Rethinking Document-level Neural Machine Translation

• URL: http://arxiv.org/abs/2010.08961v2
• Date: Mon, 14 Mar 2022 13:28:53 GMT
• Title: Rethinking Document-level Neural Machine Translation
• Authors: Zewei Sun, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Shujian Huang, Jiajun Chen, Lei Li
• Abstract summary: We try to answer the question: Is the capacity of current models strong enough for document-level translation? We observe that the original Transformer with appropriate training techniques can achieve strong results for document translation, even with a length of 2000 words.
• Score: 73.42052953710605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper does not aim at introducing a novel model for document-level neural machine translation. Instead, we head back to the original Transformer model and hope to answer the following question: Is the capacity of current models strong enough for document-level translation? Interestingly, we observe that the original Transformer with appropriate training techniques can achieve strong results for document translation, even with a length of 2000 words. We evaluate this model and several recent approaches on nine document-level datasets and two sentence-level datasets across six languages. Experiments show that document-level Transformer models outperforms sentence-level ones and many previous methods in a comprehensive set of metrics, including BLEU, four lexical indices, three newly proposed assistant linguistic indicators, and human evaluation.

Related papers

• Document-Level Language Models for Machine Translation [37.106125892770315]
  We build context-aware translation systems utilizing document-level monolingual data instead. We improve existing approaches by leveraging recent advancements in model combination. In most scenarios, back-translation gives even better results, at the cost of having to re-train the translation system.
  arXiv  Detail & Related papers  (2023-10-18T20:10:07Z)
• HanoiT: Enhancing Context-aware Translation via Selective Context [95.93730812799798]
  Context-aware neural machine translation aims to use the document-level context to improve translation quality. The irrelevant or trivial words may bring some noise and distract the model from learning the relationship between the current sentence and the auxiliary context. We propose a novel end-to-end encoder-decoder model with a layer-wise selection mechanism to sift and refine the long document context.
  arXiv  Detail & Related papers  (2023-01-17T12:07:13Z)
• Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation [14.135048254120615]
  We introduce a recurrent memory unit to the vanilla Transformer, which supports the information exchange between the sentence and previous context. We conduct experiments on three popular datasets for document-level machine translation and our model has an average improvement of 0.91 s-BLEU over the sentence-level baseline.
  arXiv  Detail & Related papers  (2022-05-03T14:55:53Z)
• SMDT: Selective Memory-Augmented Neural Document Translation [53.4627288890316]
  We propose a Selective Memory-augmented Neural Document Translation model to deal with documents containing large hypothesis space of context. We retrieve similar bilingual sentence pairs from the training corpus to augment global context. We extend the two-stream attention model with selective mechanism to capture local context and diverse global contexts.
  arXiv  Detail & Related papers  (2022-01-05T14:23:30Z)
• SPECTER: Document-level Representation Learning using Citation-informed Transformers [51.048515757909215]
  SPECTER generates document-level embedding of scientific documents based on pretraining a Transformer language model. We introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction to document classification and recommendation.
  arXiv  Detail & Related papers  (2020-04-15T16:05:51Z)
• Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation [59.191079800436114]
  Document-level machine translation incorporates inter-sentential dependencies into the translation of a source sentence. We propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.
  arXiv  Detail & Related papers  (2020-03-30T03:38:01Z)
• Capturing document context inside sentence-level neural machine translation models with self-training [5.129814362802968]
  Document-level neural machine translation has received less attention and lags behind its sentence-level counterpart. We propose an approach that doesn't require training a specialized model on parallel document-level corpora. Our approach reinforces the choices made by the model, thus making it more likely that the same choices will be made in other sentences in the document.
  arXiv  Detail & Related papers  (2020-03-11T12:36:17Z)
• Towards Making the Most of Context in Neural Machine Translation [112.9845226123306]
  We argue that previous research did not make a clear use of the global context. We propose a new document-level NMT framework that deliberately models the local context of each sentence.
  arXiv  Detail & Related papers  (2020-02-19T03:30:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.