new data selection method which uses semi-supervised convolutional neural networks based on bitokens (Bi-SSCNNs) for training machine translation systems from a large bilingual corpus.

Machine Translation NMT +2

Finding Replicable Human Evaluations via Stable Ranking Probability

no code implementations1 Apr 2024 Parker Riley, Daniel Deutsch, George Foster, Viresh Ratnakar, Ali Dabirmoghaddam, Markus Freitag

Reliable human evaluation is critical to the development of successful natural language generation models, but achieving it is notoriously difficult.

Machine Translation Text Generation

Importance-Aware Data Augmentation for Document-Level Neural Machine Translation

no code implementations27 Jan 2024 Minghao Wu, YuFei Wang, George Foster, Lizhen Qu, Gholamreza Haffari

Document-level neural machine translation (DocNMT) aims to generate translations that are both coherent and cohesive, in contrast to its sentence-level counterpart.

Data Augmentation Machine Translation +2

To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation

no code implementations2 Jan 2024 Jiaming Luo, Colin Cherry, George Foster

We conduct a large-scale fine-grained comparative analysis of machine translations (MT) against human translations (HT) through the lens of morphosyntactic divergence.

Attribute Diversity +2

Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibration

1 code implementation23 May 2023 Daniel Deutsch, George Foster, Markus Freitag

Kendall's tau is frequently used to meta-evaluate how well machine translation (MT) evaluation metrics score individual translations.

Machine Translation

Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability

no code implementations17 May 2023 Eleftheria Briakou, Colin Cherry, George Foster

We investigate the role of incidental bilingualism -- the unintentional consumption of bilingual signals, including translation examples -- in explaining the translation capabilities of large language models, taking the Pathways Language Model (PaLM) as a case study.

Language Modelling Machine Translation +1

Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation

no code implementations16 Feb 2023 Minghao Wu, George Foster, Lizhen Qu, Gholamreza Haffari

Existing work in document-level neural machine translation commonly concatenates several consecutive sentences as a pseudo-document, and then learns inter-sentential dependencies.

Machine Translation Translation

The unreasonable effectiveness of few-shot learning for machine translation

no code implementations2 Feb 2023 Xavier Garcia, Yamini Bansal, Colin Cherry, George Foster, Maxim Krikun, Fangxiaoyu Feng, Melvin Johnson, Orhan Firat

We demonstrate the potential of few-shot translation systems, trained with unpaired language data, for both high and low-resource language pairs.

Decoder Few-Shot Learning +3

Prompting PaLM for Translation: Assessing Strategies and Performance

no code implementations16 Nov 2022 David Vilar, Markus Freitag, Colin Cherry, Jiaming Luo, Viresh Ratnakar, George Foster

Large language models (LLMs) that have been trained on multilingual but not parallel text exhibit a remarkable ability to translate between languages.

Language Modelling Machine Translation +1

Toward More Effective Human Evaluation for Machine Translation

no code implementations HumEval (ACL) 2022 Belén Saldías, George Foster, Markus Freitag, Qijun Tan

Improvements in text generation technologies such as machine translation have necessitated more costly and time-consuming human evaluation procedures to ensure an accurate signal.

Machine Translation Text Generation +1

Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation

3 code implementations29 Apr 2021 Markus Freitag, George Foster, David Grangier, Viresh Ratnakar, Qijun Tan, Wolfgang Macherey

Human evaluation of modern high-quality machine translation systems is a difficult problem, and there is increasing evidence that inadequate evaluation procedures can lead to erroneous conclusions.

Machine Translation Translation

Assessing Reference-Free Peer Evaluation for Machine Translation

no code implementations NAACL 2021 Sweta Agrawal, George Foster, Markus Freitag, Colin Cherry

Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains.

Machine Translation Translation

Human-Paraphrased References Improve Neural Machine Translation

1 code implementation WMT (EMNLP) 2020 Markus Freitag, George Foster, David Grangier, Colin Cherry

When used in place of original references, the paraphrased versions produce metric scores that correlate better with human judgment.

