Text Generation is a subfield of Natural Language Processing (NLP) which attempts to teach machines to automatically generate texts that are indistinguishable from texts written by humans.

Ethical NLP is a subfield of Responsible NLP that deals with the ethical and social implications of developing and using natural language processing technologies. It involves developing algorithms and models that can handle issues such as fairness, bias, privacy, and transparency, and designing systems that promote ethical and responsible use of the technology.

Sentence Alignment refers to the process of identifying corresponding sentences in two different languages within a parallel corpus. This is a crucial step in training statistical machine translation models as it helps the model to learn how words and phrases in one language translate into another. The alignment is usually done based on sentence length and order, but more complex methods may also consider the actual content of the sentences.

Natural Language Processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Responsible & Trustworthy NLP is a subfield of Natural Language Processing (NLP) that is concerned with implementing methods that focus on fairness, transparency, trustworthiness, explainability, accountability, and ethical aspects at its core. It involves considering the societal impact of NLP applications and ensuring that they do not perpetuate or amplify biases or discrimination. Additionally, responsible NLP also involves developing methods to ensure the privacy and security of user data, and to mitigate the risks of unintended consequences or misuse of NLP systems.

Machine Translation is a subfield of Multilinguality and Text Generation that deals with developing algorithms and models for automatically translating text or speech from one language to another. It involves analyzing the input text or speech, generating a representation of its meaning in the source language, and then producing a new text or speech in the target language that conveys the same meaning.

Statistical Machine Translation (SMT) is a method of machine translation where the translation of text is generated on the basis of statistical models. These models are derived from the analysis of bilingual text corpora. The system learns to translate by being trained on large amounts of existing translations. It uses probability distributions to select the most likely translation among multiple possibilities.

Multilinguality is a subfield of Natural Language Processing (NLP) that refers to the ability of machines to understand, process, and generate natural language text in multiple languages. Multilinguality is concerned with addressing the challenges that arise due to the linguistic and structural variations that exist across different languages. This includes variations in word order, syntax, grammar, or vocabulary.

Language Models are a subfield of Semantic Text Processing that deal with the probabilistic modeling and generation of natural language, based on a learned probability function of sequences of words. Language models generate probabilities by training on corpora of natural language texts and can be fine-tuned for a variety of different downstream tasks.

Semantic Text Processing is a subfield of Natural Language Processing (NLP) that attempts to derive meaning from natural language and helps machines interpret textual data semantically. It involves developing algorithms and models that can understand and represent the semantics of words and sentences, and the relationships between them.

Language Model Alignment is the process of ensuring that a language model's outputs align with the preferences and intentions of a human user. It involves training the model to understand and respond accurately to the inputs it receives, minimizing misunderstanding or misinterpretation. This concept is crucial in developing AI systems that can effectively and safely interact with humans meaningfully.

<p>奠基於雙語自動對齊之 動介片語翻譯改進</p> <h1>Improving Phrase Translation Based on Sentence Alignment of Chinese-English Parallel Corpus</h1> <p>國立清華大學資工系 摘要 本研究呈現片語查詢的雛形系統 PrecisePhraseBook，能從雙語語料庫中，自動擷取英文 名詞與介系詞搭配的中文翻譯及例句，可輔助語言學習者學習語言，亦可改善機器翻 譯或提供語言研究者撰寫文法規則之參考。本方法利用雙語語料庫擷取英文片語的中 文翻譯。其方法為使用統計方法由雙語語料庫中的詞彙自動對齊，分別擷取名詞及介 系詞的翻譯，再根據由中文語料庫統計而來的中文高頻搭配詞，將名詞及介系詞的翻 譯做適當調整，並產生例句。系統執行時，使用者輸入一組英文名詞與介系詞的搭 配，系統會呈現資料庫中此搭配的翻譯及例句。本研究的評估方式是隨機抽取三十組 名詞及介系詞的搭配，人工評估本研究方法產生的翻譯。 關鍵詞：雙語句子對齊、文法規則、搭配詞、動介片語翻譯 6</p> <h2>Abstract</h2> <p>This thesis presents a phrases searching system, PrecisePhraseBook, which provides Chinese translations and example sentences of English phrases with a noun and preposition to assist learners in learning English or Chinese. PrecisePhraseBook provides researchers a reference tool for generating grammar rules. We propose a method for extracting Chinese translations of English phrases from bilingual parallel corpora. We use statistical methods to extract translations of nouns and prepositions from bilingual parallel corpora with sentence alignment, and then adjust the translations according to the Chinese collocations extracted from a Chinese corpus. Finally, we generate example sentences for the translations. At run-time, the user enters an English phrase with a noun and a preposition, and the system retrieves translations and example sentences from the database and presents the results to the user. The evaluation is done using randomly 30 selected phrases. We used human judge in assess the translations. Keywords: Sentence Alignment, Grammar Patterns, Collocations, Phrase Translation</p>

This thesis presents a phrases searching system, PrecisePhraseBook, which provides Chinese translations and example sentences of English phrases with a noun and preposition to assist learners in learning English or Chinese. PrecisePhraseBook provides researchers a reference tool for generating grammar rules. We propose a method for extracting Chinese translations of English phrases from bilingual parallel corpora. We use statistical methods to extract translations of nouns and prepositions from bilingual parallel corpora with sentence alignment, and then adjust the translations according to the Chinese collocations extracted from a Chinese corpus. Finally, we generate example sentences for the translations. At run-time, the user enters an English phrase with a noun and a preposition, and the system retrieves translations and example sentences from the database and presents the results to the user. The evaluation is done using randomly 30 selected phrases. We used human judge in assess the translations.

Publication:

Improving Phrase Translation Based on Sentence Alignment of Chinese-English Parallel Corpus

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