Papers by Ke Tran
The Importance of Being Recurrent for Modeling Hierarchical Structure (D18-1)
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| Challenge: | Recent work shows that recurrent neural networks can implicitly capture hierarchical information when trained to solve common natural language processing tasks. |
| Approach: | They propose a convolutional sequence-to-sequence model that exploits hierarchical information implicitly. |
| Outcome: | The proposed model is recurrent and non-recurrent, and it can model hierarchical structure implicitly. |
A Hybrid Approach to Cross-lingual Product Review Summarization (2022.emnlp-industry)
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| Challenge: | Existing methods for summarizing product reviews with thousands of reviews are inefficient and time consuming. |
| Approach: | They propose an unsupervised extractive step and a supervised abstractive step to generate a short summary in any language. |
| Outcome: | The proposed model is as good as human written summaries in coherence, informativeness, non-redundancy, and fluency as human summary summators. |
Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations (D19-61)
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| Challenge: | Pretrained sentence representations have set the new state of the art in many language understanding tasks. |
| Approach: | They propose to use a multilingual corpus to train deep bidirectional sentence representations that are fully lexicalized to allow for the development of an unsupervised universal dependency parser. |
| Outcome: | The proposed approach outperforms the best CoNLL 2018 systems in all of the shared task’s six truly low-resource languages while using a single system. |
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation (2022.naacl-main)
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| Challenge: | Neural Machine Translation models can be optimized to improve latency by constraining the set of output words . lexical shortlisting fails to select the right set of input words for semantically non-compositional phenomena such as idiomatic expressions. |
| Approach: | They propose a model of vocabulary selection that constrains the set of allowed output words . they propose to increase the size of the allowed set to restore translation quality . |
| Outcome: | The proposed model restores translation quality of an unconstrained system, as measured by human evaluations on WMT newstest2020 and idiomatic expressions, at an inference latency competitive with alignment-based selection using aggressive thresholds. |
Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation (2021.emnlp-main)
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| Challenge: | Building neural machine translation systems to perform well on a specific target domain remains a challenge. |
| Approach: | They propose to train a single NMT system per language pair that performs well across multiple domains. |
| Outcome: | The proposed approach improves the Pareto frontier on this task. |
The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities (2024.acl-long)
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| Challenge: | Recent studies have shown that fine-tuning large language models improves their translations, but it is unclear what is the impact on desirable LLM behaviors that are not present in neural machine translation models. |
| Approach: | They perform an extensive translation evaluation on LLaMA and Falcon models with model size ranging from 7 billion up to 65 billion parameters. |
| Outcome: | The proposed model produces less literal translations after fine-tuning on parallel data. |
What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation (2026.acl-long)
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Shaomu Tan, Dawei Zhu, Ke Tran, Michael Denkowski, Sony Trenous, Leonardo F. R. Ribeiro, Bill Byrne, Felix Hieber
| Challenge: | Large language models (LLMs) have made document-level machine translation increasingly practical, enabled by long-context modeling and strong generation quality. |
| Approach: | They propose to use document-level MT followed by segment-level refinement to find the strongest and most stable improvements across six LLMs and seven language pairs. |
| Outcome: | The proposed method outperforms error-specific prompting and evaluate-then-refine schemes in document-level translation. |