A First Context-Free Grammar Applied to Nawatl Corpora Augmentation

• URL: http://arxiv.org/abs/2510.04945v1
• Date: Mon, 06 Oct 2025 15:46:54 GMT
• Title: A First Context-Free Grammar Applied to Nawatl Corpora Augmentation
• Authors: Juan-José Guzmán-Landa, Juan-Manuel Torres-Moreno, Miguel Figueroa-Saavedra, Ligia Quintana-Torres, Martha-Lorena Avendaño-Garrido, Graham Ranger,
• Abstract summary: We introduce a context-free grammar (CFG) for the Nawatl language.<n>Nawatl is an Amerindian language with few digital resources.<n>We show that a grammar enables us significantly to expand a corpus in Nawatl.
• Score: 0.21498988090998952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this article we introduce a context-free grammar (CFG) for the Nawatl language. Nawatl (or Nahuatl) is an Amerindian language of the $\pi$-language type, i.e. a language with few digital resources, in which the corpora available for machine learning are virtually non-existent. The objective here is to generate a significant number of grammatically correct artificial sentences, in order to increase the corpora available for language model training. We want to show that a grammar enables us significantly to expand a corpus in Nawatl which we call $\pi$-\textsc{yalli}. The corpus, thus enriched, enables us to train algorithms such as FastText and to evaluate them on sentence-level semantic tasks. Preliminary results show that by using the grammar, comparative improvements are achieved over some LLMs. However, it is observed that to achieve more significant improvement, grammars that model the Nawatl language even more effectively are required.

Related papers

• Simultaneous Speech-to-Speech Translation Without Aligned Data [52.467808474293605]
  Simultaneous speech translation requires translating source speech into a target language in real-time.<n>We propose Hibiki-Zero, which eliminates the need for word-level alignments entirely.<n>Hibiki-Zero achieves state-of-the-art performance in translation accuracy, latency, voice transfer, and naturalness across five X-to-English tasks.
  arXiv  Detail & Related papers  (2026-02-11T17:41:01Z)
• Two CFG Nahuatl for automatic corpora expansion [0.22577070341971636]
  This article introduces two Context-Free Grammars (CFG) for Nawatl Corpora expansion.<n>Naavell is an Amerindian language (it is a National Language of Mexico) of the $$-language type.<n>The goal is to produce a substantial number of syntactically valid artificial Nawatl sentences.
  arXiv  Detail & Related papers  (2025-12-16T09:49:31Z)
• $π$-yalli: un nouveau corpus pour le nahuatl [0.8247755416642547]
  The NAHU$2$ project is a Franco-Mexican collaboration aimed at building the $pi$-YALLI corpus adapted to machine learning.<n>The $pi$-YALLI corpus will be used to develop computer resources for the Nahuatl language.
  arXiv  Detail & Related papers  (2024-12-20T12:03:10Z)
• Machine Translation by Projecting Text into the Same Phonetic-Orthographic Space Using a Common Encoding [3.0422770070015295]
  We propose an approach based on common multilingual Latin-based encodings (WX notation) that take advantage of language similarity. We verify the proposed approach by demonstrating experiments on similar language pairs. We also get up to 1 BLEU points improvement on distant and zero-shot language pairs.
  arXiv  Detail & Related papers  (2023-05-21T06:46:33Z)
• CLSE: Corpus of Linguistically Significant Entities [58.29901964387952]
  We release a Corpus of Linguistically Significant Entities (CLSE) annotated by experts. CLSE covers 74 different semantic types to support various applications from airline ticketing to video games. We create a linguistically representative NLG evaluation benchmark in three languages: French, Marathi, and Russian.
  arXiv  Detail & Related papers  (2022-11-04T12:56:12Z)
• How do lexical semantics affect translation? An empirical study [1.0152838128195467]
  A distinguishing factor of natural language is that words are typically ordered according to the rules of the grammar of a given language. We investigate how the word ordering of and lexical similarity between the source and target language affect translation performance.
  arXiv  Detail & Related papers  (2021-12-31T23:28:28Z)
• Pre-training Universal Language Representation [46.51685959045527]
  This work introduces universal language representation learning, i.e., embeddings of different levels of linguistic units or text with quite diverse lengths in a uniform vector space. We empirically verify that well designed pre-training scheme may effectively yield universal language representation.
  arXiv  Detail & Related papers  (2021-05-30T09:29:01Z)
• Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation [53.22775597051498]
  We present a continual pre-training framework on mBART to effectively adapt it to unseen languages. Results show that our method can consistently improve the fine-tuning performance upon the mBART baseline. Our approach also boosts the performance on translation pairs where both languages are seen in the original mBART's pre-training.
  arXiv  Detail & Related papers  (2021-05-09T14:49:07Z)
• GrammarTagger: A Multilingual, Minimally-Supervised Grammar Profiler for Language Education [7.517366022163375]
  We present GrammarTagger, an open-source grammar profiler which, given an input text, identifies grammatical features useful for language education. The model architecture enables it to learn from a small amount of texts annotated with spans and their labels. We also build Octanove Learn, a search engine of language learning materials indexed by their reading difficulty and grammatical features.
  arXiv  Detail & Related papers  (2021-04-07T15:31:20Z)
• VLGrammar: Grounded Grammar Induction of Vision and Language [86.88273769411428]
  We study grounded grammar induction of vision and language in a joint learning framework. We present VLGrammar, a method that uses compound probabilistic context-free grammars (compound PCFGs) to induce the language grammar and the image grammar simultaneously.
  arXiv  Detail & Related papers  (2021-03-24T04:05:08Z)
• Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation [127.81351683335143]
  Cross-lingual pretraining requires models to align the lexical- and high-level representations of the two languages. Previous research has shown that this is because the representations are not sufficiently aligned. In this paper, we enhance the bilingual masked language model pretraining with lexical-level information by using type-level cross-lingual subword embeddings.
  arXiv  Detail & Related papers  (2021-03-18T21:17:58Z)
• Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision [110.66085917826648]
  We develop a technique that extrapolates multimodal alignments to language-only data by contextually mapping language tokens to their related images. "vokenization" is trained on relatively small image captioning datasets and we then apply it to generate vokens for large language corpora. Trained with these contextually generated vokens, our visually-supervised language models show consistent improvements over self-supervised alternatives on multiple pure-language tasks.
  arXiv  Detail & Related papers  (2020-10-14T02:11:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.