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We propose methods for transliterating English loanwords in Japanese from their Japanese written form (katakana/romaji) to their original English written form. Our data is a Japanese-English loanwords dictionary that we have created ourselves. We employ two approaches: direct transliteration, which directly converts words from katakana to English, and indirect transliteration, which utilizes the English pronunciation as a means to convert katakana words into their corresponding English sound representations, which are subsequently converted into English words. Additionally, we compare the effectiveness of using katakana versus romaji as input characters. We develop 6 models of 2 types for our experiments: one with an English lexicon-filter, and the other without. For each type, we built 3 models, including a pair n-gram based on WFSTs and two sequence-to-sequence models leveraging LSTM and transformer. Our best performing model was the pair n-gram model with a lexicon- filter, directly transliterating from katakana to English.

ACL 2023

Back-transliteration of English loanwords in Japanese

loaword

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japanese

workshop paper

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Japanese writing is a complex system, and a large part of the complexity resides in the use of kanji. A single kanji character in modern Japanese may have multiple pronunciations, either as native vocabulary or as words borrowed from Chinese. This causes a problem for text-to-speech synthesis (TTS) because the system has to predict which pronunciation of each kanji character is appropriate in the context. The problem is called homograph disambiguation. To solve the problem, this research provides a new annotated Japanese single kanji character pronunciation data set and describes an experiment using the logistic regression (LR) classifier. A baseline is computed to compare with the LR classifier accuracy. This experiment provides the first experimental research in Japanese single kanji homograph disambiguation. The annotated Japanese data is freely released to the public to support further work.

Pronunciation ambiguities in Japanese kanji

A numeration system encodes abstract numeric quantities as concrete strings of written characters. The numeration systems used by modern scripts tend to be precise and unambiguous, but this was not so for the ancient and partially-deciphered proto-Elamite (PE) script, where written numerals can have up to four distinct readings depending on the system that is used to read them. We consider the task of disambiguating between these readings in order to determine the values of the numeric quantities recorded in this corpus. We algorithmically extract a list of possible readings for each PE numeral notation, and contribute two disambiguation techniques based on structural properties of the original documents and classifiers learned with the bootstrapping algorithm. We also contribute a test set for evaluating disambiguation techniques, as well as a novel approach to cautious rule selection for bootstrapped classifiers. Our analysis confirms existing intuitions about this script and reveals previously-unknown correlations between tablet content and numeral magnitude. This work is crucial to understanding and deciphering PE, as the corpus is heavily accounting focused and contains many more numeric tokens than tokens of text.

Disambiguating numeral sequences to decipher ancient accounting corpora

This paper presents a new approach to the ancient scripts decipherment problem based on combinatorial optimisation and coupled simulated annealing, an advanced non-convex optimisation procedure. Solutions are encoded by using k-permutations allowing for null, one-to-many, and many-to-one mappings between signs.
The proposed system is able to produce enhanced results in cognate identification when compared to the state-of-the-art systems on standard evaluation benchmarks used in literature.

Decipherment of lost ancient scripts as combinatorial optimisation using coupled simulated annealing

A crucial step in deciphering a text is to identify what set of characters were used to write it. This requires grouping character tokens according to visual and contextual features, which can be challenging for human analysts when the number of tokens or underlying types is large. Prior work has shown that this process can be automated by clustering dense representations of character images, in a task which we call “script clustering”. In this work, we present novel architectures which exploit varying degrees of contextual and visual information to learn representations for use in script clustering. We evaluate on a range of modern and ancient scripts, and find that our models produce representations which are more effective for script recovery than the current state-of-theart, despite using just 2% as many parameters. Our analysis fruitfully applies these models to assess hypotheses about the character inventory of the partially-deciphered proto-Elamite script.

Learning the character inventories of undeciphered scripts using unsuperivsed deep clustering

Handwritten texts produced by young learn- ers often contain orthographic features like spelling errors, capitalization errors, punctuation errors, and impurities such as strikethroughs, inserts, and smudges. All of those are typically normalized or ignored in existing transcriptions. For applications like handwriting recognition with the goal of automatically analyzing a learner’s language performance, however, retaining such features would be necessary. To address this, we present transcription guidelines that retain the features addressed above. Our guidelines were developed iteratively and include numerous example images to illustrate the various issues. On a subset of about 90 double-transcribed texts, we compute inter-annotator agreement and show that our guidelines can be applied with high levels of percentage agreement of about .98. Overall, we transcribed 1,350 learner texts, which is about the same size as the widely adopted handwriting recognition datasets IAM (1,500 pages) and CVL (1,600 pages). Our final corpus can be used to train a handwriting recognition system that transcribes closely to the real productions by young learners. Such a system is a prerequisite for applying automatic orthography feedback systems to handwritten texts in the future.

