Experiences of Adapting Multimodal Machine Translation Techniques for Hindi

Gain, Baban; Bandyopadhyay, Dibyanayan; Ekbal, Asif

doi:10.26615/978-954-452-073-1_007

Cited by 2 publications

(3 citation statements)

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“…It is important to note that the questions in this context were well-structured and grammatically correct. Gain et al (2022) tackled the translation of user-generated questions and enhanced the translation quality by incorporating answers alongside questions during the training process. This approach allowed the model to leverage contextual information from the answers.…”

Section: Related Workmentioning

confidence: 99%

“…For pre-training our NMT model, we utilize the Samanantar corpus, which comprises over 10 million sentence pairs for English-Hindi in the general domain. During the fine-tuning process, we focus on the first 50,000 questions from the Flipkart QnA corpus (Gain et al, 2022), using only the English side of the data. In our evaluation, we employ both sides of the test set, consisting of 500 questions.…”

Section: Dataset and Annotationmentioning

confidence: 99%

“…We obtain a pre-trained model from (Gain et al, 2022), which was trained on a large-scale English-Hindi data and use it as our baseline. The baseline consists of standard transformer architecture with six encoders and six decoders.…”

Section: Baselinementioning

confidence: 99%

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

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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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Section: Related Workmentioning

confidence: 99%

Section: Dataset and Annotationmentioning

confidence: 99%