| Challenge: | Using a transformer architecture, we study coreference phenomena in three neural machine translation systems. |
| Approach: | They analyse coreference phenomena in three neural machine translation systems . they manually annotate (the possibly incorrect) coreference chains in the outputs . |
| Outcome: | The proposed model shows stronger translationese effects in machine translated outputs than in human translations. |
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Evaluating and Improving the Coreference Capabilities of Machine Translation Models (2023.eacl-main)
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| Challenge: | Currently, end-to-end models learn coreference resolution implicitly by observing aligned sentences in bilingual corpora. |
| Approach: | They develop a method that derives coreference clusters from MT output and evaluates them without requiring annotations in the target language. |
| Outcome: | The proposed model outperforms existing models on three challenging benchmarks. |
Context-aware Neural Machine Translation with Coreference Information (D19-65)
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| Challenge: | Existing models for translating a sentence in a text do not consider coreference relations provided within the text. |
| Approach: | They propose a graph-based encoder which can consider coreference relations provided within the text explicitly. |
| Outcome: | The proposed model improves on the previous approach by 0.9 points on the BLEU score . the graph-based encoder can handle a longer text well, compared with the previous model . |
Revisiting Context Choices for Context-aware Machine Translation (2024.lrec-main)
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| Challenge: | Recent work has cast doubt on whether context-aware machine translation models learn useful signals from context or are improvements in automatic evaluation metrics just a side-effect. |
| Approach: | They propose to use separate encoders for source sentence and context as multiple sources for one target sentence to train context-aware machine translation models. |
| Outcome: | The proposed model improves translation quality even with empty lines as context, but the correct context improves it and random out-of-domain context degrades it. |
ParCorFull: a Parallel Corpus Annotated with Full Coreference (L18-1)
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| Challenge: | Recent research in multilingual coreference and automatic pronoun translation has led to important insights into the problem and some promising results. |
| Approach: | They propose a corpus annotated with full coreference chains that addresses a problem that machine translation and other multilingual natural language processing (NLP) technologies face: translation of coreference across languages. |
| Outcome: | The proposed corpus contains parallel texts for the language pair English-German, two major European languages. |
Improving the Transformer Translation Model with Document-Level Context (D18-1)
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| Challenge: | Existing models for document-level context translation ignore documentlevel context. |
| Approach: | They propose a document-level context encoder to represent document- level context and integrate it into the Transformer model. |
| Outcome: | Experiments on NIST Chinese-English and IWSLT French-English datasets show that the proposed translation model outperforms the Transformer model significantly. |
Rethinking Document-level Neural Machine Translation (2022.findings-acl)
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| Challenge: | Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence . |
| Approach: | They propose to use the original Transformer model to test document-level neural machine translation . they find that the original transformer models can achieve strong results for document translation if trained properly . |
| Outcome: | The proposed model outperforms sentence-level models on nine datasets and two sentence- level datasets across six languages. |
Analysing concatenation approaches to document-level NMT in two different domains (D19-65)
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| Challenge: | a recent study has shown that discourse-related biases affect neural MT performance. |
| Approach: | They propose a comparative evaluation scheme that contrasts coherent context with artificially scrambled documents and absent context. |
| Outcome: | The proposed evaluation scheme contrasts coherent context with artificially scrambled documents and absent context on two popular datasets. |
A fine-grained error analysis of NMT, SMT and RBMT output for English-to-Dutch (L18-1)
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| Challenge: | Since 2016, the landscape of automated translation has substantially changed with the arrival of neural machine translation (NMT). |
| Approach: | They propose to use an annotated SCATE corpus of MT errors to enrich the SCATE error taxonomy to fit the neural MT output. |
| Outcome: | The proposed system outperforms phrase-based and rule-based systems except for lexical issues. |
Variation in Coreference Strategies across Genres and Production Media (2020.coling-main)
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| Challenge: | a lack of work on automatic coreference resolution on spoken and written language has led to inconclusive results. |
| Approach: | They propose to use Ontonotes, Switchboard and Twitter to investigate coreference . they find fairly clear patterns of "behavior" for the different genres/medias . |
| Outcome: | The results show that the choice of genre and the medium (spoken versus spoken) relates to the spokenwritten spectrum for coreference strategies. |
Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting (2023.emnlp-main)
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| Challenge: | idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts. |
| Approach: | They propose to use retrieval-augmented models to increase the accuracy of a strong pretrained machine translation model on idiomatic sentences by up to 13%. |
| Outcome: | The proposed techniques improve the accuracy of a strong pretrained model on idiomatic sentences by up to 13% in absolute accuracy, and holds potential benefits for non-idiomatic phrases. |