Preserving the authenticity of handwritten learner language: annotation guidelines for creating transcripts retaining orthographic features

We investigate how well words in the polysynthetic language Inuktitut can be translated by combining dictionary definitions, without use of a neural machine translation model trained on parallel text. Such a translation system would allow natural language technology to benefit from resources designed for community use in a language revitalization or education program, rather than requiring a separate parallel corpus. We show that the text-to-text generation capabilities of GPT-3 allow it to perform this task with BLEU scores of up to 18.5. We investigate prompting GPT-3 to provide multiple translations, which can help slightly, and providing it with grammar information, which is mostly ineffective. Finally, we test GPT-3's ability to derive morpheme definitions from whole-word translations, but find this process is prone to errors including hallucinations.

Translating a low-resource language using GPT-3 and a human-readable dictionary

Revisiting and Amending Central Kurdish Data on UniMorph 4.0

Linguistic analysis is a core task in the process of documenting, analyzing, and describing endangered and less-studied languages. 
In addition to providing insight into the properties of the language being studied, having tools to automatically label words in a language for the grammatical category and morphological features can support a range of applications useful for language pedagogy and revitalization.
At the same time, most modern NLP methods for these tasks require both large amounts of data in the language and compute costs well beyond the capacity of most research groups and language communities.

Here, we present a gloss-to-gloss (g2g) model for linguistic analysis (specifically, morphological analysis and part-of-speech tagging) that is lightweight in terms of both data requirements and computational expense.
The model is designed for the interlinear glossed text (IGT) format, in which we expect the source text of a sentence in a low-resource language, a translation of that sentence into a language of wider communication, and a detailed glossing of the morphological properties of each word in the sentence.

We test the model on both low-resource and high-resource languages and find that our simple CNN-based model achieves comparable performance to a state-of-the-art transformer-based model, at a fraction of the computational cost.

Lightweight morpheme labeling in context: Using structured linguistic representations to support linguistic analysis for the language documentation context

The process of lexical blending is difficult to reliably predict. This difficulty has been shown by machine learning approaches in blend modeling, including attempts using then state-of-the-art LSTM deep neural networks trained on character embeddings, which were able to predict lexical blends given the ordered constituent words in less than half of cases, at maximum. This project introduces a novel model architecture which dramatically increases the correct prediction rates for lexical blends, using only Polynomial regression and Random Forest models. This is achieved by generating multiple possible blend candidates for each input word pairing and evaluating them based on observable linguistic features. The success of this model architecture illustrates the potential usefulness of observable linguistic features for problems that elude more advanced models which utilize only features discovered in the latent space.

Improving Automated Prediction of English Lexical Blends Through the Use of Observable Linguistic Features

Colexification refers to the linguistic phe- nomenon where a single lexical form is used to convey multiple meanings. By studying cross-lingual colexifications, researchers have gained valuable insights into fields such as psycholinguistics and cognitive sciences (Jack- son et al., 2019; Xu et al., 2020; Karjus et al., 2021; Schapper and Koptjevskaja-Tamm, 2022; François, 2022). While several multilingual colexification datasets exist, there is untapped potential in using this information to bootstrap datasets across such semantic features. In this paper, we aim to demonstrate how colexifica- tions can be leveraged to create such cross- lingual datasets. We showcase curation pro- cedures which result in a dataset covering 142 languages across 21 language families across the world. The dataset includes ratings of concreteness and affectiveness, mapped with phonemes and phonological features. We fur- ther analyze the dataset along different di- mensions to demonstrate potential of the pro- posed procedures in facilitating further inter- disciplinary research in psychology, cogni- tive science, and multilingual natural language processing (NLP). Based on initial investiga- tions, we observe that i) colexifications that are closer in concreteness/affectiveness are more likely to colexify; ii) certain initial/last phonemes are significantly correlated with con- creteness/affectiveness intra language families, such as /k/ as the initial phoneme in both Tur- kic and Tai-Kadai correlated with concreteness, and /p/ in Dravidian and Sino-Tibetan corre- lated with Valence; iii) the type-to-token ratio (TTR) of phonemes are positively correlated with concreteness across several language fami- lies, while the length of phoneme segments are negatively correlated with concreteness; iv) cer- tain phonological features are negatively corre- lated with concreteness across languages. The dataset is made public online for further re- search1 .


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