Modeling Orthographic Variation to Improve NLP Performance for Nigerian Pidgin

Core Concepts

Modeling orthographic variation in Nigerian Pidgin, an English-derived contact language, can improve performance on critical NLP tasks such as sentiment analysis and machine translation.

Abstract

The content discusses the issue of orthographic variation in Nigerian Pidgin, an English-derived contact language spoken by approximately 100 million people. Nigerian Pidgin is traditionally an oral language and lacks a standardized orthography, resulting in a large proportion of orthographic variations in written texts. These variations contribute to the underperformance of NLP models on critical tasks such as sentiment analysis and machine translation.
The authors first provide an analysis of the various types of orthographic variations commonly found in Nigerian Pidgin texts, including alternation between similar sounds, conversion of digraphs, phonetic transcription of letter pairings, and deletion of silent letters. These variations often occur at specific positions within a word and have phonetic origins.
The authors then propose a phonetic-theoretic framework for word editing, which can be used to generate orthographic variations to augment training data. The framework involves transcribing the text into phonemes, aligning characters and phonemes, synthesizing variations based on identified rules, and sampling the variations based on phonological distance.
The authors test the effect of this data augmentation on two NLP tasks: sentiment analysis and machine translation. The results demonstrate that augmenting the training data with a combination of real texts from other corpora and synthesized orthographic variations leads to performance improvements of 2.1 points in sentiment analysis and 1.4 BLEU points in translation to English.
The authors also discuss the limitations of their approach, including the issue of overgeneration of implausible variations and the need for further exploration of alternative data-driven sampling methods.

Stats

Nigerian Pidgin has approximately 100 million speakers.
The Bible, JW300, and Naija Treebank datasets were used in the analysis and experiments.
The sentiment analysis task used the NaijaSenti dataset, which has 8.8K samples.
The machine translation task used the JW300 dataset, which has 20.2K parallel samples.

Quotes

"Nigerian Pidgin is a predominantly spoken language, without a normalized orthography in place."
"Orthographic variations contribute to a significant under-performance in critical tasks, such as sentiment analysis and machine translation."
"Our results demonstrate the positive effect of augmenting the training data with a combination of real texts from other corpora as well as synthesized orthographic variation."

Key Insights Distilled From

Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin

by Pin-Jie Lin,... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18264.pdf

Deeper Inquiries

How can the proposed framework be extended to other Pidgin or creole languages that lack standardized orthographies?

The proposed framework for generating orthographic variations in Nigerian Pidgin can be extended to other Pidgin or creole languages by adapting the phonological-based approach to the specific characteristics of those languages. Since many creole and pidgin languages rely on phonetic writing systems similar to Nigerian Pidgin, the framework can be adjusted to capture the phonetic properties of the target language. By analyzing the common orthographic variations in texts from the specific language, similar to the analysis done for Nigerian Pidgin, researchers can develop variation rules based on phonetic patterns. Additionally, utilizing phoneme-to-grapheme alignment tools and phonemizers specific to the target language can aid in transcribing words into phonemes accurately. By incorporating language-specific phonetic rules and characteristics, the framework can effectively generate orthographic variations for other Pidgin or creole languages.

What are the potential challenges in applying this approach to languages that are not English-lexified?

Applying the proposed approach to languages that are not English-lexified may present several challenges. One major challenge is the availability of resources and tools tailored to those specific languages. Since the framework relies on phonological properties and phoneme-to-grapheme alignment, languages with different phonetic structures and writing systems may require custom tools and models for accurate transcription and variation generation. Additionally, the lack of standardized orthographies in non-English-lexified languages may complicate the identification and analysis of orthographic variations, as there may be greater variability and diversity in spelling conventions. Furthermore, the overgeneration of implausible variations could be more pronounced in languages with complex phonetic systems or unique orthographic rules, requiring careful tuning and validation to ensure the generated variations are linguistically plausible.

Could the overgeneration of implausible variations be further mitigated through the incorporation of language-specific knowledge or user feedback?

The overgeneration of implausible variations can be mitigated through the incorporation of language-specific knowledge and user feedback. By leveraging linguistic expertise and native speakers' insights, researchers can refine the variation rules to align more closely with the linguistic norms and conventions of the target language. Language-specific knowledge can help in identifying patterns of orthographic variation that are common and plausible in the language, reducing the likelihood of generating unrealistic variations. User feedback, especially from native speakers or language experts, can provide valuable input on the authenticity and naturalness of the generated variations. Iterative refinement based on user feedback can help fine-tune the variation generation process and improve the quality of the augmented data. Incorporating language-specific knowledge and user feedback ensures that the generated variations are linguistically accurate and contextually appropriate for the target language.