Mexico

In Japanese, loanwords are primarily written in Katakana, a syllabic writing system, based on their pronunciation. However, the transliterated loanwords often exhibit spelling variations, such as the word &quot;Hepburn&quot; being written as &quot;ヘボン (hebon)&quot;, &quot;ヘプバーン (hepubaan)&quot;, &quot;ヘップバーン (heppubaan)&quot;. These orthographical variants pose a bottleneck in multilingual Named Entity Recognition (NER), because named entities (NEs) do not have one-to-one matches. In this study, we introduce a rule-based grapheme-to-phoneme (G2P) system for Japanese based on literature in linguistics and a large-scale multilingual NE dataset with annotations of the International Phonetic Alphabet (IPA), focusing on IPA to address the Katakana spelling variations in loanwords. These rules and dataset are expected to be beneficial for tasks such as NE aggregation, G2P system, construction of cross-lingual language models, and entity linking. We hope our work advances research on Japanese NER with multilingual loanwords by solving the spelling ambiguities.

NAACL 2024

Japanese Rule-based Grapheme-to-phoneme Conversion System and Multilingual Named Entity Dataset

rule-based g2p (grapheme-to-phoneme)

international phonetic alphabet (ipa)

multilingual dataset

named entity

japanese

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

In Japanese, loanwords are primarily written in Katakana, a syllabic writing system, based on their pronunciation. However, the transliterated loanwords often exhibit spelling variations, such as the word "Hepburn" being written as "ヘボン (hebon)", "ヘプバーン (hepubaan)", "ヘップバーン (heppubaan)". These orthographical variants pose a bottleneck in multilingual Named Entity Recognition (NER), because named entities (NEs) do not have one-to-one matches. In this study, we introduce a rule-based grapheme-to-phoneme (G2P) system for Japanese based on literature in linguistics and a large-scale multilingual NE dataset with annotations of the International Phonetic Alphabet (IPA), focusing on IPA to address the Katakana spelling variations in loanwords. These rules and dataset are expected to be beneficial for tasks such as NE aggregation, G2P system, construction of cross-lingual language models, and entity linking. We hope our work advances research on Japanese NER with multilingual loanwords by solving the spelling ambiguities.

workshop paper

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
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Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
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2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

How well does naturally-occurring digital text, such as Tweets, represent sub-dialects of Egyptian Arabic\,(EA)? This paper focuses on two EA sub-dialects: Cairene Egyptian Arabic (CEA) and Sa'idi Egyptian Arabic (SEA). We use morphological markers from ground-truth dialect surveys as a distance measure across four geo-referenced datasets. Results show that CEA markers are prevalent as expected in CEA geo-referenced tweets, while SEA markers are limited across SEA geo-referenced tweets. SEA tweets instead show a prevalence of CEA markers and higher usage of Modern Standard Arabic. We conclude that corpora intended to represent sub-dialects of EA do not accurately represent sub-dialects outside of the Cairene variety. This finding calls into question the validity of relying on tweets alone to represent dialectal differences.

How Well Do Tweets Represent Sub-Dialects of Egyptian Arabic?

Chavacano is a Spanish Creole widely spoken in the southern regions of the Philippines. It is one of the many Philippine languages yet to be studied computationally. This paper presents the development of a language identification model of Chavacano to distinguish it from languages that influence its creolization using character convolutional networks. Unlike studies that discriminated similar languages based on geographical proximity, this paper reports a similarity focused on the creolization of a language. We established the similarity of Chavacano and its related languages, Spanish, Portuguese, Cebuano, and Hiligaynon, from the number of common words in the corpus for all languages. We report an accuracy of 93% for the model generated using ten filters with a filter width of 5. The training experiments reveal that increasing the filter width, number of filters, or training epochs is unnecessary even if the accuracy increases because the generated models present irregular learning behavior or may have already been overfitted. This study also demonstrates that the character features extracted from convolutional neural networks, similar to n-grams, are sufficient in identifying Chavacano. Future work on the language identification of Chavacano includes improving classification accuracy for short or code-switched texts for practical applications such as social media sensors for disaster response and management.

Language Identification of Philippine Creole Spanish: Discriminating Chavacano from Related Languages

Whisper is a state-of-the-art automatic speech recognition (ASR) model (Radford et al., 2022). Although Swiss German dialects are allegedly not part of Whisper's training data, preliminary experiments showed Whisper can transcribe Swiss German quite well, with the output being a speech translation into Standard German. To gain a better understanding of Whisper's performance on Swiss German, we systematically evaluate it using automatic, qualitative, and human evaluation. We test its performance on three existing test sets: SwissDial (Dogan-Schönberger et al., 2021), STT4SG-350 (Plüss et al., 2023), and Swiss Parliaments Corpus (Plüss et al., 2021). In addition, we create a new test set for this study based on short mock clinical interviews. 

To automatically evaluate performance, we used word error rate (WER) and BLEU. We also conducted a qualitative analysis of Whisper's performance, discussing its strengths and weaknesses. Finally, 28 people participated in a survey evaluating Whisper's performance. 

All of our evaluations showed that Whisper is a viable ASR system for Swiss German, so long as the Standard German output is desired.

Does Whisper Understand Swiss German? An Automatic, Qualitative, and Human Evaluation.

Effectively normalizing spellings in textual data poses a considerable challenge, especially for low-resource languages lacking standardized writing systems. In this study, we fine-tuned a multilingual model with data from several Occitan dialects and conducted a series of experiments to assess the model's representations of these dialects. For evaluation purposes, we compiled a parallel lexicon encompassing four Occitan dialects.
Intrinsic evaluations of the model's embeddings revealed that surface similarity between the dialects strengthened representations. When the model was further fine-tuned for part-of-speech tagging, its performance was robust to dialectical variation, even when trained solely on part-of-speech data from a single dialect. Our findings suggest that large multilingual models minimize the need for spelling normalization during pre-processing.

