Papers with in-domain parallel
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation (2022.findings-acl)
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| Challenge: | Current research in NMT Domain Adaptation rarely provides insights on the amount of data required to perform Domain . |
| Approach: | They propose a Domain adaptation learning curve prediction model that predicts prospective DA performance based on in-domain monolingual samples in the source language. |
| Outcome: | The proposed model predicts prospective DA performance based on in-domain monolingual samples in the source language. |
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation (D19-1)
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| Challenge: | Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context. |
| Approach: | They propose a hierarchical model to learn document context for document-level neural machine translation . they use a sentence encoder to capture intra-sentence dependencies and a document encoder . |
| Outcome: | The proposed model significantly improves document-level translation performance over strong baselines. |
Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language (C18-1)
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| Challenge: | a study aims to develop a language transferring system to avoid the trouble of acquiring and labeling a new big SLU corpus . general-purpose translators cannot handle the lot of semantic labels, not to mention cultural differences . a RL-based language transfer method can be used to adapt the adapted translator to a target language . |
| Approach: | They propose to use reinforcement learning to adapt a spoken language understanding model to a target language. |
| Outcome: | The proposed language transferring method improves domain classification accuracy by 22% compared with naive translation . the proposed language transfer method can be used on Chinese to English translators with more proper slot tags . |
Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | kNN-MT is a non-parametric method that uses nearest neighbor retrieval to translate out-of-domain sentences, rare words, etc. |
| Approach: | They propose a framework that directly uses in-domain monolingual sentences to build an effective datastore for k-nearest-neighbor retrieval. |
| Outcome: | The proposed framework improves translation accuracy with target-side monolingual data while achieving comparable performance with back-translation. |
Iterative Domain-Repaired Back-Translation (2020.emnlp-main)
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| Challenge: | Existing studies show that NMT models perform poorly in specific domains when in-domain parallel corpora are scarce or nonexistent. |
| Approach: | They propose an iterative domain-repaired back-translation framework to refine translations in bilingual data by round-trip translating monolingual sentences. |
| Outcome: | The proposed framework achieves 15.79 and 4.47 BLEU improvements over unadapted models and back-translation in domain-specific translations. |
Esposito: An English-Persian Scientific Parallel Corpus for Machine Translation (2024.lrec-main)
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| Challenge: | Existing scientific corpus for English-Persian language pairs is lacking . supervised neural machine translation requires millions of parallel sentences . |
| Approach: | They propose a parallel corpus called Esposito which contains 3.5 million parallel sentences . they also propose 'test sets' that might serve as a baseline for future studies . |
| Outcome: | The proposed system improves the baseline on average by 7.6 and 8.4 BLEU scores for English-Persian language pairs. |
Iterative Nearest Neighbour Machine Translation for Unsupervised Domain Adaptation (2023.findings-acl)
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| Challenge: | Existing methods for supervised domain adaptation of machine translation focus on fine-tuning, which is non-extensible. |
| Approach: | They propose to perform unsupervised domain adaptation in a non-parametric manner by using in-domain monolingual data and performing nearest neighbour inference on both forward and backward directions. |
| Outcome: | The proposed method significantly improves the in-domain translation performance and achieves state-of-the-art results among non-parametric methods. |