Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMorph Call: Probing Morphosyntactic Content of Multilingual Transformers
تحويل الدعوة: التحقيق محتوى مورفوسنكتاكسي للمحولات متعددة اللغات
258
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
حفز الأداء المتميز لنماذج اللغة القائمة على المحولات في مجموعة كبيرة ومتنوعة من المهام NLP و NLU الاهتمام باستكشاف أعمالها الداخلية. ركزت الأبحاث الحديثة بشكل أساسي على ظواهر لغوية عالية المستوى ومعقدة مثل بناء الجملة والدلالات والمعرفة العالمية والفني المشترك. غالبية الدراسات هي أنجلجة، ولا تزال قليلا معروفة فيما يتعلق باللغات الأخرى، وتحديدا خصائص مورفوسينتانية. تحقيقا لهذه الغاية، يقدم عملنا دعوة مورف، وهو مجموعة من 46 مهام التحقيق لأربعة لغات الهند الهندية في التشكل المختلفة: الروسية والفرنسية والإنجليزية والألمانية. نقترح نوعا جديدا من المهام التحقيق بناء على الكشف عن اضطرابات الجملة الموجهة. نحن نستخدم مزيج من تقنيات التثبيت التابعة للطبقة العصبية والطبقة والتمثيل لتحليل المحتوى المورفوسنكتاكسي لأربعة محولات متعددة اللغات، بما في ذلك إصداراتهم المقطوعة. بالإضافة إلى ذلك، ندرس كيف تؤثر ضبط مهمة وضع العلامات على الأداء التحقيق.
The outstanding performance of transformer-based language models on a great variety of NLP and NLU tasks has stimulated interest in exploration of their inner workings. Recent research has been primarily focused on higher-level and complex linguistic phenomena such as syntax, semantics, world knowledge and common-sense. The majority of the studies is anglocentric, and little remains known regarding other languages, specifically their morphosyntactic properties. To this end, our work presents Morph Call, a suite of 46 probing tasks for four Indo-European languages of different morphology: Russian, French, English and German. We propose a new type of probing tasks based on detection of guided sentence perturbations. We use a combination of neuron-, layer- and representation-level introspection techniques to analyze the morphosyntactic content of four multilingual transformers, including their understudied distilled versions. Besides, we examine how fine-tuning on POS-tagging task affects the probing performance.
References used
https://aclanthology.org/
rate research
Read More
Pre-trained multilingual language models have become an important building block in multilingual Natural Language Processing. In the present paper, we investigate a range of such models to find out how well they transfer discourse-level knowledge acr
oss languages. This is done with a systematic evaluation on a broader set of discourse-level tasks than has been previously been assembled. We find that the XLM-RoBERTa family of models consistently show the best performance, by simultaneously being good monolingual models and degrading relatively little in a zero-shot setting. Our results also indicate that model distillation may hurt the ability of cross-lingual transfer of sentence representations, while language dissimilarity at most has a modest effect. We hope that our test suite, covering 5 tasks with a total of 22 languages in 10 distinct families, will serve as a useful evaluation platform for multilingual performance at and beyond the sentence level.
The massive spread of false information on social media has become a global risk especially in a global pandemic situation like COVID-19. False information detection has thus become a surging research topic in recent months. In recent years, supervis
ed machine learning models have been used to automatically identify false information in social media. However, most of these machine learning models focus only on the language they were trained on. Given the fact that social media platforms are being used in different languages, managing machine learning models for each and every language separately would be chaotic. In this research, we experiment with multilingual models to identify false information in social media by using two recently released multilingual false information detection datasets. We show that multilingual models perform on par with the monolingual models and sometimes even better than the monolingual models to detect false information in social media making them more useful in real-world scenarios.
We propose using a multilabel probing task to assess the morphosyntactic representations of multilingual word embeddings. This tweak on canonical probing makes it easy to explore morphosyntactic representations, both holistically and at the level of
individual features (e.g., gender, number, case), and leads more naturally to the study of how language models handle co-occurring features (e.g., agreement phenomena). We demonstrate this task with multilingual BERT (Devlin et al., 2018), training probes for seven typologically diverse languages: Afrikaans, Croatian, Finnish, Hebrew, Korean, Spanish, and Turkish. Through this simple but robust paradigm, we verify that multilingual BERT renders many morphosyntactic features simultaneously extractable. We further evaluate the probes on six held-out languages: Arabic, Chinese, Marathi, Slovenian, Tagalog, and Yoruba. This zero-shot style of probing has the added benefit of revealing which cross-linguistic properties a language model recognizes as being shared by multiple languages.
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.
Recent research has adopted a new experimental field centered around the concept of text perturbations which has revealed that shuffled word order has little to no impact on the downstream performance of Transformer-based language models across many
NLP tasks. These findings contradict the common understanding of how the models encode hierarchical and structural information and even question if the word order is modeled with position embeddings. To this end, this paper proposes nine probing datasets organized by the type of controllable text perturbation for three Indo-European languages with a varying degree of word order flexibility: English, Swedish and Russian. Based on the probing analysis of the M-BERT and M-BART models, we report that the syntactic sensitivity depends on the language and model pre-training objectives. We also find that the sensitivity grows across layers together with the increase of the perturbation granularity. Last but not least, we show that the models barely use the positional information to induce syntactic trees from their intermediate self-attention and contextualized representations.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
