Language models and reranking for machine translation

Olteanu, Marian; Suriyentrakorn, Pasin; Moldovan, Dan

doi:10.3115/1654650.1654674

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…In the NLP field, LMs are widely used to estimate the acceptability of text (Olteanu et al, 2006;Kann et al, 2018). An overview of the LM-based method is shown in Figure 1.…”

Section: Overview Of the Lm-based Methodsmentioning

confidence: 99%

Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese

Kuribayashi

Ito²,

Suzuki

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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We examine a methodology using neural language models (LMs) for analyzing the word order of language. This LM-based method has the potential to overcome the difficulties existing methods face, such as the propagation of preprocessor errors in count-based methods. In this study, we explore whether the LMbased method is valid for analyzing the word order. As a case study, this study focuses on Japanese due to its complex and flexible word order. To validate the LM-based method, we test (i) parallels between LMs and human word order preference, and (ii) consistency of the results obtained using the LM-based method with previous linguistic studies. Through our experiments, we tentatively conclude that LMs display sufficient word order knowledge for usage as an analysis tool. Finally, using the LMbased method, we demonstrate the relationship between the canonical word order and topicalization, which had yet to be analyzed by largescale experiments.

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“…In the NLP field, LMs are widely used to estimate the acceptability of text (Olteanu et al, 2006;Kann et al, 2018). An overview of the LM-based method is shown in Figure 1.…”

Section: Overview Of the Lm-based Methodsmentioning

confidence: 99%

Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese

Kuribayashi

Ito²,

Suzuki

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

View full text Add to dashboard Cite

show abstract

“…In the NLP field, LMs are widely used to estimate the acceptability of text (Olteanu et al, 2006;Kann et al, 2018). An overview of the LM-based method is shown in Figure 1.…”

Section: Overview Of the Lm-based Methodsmentioning

confidence: 99%

Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese

Kuribayashi

Ito²,

Suzuki

et al. 2020

Preprint

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“…LM and MLM scores are commonly employed in MT re-ranking (Olteanu et al, 2006). Typically, a set of K candidate translations is generated using an NMT model.…”

Section: Masked Language Model Scorementioning

confidence: 99%

Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards

Gain,

Appicharla,

Chennabasavaraj

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Community Question-Answering (CQA) portals serve as a valuable tool for helping users within an organization. However, making them accessible to non-English-speaking users continues to be a challenge. Translating questions can broaden the community's reach, benefiting individuals with similar inquiries in various languages. Translating questions using Neural Machine Translation (NMT) poses more challenges, especially in noisy environments, where the grammatical correctness of the questions is not monitored. These questions may be phrased as statements by non-native speakers, with incorrect subject-verb order and sometimes even missing question marks. Creating a synthetic parallel corpus from such data is also difficult due to its noisy nature. To address this issue, we propose a training methodology that fine-tunes the NMT system only using source-side data. Our approach balances adequacy and fluency by utilizing a loss function that combines BERTScore and Masked Language Model (MLM) Score. Our method surpasses the conventional Maximum Likelihood Estimation (MLE) based fine-tuning approach, which relies on synthetic target data, by achieving a 1.9 BLEU score improvement. Our model exhibits robustness while we add noise to our baseline, and still achieve 1.1 BLEU improvement and large improvements on TER and BLEURT metrics. Our proposed methodology is model-agnostic and is only necessary during the training phase. We make the codes and datasets publicly available at https: //www.iitp.ac.in/~ai-nlp-ml/ resources.html#DomainAdapt for facilitating further research.

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Language models and reranking for machine translation

Cited by 10 publications

References 6 publications

Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese

Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese

Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese

Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards

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