| Challenge: | Existing models for machine translation have been evaluated with standard automatic metrics, but are poorly adapted to evaluating discourse phenomena. |
| Approach: | They propose to use BLEU to train multi-encoder NMT models on English subtitles to test their ability to exploit previous source and target sentences. |
| Outcome: | The proposed multi-encoder models give limited improvements on the coreference and coherence tests. |
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Exploiting Sentential Context for Neural Machine Translation (P19-1)
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| Challenge: | Existing approaches to exploit sentential context for machine translation are not well studied. |
| Approach: | They propose a shallow sentential context that exploits top encoder layer, and a deep sentential one that aggregates sentential representations from all internal layers. |
| Outcome: | The proposed model outperforms the strong Transformer model on the English-German and English-French benchmarks. |
Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns (2020.acl-main)
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| Challenge: | Recent studies have focused on past sentences as context with a focus on anaphora translation. |
| Approach: | They propose to use future context to improve NMT performance by comparing a contextual NMT model trained with past context to a context-agnostic model. |
| Outcome: | The proposed model outperforms the context-agnostic Transformer and shows comparable and in some cases improved performance. |
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference (N18-2)
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| Challenge: | Existing methods to investigate whether sentence representations from NMT systems capture distinct semantic phenomena are limited. |
| Approach: | They propose a process to investigate the extent to which sentence representations arising from neural machine translation systems encode distinct semantic phenomena. |
| Outcome: | The proposed model is suited to supporting inferences at the syntax-semantics interface, compared to anaphora resolution requiring world knowledge. |
A Test Set for Discourse Translation from Japanese to English (2020.lrec-1)
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| Challenge: | Compared with a previous study on test sets for English-to-French discourse translation, we needed different approaches because Japanese has zero pronouns and represents different senses in different characters. |
| Approach: | They used a test set for Japanese-to-English discourse translation to evaluate the power of context-aware machine translation. |
| Outcome: | The results show that the translation accuracy of Japanese-to-English discourse translation is improved by using context-aware neural machine translation. |
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. |
When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion (P19-1)
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| Challenge: | et al., 2018: translation errors due to the lack of extra-sentential context are becoming more and more noticeable among otherwise adequate translations. |
| Approach: | They propose a context-aware translation model that uses sentence-level data to identify inconsistencies . standard metrics are not sensitive to improvements in consistency in document-level translations . |
| Outcome: | The proposed model shows major gains over baseline without sacrificing performance . standard metrics are not sensitive to improvements in document-level translations . |
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation (2020.acl-main)
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| Challenge: | In encoder-decoder neural models, multiple encoders are used to represent contextual information in addition to the individual sentence. |
| Approach: | They propose to use multiple context encoders to encode the individual sentences in document-level neural machine translation (NMT) They propose a noisy dropout setup and a single-encoder approach to encode context sentences. |
| Outcome: | The proposed approach encodes the context and the current sentence without contexts. |
Document Graph for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing document-level NMT methods fail to leverage contexts beyond a few set of previous sentences. |
| Approach: | They propose to represent a document as a graph that connects relevant contexts regardless of distances. |
| Outcome: | Experiments on IWSLT English–French, Chinese-English, WMT English–German and Opensubtitle English–Russian show that using document graphs can significantly improve translation quality. |
Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)
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| Challenge: | Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. |
| Approach: | They propose to use mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. |
| Outcome: | The proposed model distinguishes and exploits word-level domain contexts on Chinese-English and English-French translation tasks. |
One Sentence One Model for Neural Machine Translation (L18-1)
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| Challenge: | Neural machine translation (NMT) is a new state of the art that can produce better results than traditional statistical machine translation. |
| Approach: | They propose a dynamic neural network which learns a general network as usual and fine-tunes it for each test sentence. |
| Outcome: | The proposed method improves translation performance when similar sentences are available. |