Modeling Orthographic Variation in Occitan's Dialects

This work addresses the task of identifying English code-switching in multilingual text. We train two token-level classifiers on data of high-resource language pairs. The first distinguishes between English, not English, morphologically mixed, and other words. The second is a binary classifier that identifies named entities. Results indicate that our system is on-par with SoTA for high-resource language pairs. Meanwhile we show that on low-resource language pairs not in the training data our system outperforms SoTA by between 2.31 and 4.59\% $F_1$. We also analyse the correlation between typological similarity of the languages and difficulty in recognizing code-switching. Our system is a new strong baseline system for code-switching research between any language and English.

Multilingual Identification of English Code-Switching

This paper aims to assess the role of multiword compound adverbs in distinguishing Brazilian Portuguese (PT-BR) from European Portuguese (PT-PT). Two key factors underpin this focus: Firstly, multiword expressions often provide less ambiguity compared to single words, even when their meaning is idiomatic (non-compositional). Secondly, despite constituting a significant portion of lexicons in many languages, they are frequently overlooked in Natural Language Processing, possibly due to their heterogeneous nature and lexical range.
For this study, a large lexicon of Portuguese multiword adverbs (3,665) annotated with diatopic information regarding language variety was utilized. 
The paper investigates the distribution of this category in a corpus consisting in excerpts from journalistic texts sourced from the DSL (Dialect and Similar Language) corpus, representing Brazilian (PT-BR) and European Portuguese (PT-PT), respectively, each partition containing 18,000 sentences.
Results indicate a substantial similarity between the two varieties, with a considerable overlap in the lexicon of multiword adverbs. Additionally, specific adverbs unique to each language variety were identified. 
Lexical entries recognized in the corpus represent 18.2% (PT-BR) to 19.5% (PT-PT) of the lexicon, and approximately 5,700 matches in each partition. While many of the matches are spurious due to ambiguity with otherwise non-idiomatic, free strings, occurrences of adverbs marked as exclusive to one variety in texts from the other variety are rare.

The Role of Adverbs in Language Variety Identification: The case of Portuguese Multiword Adverbs

Spanish is an official language in 20 countries; in 19 of them, it arrived by means of overseas colonisation. Its close contact with several coexistent languages and the rich regional and cultural diversity has produced varieties which divert from each other. We study these divergences in a data-based approach and according to their qualitative and quantitative effects in word embeddings. We generate embeddings for Spanish in 24 countries and examine the topology of the spaces. Due to the similarities between varieties —in contrast to what happens to different languages in bilingual topological studies— we first scrutinise the behaviour of three isomorphism measures in (quasi-)isomorphic settings: relational similarity, Eigenvalue similarity and Gromov-Hausdorff distance. We then use the most trustworthy measure to quantify the divergences among varieties. Finally, we use the departures from isomorphism to build relational trees for the Spanish varieties by hierarchical clustering.

When Elote, Choclo and Mazorca are not the same. Isomorphism-based Perspective to the Spanish Varieties Divergences

The paper presents new causal commonsense reasoning datasets for South Slavic dialects, based on the Choice of Plausible Alternatives (COPA) dataset. The dialectal datasets are built by translating by native dialect speakers from the English original and the corresponding standard translation. Three dialects are covered -- the Cerkno dialect of Slovenian, the Chakavian dialect of Croatian and the Torlak dialect of Serbian. The datasets are the first resource for evaluation of large language models on South Slavic dialects, as well as among the first commonsense reasoning datasets on dialects overall. The paper describes specific challenges met during the translation process. A comparison of the dialectal datasets with their standard language counterparts shows a varying level of character-level, word-level and lexicon-level deviation of dialectal text from the standard datasets. The observed differences are well reproduced in initial zero-shot and 10-shot experiments, where the Slovenian Cerkno dialect and the Croatian Chakavian dialect show significantly lower results than the Torlak dialect. These results show also for the dialectal datasets to be significantly more challenging than the standard datasets. Finally, in-context learning on just 10 examples shows to improve the results dramatically, especially for the dialects with the lowest results.

DIALECT-COPA: Extending the Standard Translations of the COPA Causal Commonsense Reasoning Dataset to South Slavic Dialects

This paper presents a new textual resource for Norwegian and its dialects. The NoMusic corpus contains Norwegian translations of the xSID dataset, an evaluation dataset for spoken language understanding (slot and intent detection). The translations cover Norwegian Bokmål, as well as eight dialects from three of the four major Norwegian dialect areas. To our knowledge, this is the first multi-parallel resource for written Norwegian dialects, and the first evaluation dataset for slot and intent detection focusing on non-standard Norwegian varieties. In this paper, we describe the annotation process and provide some analyses on the types of linguistic variation that can be found in the dataset.

NoMusic - The Norwegian Multi-Dialectal Slot and Intent Detection Corpus

The VLP team participated in the DSL-ML shared task of the VarDial 2024 workshop which aims to distinguish texts in similar languages. This paper presents our approach to solving the problem and discusses our experimental and official results. We propose to integrate semantics-aware word embeddings which are learned from ConceptNet into a bidirectional long short-term memory network. This approach achieves good performance – our sys- tem is ranked in the top two or three of the best performing teams for the task.

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How Well Do Tweets Represent Sub-Dialects of Egyptian Arabic?

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Back-transliteration of English loanwords in Japanese

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How Well Do Tweets Represent Sub-Dialects of Egyptian Arabic?

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Back-transliteration of English loanwords in Japanese

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