The primary objective of our work is to build a large-scale English-Thai dataset for machine translation. We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources, namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents. Methodology for gathering data, building parallel texts and removing noisy sentence pairs are presented in a reproducible manner. We train machine translation models based on this dataset. Our models' performance are comparable to that of Google Translation API (as of May 2020) for Thai-English and outperform Google when the Open Parallel Corpus (OPUS) is included in the training data for both Thai-English and English-Thai translation. The dataset, pre-trained models, and source code to reproduce our work are available for public use.
King Mongkut’s Institute of Technology Ladkrabang (KMITL)（先皇技术学院（拉卡邦））；ITMO University（ITMO大学）
Distributional semantics in the form of word embeddings are an essential ingredient to many modern natural language processing systems. The quantification of semantic similarity between words can be used to evaluate the ability of a system to perform semantic interpretation. To this end, a number of word similarity datasets have been created for the English language over the last decades. For Thai language few such resources are available. In this work, we create three Thai word similarity datasets by translating and re-rating the popular WordSim-353, SimLex-999 and SemEval-2017-Task-2 dataset…
Reference-Grounded Data Curation for Instruction-Following Thai-English Machine Translation
iApp Technology（iApp科技公司）；Intelligent Informatics and Service Innovation Research Center（智能信息学与服务创新研究中心）；Artificial Intelligence Entrepreneur Association of Thailand (AIEAT)（泰国人工智能企业家协会）；Sirindhorn International Institute of Technology, Thammasat University（诗琳通国际理工学院，法政大学）
SiamGPT: Quality-First Fine-Tuning for Stable Thai Text Generation
iApp Technology Co., Ltd.（iApp技术有限公司）；Intelligent Informatics and Service Innovation Research Center, Thailand（智能信息与服务创新研究中心，泰国）；Rajamangala University of Technology Lanna Nan, Thailand（拉玛安高校技术大学兰纳纳分校，泰国）；National Electronics and Computer Technology Center (NECTEC)（国家电子与计算机技术中心）；Artificial Intelligence Association of Thailand (AIAT)（泰国人工智能协会）
Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?
Ruamrudee International School（蓝木国际学校）；Chulalongkorn University（朱拉隆功大学）；Vidyasirimedhi Institute of Science and Technology（维迪西里梅蒂科学技术学院）；PreceptorAI team, CARIVA Thailand（泰国CARIVA公司PreceptorAI团队）；Bangkok Christian International School（曼谷基督国际学校）；Mahidol University（玛希隆大学）；King’s College London（伦敦国王学院）
Chulalongkorn University（朱拉隆功大学）；Kasikorn Business Technology Group (KBTG)（开泰商业科技集团（KBTG））
A sentence is typically treated as the minimal syntactic unit used for extracting valuable information from a longer piece of text. However, in written Thai, there are no explicit sentence markers. We proposed a deep learning model for the task of sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of la…
Dublin City University（都柏林城市大学）；Iconic Translation Machines（Iconic翻译机器公司）；Trinity College Dublin（都柏林圣三一学院）
Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English--Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations o…
On Creating an English-Thai Code-switched Machine Translation in Medical Domain
Bangkok Christian International School（曼谷基督教国际学校）；Vidyasirimedhi Institute of Science and Technology（维迪亚西密提科技学院）；Chulalongkorn University（朱拉隆功大学）；Mahidol University（玛希隆大学）
King Mongkut’s Institute of Technology Ladkrabang（蒙kut国王理工学院）；Chiang Mai University（清迈大学）；Kasikorn Labs（开泰实验室）
Dependency parsing (DP) is a task that analyzes text for syntactic structure and relationship between words. DP is widely used to improve natural language processing (NLP) applications in many languages such as English. Previous works on DP are generally applicable to formally written languages. However, they do not apply to informal languages such as the ones used in social networks. Therefore, DP has to be researched and explored with such social network data. In this paper, we explore and identify a DP model that is suitable for Thai social network data. After that, we will identify the app…
This paper proposes an analysis of classifiers into four major types: UNIT, METRIC, GROUP and SPECIES, based on properties of both Japanese and English. The analysis makes possible a uniform and straightforward treatment of noun phrases headed by classifiers in Japanese-to-English machine translation, and has been implemented in the MT system ALT-J/E. Although the analysis is based on the characteristics of, and differences between, Japanese and English, it is shown to be also applicable to the unrelated language Thai.
Unsupervised Bilingual Dictionary Induction methods based on the initialization and the self-learning have achieved great success in similar language pairs, e.g., English-Spanish. But they still fail and have an accuracy of 0% in many distant language pairs, e.g., English-Japanese. In this work, we show that this failure results from the gap between the actual initialization performance and the minimum initialization performance for the self-learning to succeed. We propose Iterative Dimension Reduction to bridge this gap. Our experiments show that this simple method does not hamper the perform…
Google Research（谷歌研究院）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case study. We assume a strict setting with no access to parallel data or machine translation and find that common transfer learning approaches struggle in this setting, as a generative multilingual model fine-tuned purely on English catastrophically forgets how to generate non-English. Given the recent rise of parameter-efficient adaptation techniques, we conduct the first investigation into how one such method, prompt …
This paper accompanies the software documentation data set for machine translation, a parallel evaluation data set of data originating from the SAP Help Portal, that we released to the machine translation community for research purposes. It offers the possibility to tune and evaluate machine translation systems in the domain of corporate software documentation and contributes to the availability of a wider range of evaluation scenarios. The data set comprises of the language pairs English to Hindi, Indonesian, Malay and Thai, and thus also increases the test coverage for the many low-resource …
ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languages
