Translation Word-Level Auto-Completion: What can we achieve out of the box?

• URL: http://arxiv.org/abs/2210.12802v1
• Date: Sun, 23 Oct 2022 17:58:01 GMT
• Title: Translation Word-Level Auto-Completion: What can we achieve out of the box?
• Abstract summary: We investigate the possibility of using pre-trained models and out-of-the-box features from available libraries. We introduce our open-source API, based on CTranslate2, to serve translations, auto-suggestions, and auto-completions.
• Score: 6.241494296494433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research on Machine Translation (MT) has achieved important breakthroughs in several areas. While there is much more to be done in order to build on this success, we believe that the language industry needs better ways to take full advantage of current achievements. Due to a combination of factors, including time, resources, and skills, businesses tend to apply pragmatism into their AI workflows. Hence, they concentrate more on outcomes, e.g. delivery, shipping, releases, and features, and adopt high-level working production solutions, where possible. Among the features thought to be helpful for translators are sentence-level and word-level translation auto-suggestion and auto-completion. Suggesting alternatives can inspire translators and limit their need to refer to external resources, which hopefully boosts their productivity. This work describes our submissions to WMT's shared task on word-level auto-completion, for the Chinese-to-English, English-to-Chinese, German-to-English, and English-to-German language directions. We investigate the possibility of using pre-trained models and out-of-the-box features from available libraries. We employ random sampling to generate diverse alternatives, which reveals good results. Furthermore, we introduce our open-source API, based on CTranslate2, to serve translations, auto-suggestions, and auto-completions.

