Papers with native language identification
Automated essay scoring with string kernels and word embeddings (P18-2)
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| Challenge: | Existing approaches to automatic essay scoring use low-level character n-gram features. |
| Approach: | They propose to combine string kernels and word embeddings for automatic essay scoring. |
| Outcome: | The proposed method outperforms state-of-the-art deep learning methods in Arabic dialect identification and native language identification tasks. |
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set (D18-1)
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| Challenge: | Recent studies have demonstrated remarkable performance in text classification tasks such as Arabic dialect identification. |
| Approach: | They propose two approaches to improve string kernels' accuracy in Arabic and English . first approach interprets pairwise string kernel similarities between training and test sets as features . second approach adapts to training set and adds test samples for another round of training . |
| Outcome: | The proposed methods improve English polarity classification and Arabic dialect identification. |
Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions (2020.acl-main)
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| Challenge: | a growing demand for the ability to communicate in English means automated tutoring and assessment systems are becoming more popular. |
| Approach: | They propose to use automatic speech recognition transcripts to grade spontaneous speech based on textual features. |
| Outcome: | The proposed system improves on a transformer encoder with native language identification as an auxiliary task. |
Punctuation as Native Language Interference (C18-1)
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| Challenge: | Numerous aspects of written language have been studied for native language identification (NLI) but its impact has not been studied. |
| Approach: | They propose to use punctuation marks as indicators of native language . they propose to apply them to native language identification . |
| Outcome: | The proposed methods support the hypothesis that punctuation marks are persistent and robust indicators of the native language of the author, even when a high proficiency level in a non-native language is achieved. |
Native Language Identification with User Generated Content (D18-1)
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| Challenge: | Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task. |
| Approach: | They propose to use linguistically-motivated features and social media characteristics to obtain high accuracy on this task. |
| Outcome: | The proposed method is highly accurate on a social media content where authors are highly-fluent nonnative speakers. |
Robust Native Language Identification through Agentic Decomposition (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) often achieve high performance by leveraging superficial contextual clues rather than the underlying linguistic patterns indicative of native language (L1) influence. |
| Approach: | They propose an agentic NLI pipeline where specialized agents accumulate and categorize diverse linguistic evidence before an independent final assessment. |
| Outcome: | The proposed pipeline significantly improves robustness against misleading contextual clues and performance consistency compared to standard prompting methods. |
Topics to Avoid: Demoting Latent Confounds in Text Classification (D19-1)
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| Challenge: | Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. |
| Approach: | They propose a method that represents latent topical confounds and a model which “unlearns” confounding features by predicting both the label of the input text and the confound. |
| Outcome: | The proposed model generalizes better and learns features indicative of the writing style rather than the content. |