| Challenge: | Lexical borrowing happens in almost all languages, and we propose a new method to identify loanwords in Uyghur. |
| Approach: | They propose a neural network based loanword identification model for Uyghur that captures past and future information and learns both word level and character level features automatically. |
| Outcome: | The proposed model outperforms baseline models on Chinese, Arabic and Russian loanword detection in Uyghur. |
Similar Papers
Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings (C18-1)
Copied to clipboard
| Challenge: | Almost every natural language processing task suffers from data sparseness. |
| Approach: | They propose a method which identify loanwords in monolingual corpora by using cross-lingual word embeddings as core feature and a log-linear model which combines several shallow features to predict the final results. |
| Outcome: | The proposed method outperforms baseline models significantly on loanword identification and translation in four languages and eight translation directions. |
State-of-the-art Chinese Word Segmentation with Bi-LSTMs (D18-1)
Copied to clipboard
| Challenge: | A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. |
| Approach: | They propose a bidirectional LSTM model with standard deep learning techniques and best practices for the task of Chinese word segmentation. |
| Outcome: | The proposed model outperforms models based on standard deep learning techniques and best practices on Chinese word segmentation datasets. |
Automatic Speech Recognition for Uyghur through Multilingual Acoustic Modeling (2020.lrec-1)
Copied to clipboard
| Challenge: | Low-resource languages suffer from lower performance of Automatic Speech Recognition (ASR) due to the lack of data. |
| Approach: | They propose to use Turkish as donor language to train acoustic models using multilingual training to achieve more context coverage. |
| Outcome: | The proposed system performs better with multilingual training for the under-resourced Uyghur language. |
ConLoan: A Contrastive Multilingual Dataset for Evaluating Loanwords (2025.acl-long)
Copied to clipboard
Sina Ahmadi, Micha David Hess, Elena Álvarez-Mellado, Alessia Battisti, Cui Ding, Anne Göhring, Yingqiang Gao, Zifan Jiang, Andrianos Michail, Peshmerge Morad, Joel Niklaus, Maria Christina Panagiotopoulou, Stefano Perrella, Juri Opitz, Anastassia Shaitarova, Rico Sennrich
| Challenge: | Lexical borrowing is a ubiquitous linguistic phenomenon influenced by geopolitical, societal, and technological factors. |
| Approach: | They propose a novel contrastive dataset comprising sentences with and without loanwords across 10 languages to examine how machine translation and language models process loanword . |
| Outcome: | The proposed dataset shows that state-of-the-art models prefer loanwords over native terms and exhibit varying performance across languages. |
Error Analysis of Uyghur Name Tagging: Language-specific Techniques and Remaining Challenges (L18-1)
Copied to clipboard
| Challenge: | despite efforts at name tagging, there is limited understanding on the performance ceiling . despite the high-resource language, there are very few natural language processing tools available . |
| Approach: | They propose to use a machine learning model to identify Uyghur name tagger errors . they conclude that such a model is unlikely to be effective for Uygur, or low-resource languages . |
| Outcome: | The proposed model is unlikely to be effective for Uyghur, or low-resource languages in general, the authors argue . they show that the proposed model can be used for high-res languages with superficial features . |
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering (C18-1)
Copied to clipboard
| Challenge: | Sentence pair modeling is a fundamental technique underlying many NLP tasks. |
| Approach: | They analyze several neural network designs for sentence pair modeling and compare their performance extensively across eight datasets. |
| Outcome: | The proposed models perform well across eight datasets including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. |
Learning to Discover, Ground and Use Words with Segmental Neural Language Models (P19-1)
Copied to clipboard
| Challenge: | Existing models of word learning do not account for the long-range dependencies manifest in language and that are easily captured by recurrent neural networks. |
| Approach: | They propose a segmental neural language model that unifies word discovery, learning how words fit together to form sentences, and by conditioning the model on visual context, how words’ meanings ground in representations of nonlinguistic modalities. |
| Outcome: | The proposed model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and improves on both. |
A Generalized Method for Automated Multilingual Loanword Detection (2022.coling-1)
Copied to clipboard
Abhijnan Nath, Sina Mahdipour Saravani, Ibrahim Khebour, Sheikh Mannan, Zihui Li, Nikhil Krishnaswamy
| Challenge: | Loanwords are words incorporated from one language into another without translation . authors present a method to automatically detect loanwords across language pairs . |
| Approach: | They propose a method to automatically detect loanwords across language pairs . they incorporate edit distance, semantic similarity measures, phonetic alignment . |
| Outcome: | The proposed method outperforms existing methods on single-pair loanword detection tasks and can generalize to unseen language pairs with sufficient data. |
Correcting Chinese Word Usage Errors for Learning Chinese as a Second Language (C18-1)
Copied to clipboard
| Challenge: | a word usage error is the most common error type in Chinese, according to the HSK dynamic composition corpus . a system that considers both target erroneous token and context can generate a correction vector . |
| Approach: | They propose a neural network model that considers target erroneous token and context to generate a correction vector and compare it against a candidate vocabulary to propose suitable corrections. |
| Outcome: | The proposed model can detect 91% of the cases and propose suitable corrections within a list of five candidates. |
N-LTP: An Open-source Neural Language Technology Platform for Chinese (2021.emnlp-demo)
Copied to clipboard
| Challenge: | Existing tools that teach an independent model for each task are not supported in Chinese. |
| Approach: | They propose an open-source neural language platform supporting six Chinese NLP tasks . source code, documentation, and pre-trained models are available at https://github.com/hit-SCIR/ltp . |
| Outcome: | The proposed platform supports six Chinese NLP tasks. |