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Register a new userMultilingual Sequence Labeling Approach to solve Lexical Normalization
نهج وضع التسلسل متعدد اللغات لحل التطبيع المعجمي
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تعرف مهمة تحويل نص غير قياسي إلى نص قياسي وقابل للقراءة باسم التطبيع المعجمي. تتطلب جميع تطبيقات معالجة اللغة الطبيعية تقريبا (NLP) البيانات النصية في النموذج الطبيعي لإنشاء نماذج محددة ذات جودة عالية. وبالتالي، فقد ثبت التطبيع المعجمي لتحسين أداء العديد من مهام معالجة اللغة الطبيعية على وسائل التواصل الاجتماعي. تهدف هذه الدراسة إلى حل مشكلة التطبيع المعجمي من خلال صياغة مهمة التطبيع المعجمية مشكلة وضع علامة تسلسل. تقترح هذه الورقة نهج وضع علامة تسلسل لحل مشكلة التطبيع المعجمي في تركيبة مع تقنية محاذاة الكلمة. الهدف هو استخدام نموذج واحد لتطبيع النص باللغات المختلفة وهي الكرواتية والدنماركية والهولندية والإنجليزية والإندونيسية والإنجليزية والألمانية والإيطالية والصربية والسلوفينية والإسبانية والتركية والتركية والألمانية والألمانية. هذه مهمة مشتركة في عام 2021 ورشة العمل السابعة حول النص الناتج عن المستخدم الصاخب (W-NUT) "" من المتوقع أن يقوم المشاركون بإنشاء نظام / نموذج يقوم بتنفيذ التطبيع المعجمي، وهو ترجمة النصوص غير القانونية في تعادلهم الكنسي، الذين يشتملون على بيانات من أكثر من 12 لغة. يحقق النموذج المتعدد اللغوي المقترح نتيجة ERS الإجمالية من 43.75 بشأن التقييم الجوهري ونتيجة إجمالي درجة المرفقات (LAS) من 63.12 على التقييم الخارجي. علاوة على ذلك، تحقق الطريقة المقترحة أعلى نقاط معدل تخفيض الأخطاء (ERR) من 61.33 من بين المشاركين في المهمة المشتركة. تسلط هذه الدراسة الضوء على آثار استخدام بيانات تدريب إضافية للحصول على نتائج أفضل وكذلك استخدام نموذج لغة مدرب مسبقا تدرب على لغات متعددة بدلا من لغة واحدة فقط.
The task of converting a nonstandard text to a standard and readable text is known as lexical normalization. Almost all the Natural Language Processing (NLP) applications require the text data in normalized form to build quality task-specific models. Hence, lexical normalization has been proven to improve the performance of numerous natural language processing tasks on social media. This study aims to solve the problem of Lexical Normalization by formulating the Lexical Normalization task as a Sequence Labeling problem. This paper proposes a sequence labeling approach to solve the problem of Lexical Normalization in combination with the word-alignment technique. The goal is to use a single model to normalize text in various languages namely Croatian, Danish, Dutch, English, Indonesian-English, German, Italian, Serbian, Slovenian, Spanish, Turkish, and Turkish-German. This is a shared task in 2021 The 7th Workshop on Noisy User-generated Text (W-NUT)'' in which the participants are expected to create a system/model that performs lexical normalization, which is the translation of non-canonical texts into their canonical equivalents, comprising data from over 12 languages. The proposed single multilingual model achieves an overall ERR score of 43.75 on intrinsic evaluation and an overall Labeled Attachment Score (LAS) score of 63.12 on extrinsic evaluation. Further, the proposed method achieves the highest Error Reduction Rate (ERR) score of 61.33 among the participants in the shared task. This study highlights the effects of using additional training data to get better results as well as using a pre-trained Language model trained on multiple languages rather than only on one language.
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Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for soci
al media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system.
We present the winning entry to the Multilingual Lexical Normalization (MultiLexNorm) shared task at W-NUT 2021 (van der Goot et al., 2021a), which evaluates lexical-normalization systems on 12 social media datasets in 11 languages. We base our solut
ion on a pre-trained byte-level language model, ByT5 (Xue et al., 2021a), which we further pre-train on synthetic data and then fine-tune on authentic normalization data. Our system achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. The source code is released at https://github.com/ufal/multilexnorm2021 and the fine-tuned models at https://huggingface.co/ufal.
Social media is notoriously difficult to process for existing natural language processing tools, because of spelling errors, non-standard words, shortenings, non-standard capitalization and punctuation. One method to circumvent these issues is to nor
malize input data before processing. Most previous work has focused on only one language, which is mostly English. In this paper, we are the first to propose a model for cross-lingual normalization, with which we participate in the WNUT 2021 shared task. To this end, we use MoNoise as a starting point, and make a simple adaptation for cross-lingual application. Our proposed model outperforms the leave-as-is baseline provided by the organizers which copies the input. Furthermore, we explore a completely different model which converts the task to a sequence labeling task. Performance of this second system is low, as it does not take capitalization into account in our implementation.
Current benchmark tasks for natural language processing contain text that is qualitatively different from the text used in informal day to day digital communication. This discrepancy has led to severe performance degradation of state-of-the-art NLP m
odels when fine-tuned on real-world data. One way to resolve this issue is through lexical normalization, which is the process of transforming non-standard text, usually from social media, into a more standardized form. In this work, we propose a sentence-level sequence-to-sequence model based on mBART, which frames the problem as a machine translation problem. As the noisy text is a pervasive problem across languages, not just English, we leverage the multi-lingual pre-training of mBART to fine-tune it to our data. While current approaches mainly operate at the word or subword level, we argue that this approach is straightforward from a technical standpoint and builds upon existing pre-trained transformer networks. Our results show that while word-level, intrinsic, performance evaluation is behind other methods, our model improves performance on extrinsic, downstream tasks through normalization compared to models operating on raw, unprocessed, social media text.
This paper describes the HEL-LJU submissions to the MultiLexNorm shared task on multilingual lexical normalization. Our system is based on a BERT token classification preprocessing step, where for each token the type of the necessary transformation i
s predicted (none, uppercase, lowercase, capitalize, modify), and a character-level SMT step where the text is translated from original to normalized given the BERT-predicted transformation constraints. For some languages, depending on the results on development data, the training data was extended by back-translating OpenSubtitles data. In the final ordering of the ten participating teams, the HEL-LJU team has taken the second place, scoring better than the previous state-of-the-art.
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