Machine Translation NMT +1

Inference Strategies for Machine Translation with Conditional Masking

no code implementations EMNLP 2020 Julia Kreutzer, George Foster, Colin Cherry

Conditional masked language model (CMLM) training has proven successful for non-autoregressive and semi-autoregressive sequence generation tasks, such as machine translation.

Language Modelling Machine Translation +1

Re-translation versus Streaming for Simultaneous Translation

no code implementations WS 2020 Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, George Foster

There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes available.

Attribute Data Augmentation +2

Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation

1 code implementation6 Dec 2019 Naveen Arivazhagan, Colin Cherry, Te I, Wolfgang Macherey, Pallavi Baljekar, George Foster

As this scenario allows for revisions to our incremental translations, we adopt a re-translation approach to simultaneous translation, where the source is repeatedly translated from scratch as it grows.

Machine Translation speech-recognition +2

Thinking Slow about Latency Evaluation for Simultaneous Machine Translation

no code implementations31 May 2019 Colin Cherry, George Foster

Simultaneous machine translation attempts to translate a source sentence before it is finished being spoken, with applications to translation of spoken language for live streaming and conversation.

Machine Translation Sentence +1

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

2 code implementations21 Feb 2019 Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia X. Chen, Ye Jia, Anjuli Kannan, Tara Sainath, Yuan Cao, Chung-Cheng Chiu, Yanzhang He, Jan Chorowski, Smit Hinsu, Stella Laurenzo, James Qin, Orhan Firat, Wolfgang Macherey, Suyog Gupta, Ankur Bapna, Shuyuan Zhang, Ruoming Pang, Ron J. Weiss, Rohit Prabhavalkar, Qiao Liang, Benoit Jacob, Bowen Liang, HyoukJoong Lee, Ciprian Chelba, Sébastien Jean, Bo Li, Melvin Johnson, Rohan Anil, Rajat Tibrewal, Xiaobing Liu, Akiko Eriguchi, Navdeep Jaitly, Naveen Ari, Colin Cherry, Parisa Haghani, Otavio Good, Youlong Cheng, Raziel Alvarez, Isaac Caswell, Wei-Ning Hsu, Zongheng Yang, Kuan-Chieh Wang, Ekaterina Gonina, Katrin Tomanek, Ben Vanik, Zelin Wu, Llion Jones, Mike Schuster, Yanping Huang, Dehao Chen, Kazuki Irie, George Foster, John Richardson, Klaus Macherey, Antoine Bruguier, Heiga Zen, Colin Raffel, Shankar Kumar, Kanishka Rao, David Rybach, Matthew Murray, Vijayaditya Peddinti, Maxim Krikun, Michiel A. U. Bacchiani, Thomas B. Jablin, Rob Suderman, Ian Williams, Benjamin Lee, Deepti Bhatia, Justin Carlson, Semih Yavuz, Yu Zhang, Ian McGraw, Max Galkin, Qi Ge, Golan Pundak, Chad Whipkey, Todd Wang, Uri Alon, Dmitry Lepikhin, Ye Tian, Sara Sabour, William Chan, Shubham Toshniwal, Baohua Liao, Michael Nirschl, Pat Rondon

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.

Sequence-To-Sequence Speech Recognition

Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue

no code implementations31 Jan 2019 Kory W. Mathewson, Pablo Samuel Castro, Colin Cherry, George Foster, Marc G. Bellemare

We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives.


Revisiting Character-Based Neural Machine Translation with Capacity and Compression

no code implementations EMNLP 2018 Colin Cherry, George Foster, Ankur Bapna, Orhan Firat, Wolfgang Macherey

Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and improve results by eliminating hyper-parameters and manual feature engineering.

Feature Engineering Machine Translation +2

Cost Weighting for Neural Machine Translation Domain Adaptation

no code implementations WS 2017 Boxing Chen, Colin Cherry, George Foster, Samuel Larkin

We compare cost weighting to two traditional domain adaptation techniques developed for statistical machine translation: data selection and sub-corpus weighting.

Domain Adaptation Machine Translation +1

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