Anthony Aue


2023

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TRIP: Accelerating Document-level Multilingual Pre-training via Triangular Document-level Pre-training on Parallel Data Triplets
Hongyuan Lu | Haoyang Huang | Shuming Ma | Dongdong Zhang | Wai Lam | Zhaochuan Gao | Anthony Aue | Arul Menezes | Furu Wei
Findings of the Association for Computational Linguistics: EMNLP 2023

Despite the success of multilingual sequence-to-sequence pre-training, most existing approaches rely on document-level monolingual corpora in many different languages, sentence-level bilingual corpora, and sometimes synthetic document-level bilingual corpora. This hampers the performance with cross-lingual document-level tasks such as document-level translation. Hence, we propose to mine and leverage document-level trilingual parallel corpora to improve sequence-to-sequence multilingual pre-training. We present Triangular Document-level Pre-training (TRIP) as the first in the field to accelerate the conventional monolingual and bilingual objectives into a trilingual objective with a novel method called Grafting. Experiments show that TRIP achieves several strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark, including consistent improvements by up to 3.11 d-BLEU points and 8.9 ROUGE-L points.

2018

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Marian: Cost-effective High-Quality Neural Machine Translation in C++
Marcin Junczys-Dowmunt | Kenneth Heafield | Hieu Hoang | Roman Grundkiewicz | Anthony Aue
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

This paper describes the submissions of the “Marian” team to the WNMT 2018 shared task. We investigate combinations of teacher-student training, low-precision matrix products, auto-tuning and other methods to optimize the Transformer model on GPU and CPU. By further integrating these methods with the new averaging attention networks, a recently introduced faster Transformer variant, we create a number of high-quality, high-performance models on the GPU and CPU, dominating the Pareto frontier for this shared task.

2013

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MSR-FBK IWSLT 2013 SLT system description
Anthony Aue | Qin Gao | Hany Hassan | Xiaodong He | Gang Li | Nicholas Ruiz | Frank Seide
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the systems used for the MSR+FBK submission for the SLT track of IWSLT 2013. Starting from a baseline system we made a series of iterative and additive improvements, including a novel method for processing bilingual data used to train MT systems for use on ASR output. Our primary submission is a system combination of five individual systems, combining the output of multiple ASR engines with multiple MT techniques. There are two contrastive submissions to help place the combined system in context. We describe the systems used and present results on the test sets.

2006

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Proceedings of the Workshop on Sentiment and Subjectivity in Text
Michael Gamon | Anthony Aue
Proceedings of the Workshop on Sentiment and Subjectivity in Text

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Dependency Parsing with Reference to Slovene, Spanish and Swedish
Simon Corston-Oliver | Anthony Aue
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)

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Multilingual Dependency Parsing using Bayes Point Machines
Simon Corston-Oliver | Anthony Aue | Kevin Duh | Eric Ringger
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

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Detecting Inter-domain Semantic Shift using Syntactic Similarity
Masaki Itagaki | Anthony Aue | Takako Aikawa
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This poster is a preliminary report of our experiments for detecting semantically shifted terms between different domains for the purposes of new concept extraction. A given term in one domain may represent a different concept in another domain. In our approach, we quantify the degree of similarity of words between different domains by measuring the degree of overlap in their domain-specific semantic spaces. The domain-specific semantic spaces are defined by extracting families of syntactically similar words, i.e. words that occur in the same syntactic context. Our method does not rely on any external resources other than a syntactic parser. Yet it has the potential to extract semantically shifted terms between two different domains automatically while paying close attention to contextual information. The organization of the poster is as follows: Section 1 provides our motivation. Section 2 provides an overview of our NLP technology and explains how we extract syntactically similar words. Section 3 describes the design of our experiments and our method. Section 4 provides our observations and preliminary results. Section 5 presents some work to be done in the future and concluding remarks.

2005

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Sentence-level MT evaluation without reference translations: beyond language modeling
Michael Gamon | Anthony Aue | Martine Smets
Proceedings of the 10th EAMT Conference: Practical applications of machine translation

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Automatic Identification of Sentiment Vocabulary: Exploiting Low Association with Known Sentiment Terms
Michael Gamon | Anthony Aue
Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing

2004

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Statistical machine translation using labeled semantic dependency graphs
Anthony Aue | Arul Menezes | Bob Moore | Chris Quirk | Eric Ringger
Proceedings of the 10th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

2002

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English-Japanese Example-Based Machine Translation Using Abstract Linguistic Representations
Chris Brockett | Takako Aikawa | Anthony Aue | Arul Menezes | Chris Quirk | Hisami Suzuki
COLING-02: Machine Translation in Asia