Learning to match names across languages

We report on research on matching names in different scripts across languages. We explore two trainable approaches based on comparing pronunciations. The first, a cross-lingual approach, uses an automatic name-matching program that exploits rules based on phonological comparisons of the two languages carried out by humans. The second, monolingual approach, relies only on automatic comparison of the phonological representations of each pair. Alignments produced by each approach are fed to a machine learning algorithm. Results show that the monolingual approach results in machine-learning based comparison of person-names in English and Chinese at an accuracy of over 97.0 F-measure.

The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review

Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. With new deep learning techniques and the combination with other domain knowledge, offline handwritten Chinese recognition has gained breakthroughs in methods and performance in recent years. However, there have yet to be articles that provide a technical review of this field since 2016. In light of this, this paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods, methods combining deep learning with traditional techniques, and knowledge from other areas from 2016 to 2022. Firstly, it introduces the research background and status of handwritten Chinese recognition, standard datasets, and evaluation metrics. Secondly, a comprehensive summary and analysis of offline HCCR and offline HCTR approaches during the last seven years is provided, along with an explanation of their concepts, specifics, and performances. Finally, the main research problems in this field over the past few years are presented. The challenges still exist in offline handwritten Chinese recognition are discussed, aiming to inspire future research work

GNN-LM: Language Modeling based on Global Contexts via GNN

Inspired by the notion that ``{\it to copy is easier than to memorize}``, in this work, we introduce GNN-LM, which extends the vanilla neural language model (LM) by allowing to reference similar contexts in the entire training corpus. We build a directed heterogeneous graph between an input context and its semantically related neighbors selected from the training corpus, where nodes are tokens in the input context and retrieved neighbor contexts, and edges represent connections between nodes. Graph neural networks (GNNs) are constructed upon the graph to aggregate information from similar contexts to decode the token. This learning paradigm provides direct access to the reference contexts and helps improve a model's generalization ability. We conduct comprehensive experiments to validate the effectiveness of the GNN-LM: GNN-LM achieves a new state-of-the-art perplexity of 14.8 on WikiText-103 (a 3.9 point improvement over its counterpart of the vanilla LM model), and shows substantial improvement on One Billion Word and Enwiki8 datasets against strong baselines. In-depth ablation studies are performed to understand the mechanics of GNN-LM. \footnote{The code can be found at https://github.com/ShannonAI/GNN-LMComment: To appear at ICLR 202

