Papers by Colin Cherry
Assessing Reference-Free Peer Evaluation for Machine Translation (2021.naacl-main)
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| Challenge: | Existing methods to evaluate machine translation output are based on comparing MT output to one or more reference translations. |
| Approach: | They propose to use probabilities given by a large, multilingual model as a reference-free metric. |
| Outcome: | The proposed model is robust and likely to offer reasonable performance across a broad spectrum of domains and different system qualities. |
Simultaneous Translation (2020.emnlp-tutorials)
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| Challenge: | Simultaneous translation is a problem that has long been considered one of the hardest problems in AI . this tutorial will provide a deep understanding of the history and the recent advances in simultaneous translation. |
| Approach: | This tutorial will examine the design and evaluation of policies for simultaneous translation . it will provide an overview of the history and recent advances in simultaneous translation. |
| Outcome: | This tutorial will examine the design and evaluation of policies for simultaneous translation . |
Inference Strategies for Machine Translation with Conditional Masking (2020.emnlp-main)
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| Challenge: | Conditional masked language model training has proven successful for non-autoregressive and semi-auto-regressively sequence generation tasks. |
| Approach: | They propose a conditional masked language model (CMLM) that is a factorization of conditional probabilities of partial sequences and propose heuristics to improve performance. |
| Outcome: | The proposed algorithm is more efficient than the standard “mask-predict” algorithm on machine translation tasks. |
Alligators All Around: Mitigating Lexical Confusion in Low-resource Machine Translation (2025.naacl-short)
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| Challenge: | Current machine translation systems for low-resource languages have a particular failure mode: they tend to confuse words within a domain. |
| Approach: | They propose a recall-based metric to measure the failure mode of machine translation systems for low-resource languages. |
| Outcome: | The proposed model outperforms a lexicon-based translator in 122 low-resource languages. |
A Natural Diet: Towards Improving Naturalness of Machine Translation Output (2022.findings-acl)
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| Challenge: | MT evaluation often focuses on accuracy and fluency without paying much attention to translation style. |
| Approach: | They propose a method for training machine translation systems to achieve a more natural style by contrasting training data according to the naturalness of the target side. |
| Outcome: | The proposed method achieves lexical richness on par with human translations, and is preferred by human experts when compared to baseline translations. |
SSA-COMET: Do LLMs Outperform Learned Metrics in Evaluating MT for Under-Resourced African Languages? (2025.emnlp-main)
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Senyu Li, Jiayi Wang, Felermino D. M. A. Ali, Colin Cherry, Daniel Deutsch, Eleftheria Briakou, Rui Sousa-Silva, Henrique Lopes Cardoso, Pontus Stenetorp, David Ifeoluwa Adelani
| Challenge: | Existing metrics for machine translation quality for under-resourced African languages suffer from limited language coverage and poor performance in low-resource settings. |
| Approach: | They propose a large-scale human-annotated machine translation evaluation dataset . they use a reference-based and reference-free evaluation model to compare MT quality . |
| Outcome: | The proposed models outperform AfriCOMET and the strongest LLM on low-resource languages. |
Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM’s Translation Capability (2023.acl-long)
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| Challenge: | Large multilingual language models exhibit impressive zero- or few-shot machine translation capabilities, despite never having been explicitly and intentionally exposed to translation data. |
| Approach: | They propose a mixed-method approach to measure and understand incidental bilingualism at scale using the Pathways Language Model. |
| Outcome: | The proposed model is exposed to over 30 million translation pairs across at least 44 languages. |
Prompting PaLM for Translation: Assessing Strategies and Performance (2023.acl-long)
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| Challenge: | Large language models trained on multilingual but not parallel text exhibit remarkable ability to translate between languages. |
| Approach: | They investigate the pathways language model which has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
| Outcome: | The pathways language model (PaLM) has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages (2023.findings-emnlp)
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Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac Caswell, Colin Cherry, Dan Garrette, Reeve Ingle, Melvin Johnson, Dmitry Panteleev, Partha Talukdar
| Challenge: | Existing datasets are often informed by established research directions in the NLP community. |
| Approach: | They propose a benchmark to evaluate the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
| Outcome: | The proposed benchmark evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
Revisiting Character-Based Neural Machine Translation with Capacity and Compression (D18-1)
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| Challenge: | Translating characters instead of words or word-fragments can simplify the processing pipeline but results in longer sequences . |
| Approach: | They propose to use sequence-to-sequence architectures of sufficient depth to solve the problem . they also evaluate the performance versus computation time tradeoffs they offer . |
| Outcome: | The proposed models outperform models operating over word fragments in character-level NMT, the authors show . they also show that the proposed models do not match the performance of their deep character baseline model . |
Barriers to Effective Evaluation of Simultaneous Interpretation (2024.findings-eacl)
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| Challenge: | Existing studies have relied on out-of-the-box machine translation metrics to evaluate interpretation data, but they do not account for human judgments of interpretation quality. |
| Approach: | They propose to use machine translation metrics to evaluate human interpretations to address potential barriers to disfluency, summarization, paraphrasing and segmentation. |
| Outcome: | The proposed model achieves better correlation with human judgments than state-of-the-art metrics. |
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation (N19-1)
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| Challenge: | a heterogeneous training dataset can vary in characteristics such as domain, translation quality, and degree of difficulty. |
| Approach: | They propose to use reinforcement learning to learn an optimal curriculum for NMT training . they find it can beat uniform baselines and hand-designed, state-of-the-art curricula . |
| Outcome: | The proposed approach beats baselines and hand-designed curricula on English-to-French datasets. |
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation (P19-1)
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Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, Colin Raffel
| Challenge: | Simultaneous machine translation begins to translate each source sentence before the source speaker has finished speaking, with applications to live and streaming scenarios. |
| Approach: | They propose a simultaneous translation system that learns an adaptive schedule with a neural machine translation model that attends over all source tokens read thus far. |
| Outcome: | The proposed system can achieve latency-quality trade-offs favorable to a proposed wait-k strategy for many latency values. |