Papers by Philip Arthur
It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data (2021.emnlp-main)
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| Challenge: | Existing siMT systems are trained and evaluated on offline translations . however, evaluation gap remains notable, calling for constructing large-scale interpretation corpora . |
| Approach: | They propose a translation-to-interpretation transfer method which converts offline translations into interpretation-style data. |
| Outcome: | The proposed interpretation test set shows that SiMT models improve on translation vs interpretation data. |
Multilingual Simultaneous Neural Machine Translation (2021.findings-acl)
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| Challenge: | Simultaneous machine translation (SIMT) involves translating source utterances to the target language in real-time before the speaker utterrance completes. |
| Approach: | They propose a multilingual approach to simultaneous machine translation where a single model simultaneously translates between multiple languages. |
| Outcome: | The proposed multilingual approach improves on two Germanic and three Romance languages and is on-par or better than the universal model trained for all languages. |
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning (2021.eacl-main)
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| Challenge: | Existing approaches to learn simultaneous translation model with coupled programmer-interpreter policies are suboptimal as they fix the agent's policy to focus learning the NMT model or learn adaptive agent policies while the NRT model is fixed. |
| Approach: | They propose an algorithmic oracle to produce oracular READ/WRITE actions for training bilingual sentence-pairs using the notion of word alignments. |
| Outcome: | The proposed method outperforms baselines in terms of translation quality quality while keeping the delay low. |
An analysis of language models for metaphor recognition (2020.coling-main)
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| Challenge: | Metaphor recognition systems that are based on language models perform substantially worse on unconventional metaphors than on conventional ones. |
| Approach: | They conduct a linguistic analysis of recent metaphor recognition systems based on language models and a variant of BERT language models to examine their performance. |
| Outcome: | The proposed systems show that they can recognise unseen words if synonyms or morphological variations have been seen before, leading to enhanced generalisation beyond word sense disambiguation. |
Mastering the Craft of Data Synthesis for CodeLLMs (2025.naacl-long)
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Meng Chen, Philip Arthur, Qianyu Feng, Cong Duy Vu Hoang, Yu-Heng Hong, Mahdi Kazemi Moghaddam, Omid Nezami, Duc Thien Nguyen, Gioacchino Tangari, Duy Vu, Thanh Vu, Mark Johnson, Krishnaram Kenthapadi, Don Dharmasiri, Long Duong, Yuan-Fang Li
| Challenge: | Large language models (LLMs) have shown impressive performance in code understanding and generation. |
| Approach: | They propose a systematic review of large language models and their taxonomy and propose specialized LLMs for code-related tasks. |
| Outcome: | The proposed models have shown to be highly effective in coding tasks. |