Related papers

• Finding the Translation Switch: Discovering and Exploiting the Task-Initiation Features in LLMs [69.28193153685893]
  Large Language Models (LLMs) frequently exhibit strong translation abilities, even without task-specific fine-tuning.<n>To demystify this process, we leverage Sparse Autoencoders (SAEs) and introduce a novel framework for identifying task-specific features.<n>Our work not only decodes a core component of the translation mechanism in LLMs but also provides a blueprint for using internal model mechanism to create more robust and efficient models.
  arXiv  Detail & Related papers  (2026-01-16T06:29:07Z)
• Translation via Annotation: A Computational Study of Translating Classical Chinese into Japanese [5.799589603302489]
  Ancient people translated classical Chinese into Japanese by annotating around each character.<n>We abstract this process as sequence tagging tasks and fit them into modern language technologies.<n>We show that under the low-resource setting, introducing auxiliary Chinese NLP tasks has a promoting effect on the training of sequence tagging tasks.
  arXiv  Detail & Related papers  (2025-11-07T13:46:16Z)
• The AI Language Proficiency Monitor -- Tracking the Progress of LLMs on Multilingual Benchmarks [0.0]
  We introduce the AI Language Monitor, a comprehensive benchmark that assesses large language models (LLMs) performance across up to 200 languages.<n>Our benchmark aggregates diverse tasks including translation, question answering, math, and reasoning, using datasets such as FLORES+, MMLU, GSM8K, TruthfulQA, and ARC.<n>We provide an open-source, auto-updating leaderboard and dashboard that supports researchers, developers, and policymakers in identifying strengths and gaps in model performance.
  arXiv  Detail & Related papers  (2025-07-11T12:38:02Z)
• KIT's Offline Speech Translation and Instruction Following Submission for IWSLT 2025 [56.61209412965054]
  We present the Karlsruhe Institute of Technology's submissions for the Offline ST and Instruction Following (IF) tracks.<n>We propose a pipeline that employs multiple automatic speech recognition systems, whose outputs are fused using an LLM with document-level context.<n>For the IF track, we develop an end-to-end model that integrates a speech encoder with an LLM to perform a wide range of instruction-following tasks.
  arXiv  Detail & Related papers  (2025-05-19T12:21:29Z)
• Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs [13.458891794688551]
  We evaluate pre-translation strategies across 35 languages covering both low and high-resource languages. Our experiments show the impact of factors as similarity to English, translation quality and the size of pre-trained data, on the model performance with pre-translation.
  arXiv  Detail & Related papers  (2025-02-13T13:49:30Z)
• Enhancing Entertainment Translation for Indian Languages using Adaptive Context, Style and LLMs [3.55026004901472]
  We introduce an algorithm to estimate the context and style of the current session and use these estimations to generate a prompt that guides a Large Language Model (LLM) to generate high-quality translations. Our method is both language and LLM-agnostic, making it a general-purpose tool.
  arXiv  Detail & Related papers  (2024-12-29T11:33:51Z)
• Extracting and Transferring Abilities For Building Multi-lingual Ability-enhanced Large Language Models [104.96990850774566]
  We propose a Multi-lingual Ability Extraction and Transfer approach, named as MAET. Our key idea is to decompose and extract language-agnostic ability-related weights from large language models. Experiment results show MAET can effectively and efficiently extract and transfer the advanced abilities, and outperform training-based baseline methods.
  arXiv  Detail & Related papers  (2024-10-10T11:23:18Z)
• Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing? [3.8073142980733]
  We ask whether the algorithms used in Neural Machine Translation have inherent inductive biases that are beneficial for most types of inputs but might harm the processing of untypical texts. We conduct a series of experiments to investigate whether NMT systems struggle with maintaining the diversity of such texts. Our ultimate goal is to develop alternatives that do not enforce uniformity in the distribution of statistical properties in the output.
  arXiv  Detail & Related papers  (2024-09-15T01:06:07Z)
• Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions [0.0]
  Generative large language models (LLMs) have demonstrated exceptional proficiency in various natural language processing (NLP) tasks. We conducted an investigation into two popular prompting methods and their combination, focusing on cross-language combinations of Persian, English, and Russian.
  arXiv  Detail & Related papers  (2024-01-16T15:16:34Z)
• DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models [76.79929883963275]
  DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems. It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
  arXiv  Detail & Related papers  (2024-01-04T11:27:48Z)
• Is Translation Helpful? An Empirical Analysis of Cross-Lingual Transfer in Low-Resource Dialog Generation [21.973937517854935]
  Cross-lingual transfer is important for developing high-quality chatbots in multiple languages. In this work, we investigate whether it is helpful to utilize machine translation (MT) at all in this task. Experiments show that leveraging English dialog corpora can indeed improve the naturalness, relevance and cross-domain transferability in Chinese.
  arXiv  Detail & Related papers  (2023-05-21T15:07:04Z)
• Exploring Human-Like Translation Strategy with Large Language Models [93.49333173279508]
  Large language models (LLMs) have demonstrated impressive capabilities in general scenarios. This work proposes the MAPS framework, which stands for Multi-Aspect Prompting and Selection. We employ a selection mechanism based on quality estimation to filter out noisy and unhelpful knowledge.
  arXiv  Detail & Related papers  (2023-05-06T19:03:12Z)
• Adaptive Machine Translation with Large Language Models [7.803471587734353]
  We investigate how we can utilize in-context learning to improve real-time adaptive machine translation. We conduct experiments across five diverse language pairs, namely English-to-Arabic (EN-AR), English-to-Chinese (EN-ZH), English-to-French (EN-FR), English-to-Kinyarwanda (EN-RW), and English-to-Spanish (EN-ES)
  arXiv  Detail & Related papers  (2023-01-30T21:17:15Z)
• 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)
• No Language Left Behind: Scaling Human-Centered Machine Translation [69.28110770760506]
  We create datasets and models aimed at narrowing the performance gap between low and high-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2022-07-11T07:33:36Z)
• Towards Best Practices for Training Multilingual Dense Retrieval Models [54.91016739123398]
  We focus on the task of monolingual retrieval in a variety of typologically diverse languages using one such design. Our study is organized as a "best practices" guide for training multilingual dense retrieval models.
  arXiv  Detail & Related papers  (2022-04-05T17:12:53Z)
• Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation [53.22775597051498]
  We present a continual pre-training framework on mBART to effectively adapt it to unseen languages. Results show that our method can consistently improve the fine-tuning performance upon the mBART baseline. Our approach also boosts the performance on translation pairs where both languages are seen in the original mBART's pre-training.
  arXiv  Detail & Related papers  (2021-05-09T14:49:07Z)

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.