Extended Parallel Corpus for Amharic-English Machine Translation

• URL: http://arxiv.org/abs/2104.03543v1
• Date: Thu, 8 Apr 2021 06:51:08 GMT
• Title: Extended Parallel Corpus for Amharic-English Machine Translation
• Authors: Andargachew Mekonnen Gezmu, Andreas N\"urnberger and Tesfaye Bayu Bati
• Abstract summary: It will be useful for machine translation of an under-resourced language, Amharic. We trained neural machine translation and phrase-based statistical machine translation models using the corpus.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes the acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. It will be useful for machine translation of an under-resourced language, Amharic. The corpus is larger than previously compiled corpora; it is released for research purposes. We trained neural machine translation and phrase-based statistical machine translation models using the corpus. In the automatic evaluation, neural machine translation models outperform phrase-based statistical machine translation models.

Related papers

• LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translation [67.24113079928668]
  We present LexMatcher, a method for data curation driven by the coverage of senses found in bilingual dictionaries. Our approach outperforms the established baselines on the WMT2022 test sets.
  arXiv  Detail & Related papers  (2024-06-03T15:30:36Z)
• Language Model is a Branch Predictor for Simultaneous Machine Translation [73.82754138171587]
  We propose incorporating branch prediction techniques in SiMT tasks to reduce translation latency. We utilize a language model as a branch predictor to predict potential branch directions. When the actual source word deviates from the predicted source word, we use the real source word to decode the output again, replacing the predicted output.
  arXiv  Detail & Related papers  (2023-12-22T07:32:47Z)
• Bilingual Corpus Mining and Multistage Fine-Tuning for Improving Machine Translation of Lecture Transcripts [50.00305136008848]
  We propose a framework for parallel corpus mining, which provides a quick and effective way to mine a parallel corpus from publicly available lectures on Coursera. For both English--Japanese and English--Chinese lecture translations, we extracted parallel corpora of approximately 50,000 lines and created development and test sets. This study also suggests guidelines for gathering and cleaning corpora, mining parallel sentences, cleaning noise in the mined data, and creating high-quality evaluation splits.
  arXiv  Detail & Related papers  (2023-11-07T03:50:25Z)
• Exploring Linguistic Similarity and Zero-Shot Learning for Multilingual Translation of Dravidian Languages [0.34998703934432673]
  We build a single-decoder neural machine translation system for Dravidian-Dravidian multilingual translation. Our model achieves scores within 3 BLEU of large-scale pivot-based models when it is trained on 50% of the language directions.
  arXiv  Detail & Related papers  (2023-08-10T13:38:09Z)
• Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models [55.35106713257871]
  We introduce DecoMT, a novel approach of few-shot prompting that decomposes the translation process into a sequence of word chunk translations. We show that DecoMT outperforms the strong few-shot prompting BLOOM model with an average improvement of 8 chrF++ scores across the examined languages.
  arXiv  Detail & Related papers  (2023-05-22T14:52:47Z)
• 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)
• DEEP: DEnoising Entity Pre-training for Neural Machine Translation [123.6686940355937]
  It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. We propose DEEP, a DEnoising Entity Pre-training method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences.
  arXiv  Detail & Related papers  (2021-11-14T17:28:09Z)
• Lexically Cohesive Neural Machine Translation with Copy Mechanism [21.43163704217968]
  We employ a copy mechanism into a context-aware neural machine translation model to allow copying words from previous outputs. We conduct experiments on Japanese to English translation using an evaluation dataset for discourse translation.
  arXiv  Detail & Related papers  (2020-10-11T08:39:02Z)
• Preparation of Sentiment tagged Parallel Corpus and Testing its effect on Machine Translation [12.447116722795899]
  The paper discusses the preparation of the same sentiment tagged English-Bengali parallel corpus. The output of the translation model has been compared with a base-line translation model using automated metrics such as BLEU and TER.
  arXiv  Detail & Related papers  (2020-07-28T09:04:47Z)
• Parallel Corpus Filtering via Pre-trained Language Models [14.689457985200141]
  Web-crawled data provides a good source of parallel corpora for training machine translation models. Recent work shows that neural machine translation systems are more sensitive to noise than traditional statistical machine translation methods. We propose a novel approach to filter out noisy sentence pairs from web-crawled corpora via pre-trained language models.
  arXiv  Detail & Related papers  (2020-05-13T06:06:23Z)
• Urdu-English Machine Transliteration using Neural Networks [0.0]
  We present transliteration technique based on Expectation Maximization (EM) which is un-supervised and language independent. System learns the pattern and out-of-vocabulary words from parallel corpus and there is no need to train it on transliteration corpus explicitly.
  arXiv  Detail & Related papers  (2020-01-12T17:30:42Z)

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.