Chinese Text Entry with Mobile Devices

Tietokoneiden ja nykyaikaisten matkapuhelimien käytön kannalta on olennaista, että niihin voidaan syöttää tekstiä tehokkaasti. Kiinan kielen eri murteita puhuu äidinkielenään noin viidesosa maailman väestöstä eli yli miljardi ihmistä. Kiinan kielen merkki- ja tavuperustaisuus tekee siitä tekstinsyötön kannalta ainutlaatuisen haastavan. Monet kiinalaisista merkeistä ovat rakenteeltaan monimutkaisia ja homofonisia (ääntyvät samalla tavoin) joidenkin muiden merkkien kanssa. Syötettäessä tekstiä näppäimistöltä tavallinen tapa on käyttää ns. pinyin-koodeja, joiden avulla kukin kiinan merkki voidaan esittää useasta latinalaisen aakkoston merkistä koostuvana koodina. Homofoniasta johtuen tarkoitettu kiinan kielen merkki joudutaan tämän jälkeen vielä valitsemaan usean vaihtoehdon joukosta, mikä tekee tekstinsyöttöprosessista vaikeampaa kuin romaanisten kielten tapauksessa. Lisäksi on otettava huomioon Kiinan eri osissa puhutut useat murteet. Kaikki nämä tekijät yhdessä tekevät kiinankielisen tekstin syötöstä tietokoneille haastavaa. Tämän väitöskirjan tavoitteena on parantaa kiinankielisen tekstin syöttötapojen käyttäjäkokemusta käytettäessä matkapuhelimia ja muita mobiililaitteita. Väitöskirjassa tutkitaan empiiristen kokeiden ja mallinnuksen avulla uusia tekstinsyöttötapoja ja niiden käyttöä. Tutkimuksen kohteena on neljä erilaista tekstinsyöttötapaa: kiinankielen käsinkirjoituksen tunnistus, pyörivän kiekon avulla tapahtuva tekstinsyöttö, mandariinikiinaan perustuva sanelu, ja numeronäppäinten avulla tapahtuva pinyin-koodien syöttö. Työssä ehdotetaan uusia tekniikoita sekä käsinkirjoituksen tunnistukseen että kiekkoa käyttävään pinyin-koodien syöttöön. Empiirisissä kokeissa osoittautui että käyttäjät pitivät uusista tekniikoista. Mandariinikiinalle on suunniteltu lyhytviestien sanelusovellus, josta on tehty kaksi käyttäjäkoetta. Myös numeronäppäinten avulla tapahtuvaa pinyin-koodien syöttöä on tutkittu kahdessa kokeessa. Ensimmäisessä kokeessa vertailtiin viittä eri menetelmää. Se tuotti suunnitteluohjeita etenkin koskien fraasien (useamman merkin kokonaisuuksien) syöttöä, tekniikkaa joka voi nopeuttaa tekstinsyöttöä. Toisen osatutkimuksen tuloksena on tekstinsyöttöä kuvaava malli, jonka avulla voidaan ennustaa menetelmän nopeutta kun syötettäessä ei tehdä virheitä. Tutkimus johti myös useisiin jatkotutkimuskysymyksiin. On tarpeen kehittää tehokkaampia menetelmiä tilanteeseen, jossa merkki joudutaan valitsemaan useista vaihtoehdoista. Kehityspotentiaalia on myös merkkien perustana olevien viivojen tunnistustavoissa sekä kosketusnäytöllä esitettyjen näppäimistöjen paremmassa hyödyntämisessä.For using computers and modern mobile phones it is essential that there are efficient methods for providing textual input. About one fifth of the world´s population, or over one billion people, speaks some variety of Chinese as their native language. Chinese has unique characteristics as a logosyllabic language. For example, many Chinese characters are complex in structure and normally homophonic with some others. With keyboards and other key-based input devices the normal approach is to use so-called pinyin input, where the Chinese characters are entered using their pinyin mark that consists of several characters in the Roman alphabet. Because of homophony this technique requires choosing the correct Chinese character from a list of posssible choices, making the input process more complicated than in Roman languages. Moreover, the many varieties of the language in different parts of China have to be taken into account as well. All above factors bring new challenges to the design and evaluation of Chinese text entry methods in computing systems. The overall objective of this dissertation is to improve user experience of Chinese text entry on mobile devices. To achieve the goal, the author explores new interaction solutions and patterns of user behavior in the Chinese text entry process with various approaches including empirical studies and performance modeling. The work covers four means of Chinese text entry on mobile devices: Chinese handwriting recognition, Chinese indirect text entry with a rotator, Mandarin dictation, and Chinese pinyin input methods with a 12-key keypad. New design solutions for Chinese handwriting recognition and pinyin methods utilizing a rotator are proposed and proved being well accepted by users with empirical studies. A Mandarin short message dictation application for mobile phones is also presented , with two associated studies on human factors. Two studies were also carried out on Chinese pinyin input methods that are based on the 12-key keypad. The comparative study of five phrasal pinyin input methods led to design guidelines for the advanced feature of phrasal input. The second study of pinyin input methods produced a predictive model addressing users´ error-free speeds. Based on the conclusions from studies in this thesis, several additional research questions were identified for the future. For example, improvements are necessary to promote user performance on target selection process in Chinese text entry on mobile devices. Moreover, design and studies on stroke methods and Chinese specific soft keyboards are also required

Language Modelling Approaches to Adaptive Machine Translation

Consistency is a key requirement of high-quality translation. It is especially important to adhere to pre-approved terminology and adapt to corrected translations in domain-specific projects. Machine translation (MT) has achieved significant progress in the area of domain adaptation. However, in-domain data scarcity is common in translation settings, due to the lack of specialised datasets and terminology, or inconsistency and inaccuracy of available in-domain translations. In such scenarios where there is insufficient in-domain data to fine-tune MT models, producing translations that are consistent with the relevant context is challenging. While real-time adaptation can make use of smaller amounts of in-domain data to improve the translation on the fly, it remains challenging due to supported context limitations and efficiency constraints. Large language models (LLMs) have recently shown interesting capabilities of in-context learning, where they learn to replicate certain input-output text generation patterns, without further fine-tuning. Such capabilities have opened new horizons for domain-specific data augmentation and real-time adaptive MT. This work attempts to address two main relevant questions: 1) in scenarios involving human interaction and continuous feedback, can we employ language models to improve the quality of adaptive MT at inference time? and 2) in the absence of sufficient in-domain data, can we use pre-trained large-scale language models to improve the process of MT domain adaptation?Comment: PhD thesi