SimplifyUR: Unsupervised Lexical Text Simplification for Urdu
This paper presents the first attempt at Automatic Text Simplification (ATS) for Urdu, the language of 170 million people worldwide. Being a low-resource language in terms of standard linguistic resources, recent text simplification approaches that rely on manually crafted simplified corpora or lexicons such as WordNet are not applicable to Urdu. Urdu is a morphologically rich language that requires unique considerations such as proper handling of inflectional case and honorifics. We present an unsupervised method for lexical simplification of complex Urdu text. Our method only requires plain Urdu text and makes use of word embeddings together with a set of morphological features to generate simplifications. Our system achieves a BLEU score of 80.15 and SARI score of 42.02 upon automatic evaluation on manually crafted simplified corpora. We also report results for human evaluations for correctness, grammaticality, meaning-preservation and simplicity of the output. Our code and corpus are publicly available to make our results reproducible.
Code (0)
등록된 구현이 없습니다.
Tasks
Lexical SimplificationText SimplificationWord EmbeddingsSimilar Papers 제목 키워드 기반
Developing a Monolingual Sentence Simplification Corpus for Urdu
Complex sentences are a hurdle in the learning process of language learners. Sentence simplification aims to convert a complex sentence into its simpler form such that it is easily comprehensible. To build such automated…
SentenceText SimplificationAutomatic Sentence Simplification in Low Resource Settings for Urdu
To build automated simplification systems, corpora of complex sentences and their simplified versions is the first step to understand sentence complexity and enable the development of automatic text simplification system…
SentenceText SimplificationAn Unsupervised Method for Discovering Lexical Variations in Roman Urdu Informal Text
MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders
In this paper we present our contribution to the TSAR-2022 Shared Task on Lexical Simplification of the EMNLP 2022 Workshop on Text Simplification, Accessibility, and Readability. Our approach builds on and extends the u…
Language ModelingLanguage ModellingLexical SimplificationNatural Language Inference+3Unsupervised Lexical Simplification with Context Augmentation
We propose a new unsupervised lexical simplification method that uses only monolingual data and pre-trained language models. Given a target word and its context, our method generates substitutes based on the target conte…
Lexical Simplification