Document-Level Language Models for Machine Translation

• URL: http://arxiv.org/abs/2310.12303v1
• Date: Wed, 18 Oct 2023 20:10:07 GMT
• Title: Document-Level Language Models for Machine Translation
• Authors: Frithjof Petrick and Christian Herold and Pavel Petrushkov and Shahram Khadivi and Hermann Ney
• Abstract summary: 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.
• Score: 37.106125892770315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the known limitations, most machine translation systems today still operate on the sentence-level. One reason for this is, that most parallel training data is only sentence-level aligned, without document-level meta information available. In this work, we set out to build context-aware translation systems utilizing document-level monolingual data instead. This can be achieved by combining any existing sentence-level translation model with a document-level language model. We improve existing approaches by leveraging recent advancements in model combination. Additionally, we propose novel weighting techniques that make the system combination more flexible and significantly reduce computational overhead. In a comprehensive evaluation on four diverse translation tasks, we show that our extensions improve document-targeted scores substantially and are also computationally more efficient. However, we also find that in most scenarios, back-translation gives even better results, at the cost of having to re-train the translation system. Finally, we explore language model fusion in the light of recent advancements in large language models. Our findings suggest that there might be strong potential in utilizing large language models via model combination.

Related papers

• Using Machine Translation to Augment Multilingual Classification [0.0]
  We explore the effects of using machine translation to fine-tune a multilingual model for a classification task across multiple languages. We show that translated data are of sufficient quality to tune multilingual classifiers and that this novel loss technique is able to offer some improvement over models tuned without it.
  arXiv  Detail & Related papers  (2024-05-09T00:31:59Z)
• Relay Decoding: Concatenating Large Language Models for Machine Translation [21.367605327742027]
  We propose an innovative approach called RD (Relay Decoding), which entails concatenating two distinct large models that individually support the source and target languages. By incorporating a simple mapping layer to facilitate the connection between these two models and utilizing a limited amount of parallel data for training, we successfully achieve superior results in the machine translation task.
  arXiv  Detail & Related papers  (2024-05-05T13:42:25Z)
• On Search Strategies for Document-Level Neural Machine Translation [51.359400776242786]
  Document-level neural machine translation (NMT) models produce a more consistent output across a document. In this work, we aim to answer the question how to best utilize a context-aware translation model in decoding.
  arXiv  Detail & Related papers  (2023-06-08T11:30:43Z)
• Unified Model Learning for Various Neural Machine Translation [63.320005222549646]
  Existing machine translation (NMT) studies mainly focus on developing dataset-specific models. We propose a versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks. OurNMT results in substantial improvements over dataset-specific models with significantly reduced model deployment costs.
  arXiv  Detail & Related papers  (2023-05-04T12:21:52Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Improving Multilingual Neural Machine Translation System for Indic Languages [0.0]
  We propose a multilingual neural machine translation (MNMT) system to address the issues related to low-resource language translation. A state-of-the-art transformer architecture is used to realize the proposed model. Trials over a good amount of data reveal its superiority over the conventional models.
  arXiv  Detail & Related papers  (2022-09-27T09:51:56Z)
• Mixed Attention Transformer for LeveragingWord-Level Knowledge to Neural Cross-Lingual Information Retrieval [15.902630454568811]
  We propose a novel Mixed Attention Transformer (MAT) that incorporates external word level knowledge, such as a dictionary or translation table. By encoding the translation knowledge into an attention matrix, the model with MAT is able to focus on the mutually translated words in the input sequence.
  arXiv  Detail & Related papers  (2021-09-07T00:33:14Z)
• Paraphrastic Representations at Scale [134.41025103489224]
  We release trained models for English, Arabic, German, French, Spanish, Russian, Turkish, and Chinese languages. We train these models on large amounts of data, achieving significantly improved performance from the original papers.
  arXiv  Detail & Related papers  (2021-04-30T16:55:28Z)
• Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model [12.543106304662059]
  We present a method to turn a sentence-level translation model into a context-aware model by incorporating a document-level language model into the decoder. Our decoder is built upon only a sentence-level parallel corpora and monolingual corpora. In a theoretical viewpoint, the core part of this work is the novel representation of contextual information using point-wise mutual information between context and the current sentence.
  arXiv  Detail & Related papers  (2020-10-24T08:06:18Z)
• Beyond English-Centric Multilingual Machine Translation [74.21727842163068]
  We create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT.
  arXiv  Detail & Related papers  (2020-10-21T17:01:23Z)
• Rethinking Document-level Neural Machine Translation [73.42052953710605]
  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.
  arXiv  Detail & Related papers  (2020-10-18T11:18:29Z)

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.