Scene text recognition has attracted increasing interest in recent years due
to its wide range of applications in multilingual translation, autonomous
driving, etc. In this report, we describe our solution to the Out of Vocabulary
Scene Text Understanding (OOV-ST) Challenge, which aims to extract
out-of-vocabulary (OOV) words from natural scene images. Our oCLIP-based model
achieves 28.59\% in h-mean which ranks 1st in end-to-end OOV word recognition
track of OOV Challenge in ECCV2022 TiE Workshop.

本报告描述了我们在 ECCV2022 TiE 工作坊的 OOV Challenge 中的解决方案，其中我们的基于 oCLIP 的模型在 OOV 词识别任务中排名第一。

ECCV 2022 挑战赛：场景外词文本理解第一名解决方案：场景外词的端到端识别

1st Place Solution to ECCV 2022 Challenge on Out of Vocabulary Scene  Text Understanding: End-to-End Recognition of Out of Vocabulary Words

Out of vocabulary (OOV) is a problem in the context of Machine Translation
(MT) in low-resourced languages. When source and/or target languages are
morphologically rich, it becomes even worse. Bilingual list integration is an
approach to address the OOV problem. This allows more words to be translated
than are in the training data. However, since bilingual lists contain words in
the base form, it will not translate inflected forms for morphologically rich
languages such as Sinhala and Tamil. This paper focuses on data augmentation
techniques where bilingual lexicon terms are expanded based on case-markers
with the objective of generating new words, to be used in Statistical machine
Translation (SMT). This data augmentation technique for dictionary terms shows
improved BLEU scores for Sinhala-English SMT.

本文主要研究针对机器翻译中低资源语种的词汇缺失问题，提出了基于双语词表整合的方法和基于数据增强技术的方法，通过以词干和格标注扩充双语词表词汇以提高 SMT 的准确性，在 Sinhala-English 机器翻译任务中取得了改善。

领域特定的僧伽罗语 - 英语 - 泰米尔语统计机器翻译的数据增强和术语整合

Data Augmentation and Terminology Integration for Domain-Specific  Sinhala-English-Tamil Statistical Machine Translation

Neural machine translation (NMT) has achieved notable performance recently.
However, this approach has not been widely applied to the translation task
between Chinese and Uyghur, partly due to the limited parallel data resource
and the large proportion of rare words caused by the agglutinative nature of
Uyghur. In this paper, we collect ~200,000 sentence pairs and show that with
this middle-scale database, an attention-based NMT can perform very well on
Chinese-Uyghur/Uyghur-Chinese translation. To tackle rare words, we propose a
novel memory structure to assist the NMT inference. Our experiments
demonstrated that the memory-augmented NMT (M-NMT) outperforms both the vanilla
NMT and the phrase-based statistical machine translation (SMT). Interestingly,
the memory structure provides an elegant way for dealing with words that are
out of vocabulary.

本文介绍了使用中等规模的数据集和新颖的记忆结构在汉维翻译中应用基于注意力机制的神经机器翻译，并且此方法可解决极少见词汇的问题。研究发现，记忆增强型 NMT 处理稀缺词汇的效果优于传统 NMT 和基于短语的统计机器翻译。

增强记忆的汉维神经机器翻译

Memory-augmented Chinese-Uyghur Neural Machine Translation

We present a new approach for neural machine translation (NMT) using the
morphological and grammatical decomposition of the words (factors) in the
output side of the neural network. This architecture addresses two main
problems occurring in MT, namely dealing with a large target language
vocabulary and the out of vocabulary (OOV) words. By the means of factors, we
are able to handle larger vocabulary and reduce the training time (for systems
with equivalent target language vocabulary size). In addition, we can produce
new words that are not in the vocabulary. We use a morphological analyser to
get a factored representation of each word (lemmas, Part of Speech tag, tense,
person, gender and number). We have extended the NMT approach with attention
mechanism in order to have two different outputs, one for the lemmas and the
other for the rest of the factors. The final translation is built using some
\textit{a priori} linguistic information. We compare our extension with a
word-based NMT system. The experiments, performed on the IWSLT'15 dataset
translating from English to French, show that while the performance do not
always increase, the system can manage a much larger vocabulary and
consistently reduce the OOV rate. We observe up to 2% BLEU point improvement in
a simulated out of domain translation setup.

本文提出了一种基于形态和语法分解的神经机器翻译方法，可以更好地处理庞大的目标语言词汇以及未登录词，实验结果显示该方法不仅可以降低 OOV 词比例，而且在模拟跨领域翻译方面也有出色表现。