Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task
محلل التبعية المحسنة DCU-EPFL في مهمة مشتركة IWPT 2021
299
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نحن تصف تقديم DCU-EPFL إلى مهمة مشتركة IWPT 2021: من النص الخام لتعزيز التبعيات العالمية. تتضمن المهمة تحليل الرسوم البيانية UD المحسنة، والتي تعد امتدادا لأشجار التبعية الأساسية المصممة لتكون أكثر تسهيلا نحو تمثيل الهيكل الدلالي. يتم التقييم على 29 شجرة Treebanks في 17 لغة ومطلوبة للمشاركين لتحليل البيانات من كل لغة تبدأ من السلاسل الخام. يستخدم نهجنا خط أنابيب Stanza لمعالجة الملفات النصية، XLM-Roberta للحصول على تمثيلات رمزية في السياق، ونموذج تسجيل الحافة والعلامات للتنبؤ الرسم البياني المحسن. أخيرا، ندير نص PostProcessing لضمان جميع مخرجاتنا هي الرسوم البيانية UD المحسنة سارية المفعول. يضع نظامنا السادس من أصل 9 مشاركا مع درجة مرفق محسنة خشنة (ELAS) 83.57. نقوم بإجراء تجارب إضافية بعد الموعد النهائي والتي تشمل استخدام Trankit لمعالجة ما قبل المعالجة، XLM-Roberta Large Protectenation، وتعلم المتعدد التعلم بين محلل التبعية الأساسية والمعززة. جميع هذه التعديلات تحسن النتيجة الأولية ونظامنا النهائي لديه إيلاس خشن 88.04.
We describe the DCU-EPFL submission to the IWPT 2021 Parsing Shared Task: From Raw Text to Enhanced Universal Dependencies. The task involves parsing Enhanced UD graphs, which are an extension of the basic dependency trees designed to be more facilitative towards representing semantic structure. Evaluation is carried out on 29 treebanks in 17 languages and participants are required to parse the data from each language starting from raw strings. Our approach uses the Stanza pipeline to preprocess the text files, XLM-RoBERTa to obtain contextualized token representations, and an edge-scoring and labeling model to predict the enhanced graph. Finally, we run a postprocessing script to ensure all of our outputs are valid Enhanced UD graphs. Our system places 6th out of 9 participants with a coarse Enhanced Labeled Attachment Score (ELAS) of 83.57. We carry out additional post-deadline experiments which include using Trankit for pre-processing, XLM-RoBERTa LARGE, treebank concatenation, and multitask learning between a basic and an enhanced dependency parser. All of these modifications improve our initial score and our final system has a coarse ELAS of 88.04.
References used
https://aclanthology.org/
rate research
Read More
We describe the second IWPT task on end-to-end parsing from raw text to Enhanced Universal Dependencies. We provide details about the evaluation metrics and the datasets used for training and evaluation. We compare the approaches taken by participating teams and discuss the results of the shared task, also in comparison with the first edition of this task.
This paper presents our multilingual dependency parsing system as used in the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies. Our system consists of an unfactorized biaffine classifier that operates directly on fine-tuned XLM-R
embeddings and generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens. To avoid sparsity issues resulting from lexicalized dependency labels, we replace lexical items in relations with placeholders at training and prediction time, later retrieving them from the parse via a hybrid rule-based/machine-learning system. In addition, we utilize model ensembling at prediction time. Our system achieves high parsing accuracy on the blind test data, ranking 3rd out of 9 with an average ELAS F1 score of 86.97.
MeasEval aims at identifying quantities along with the entities that are measured with additional properties within English scientific documents. The variety of styles used makes measurements, a most crucial aspect of scientific writing, challenging
to extract. This paper presents ablation studies making the case for several preprocessing steps such as specialized tokenization rules. For linguistic structure, we encode dependency trees in a Deep Graph Convolution Network (DGCNN) for multi-task classification.
This paper presents the Bering Lab's submission to the shared tasks of the 8th Workshop on Asian Translation (WAT 2021) on JPC2 and NICT-SAP. We participated in all tasks on JPC2 and IT domain tasks on NICT-SAP. Our approach for all tasks mainly focu
sed on building NMT systems in domain-specific corpora. We crawled patent document pairs for English-Japanese, Chinese-Japanese, and Korean-Japanese. After cleaning noisy data, we built parallel corpus by aligning those sentences with the sentence-level similarity scores. Also, for SAP test data, we collected the OPUS dataset including three IT domain corpora. We then trained transformer on the collected dataset. Our submission ranked 1st in eight out of fourteen tasks, achieving up to an improvement of 2.87 for JPC2 and 8.79 for NICT-SAP in BLEU score .
We describe the NUIG solution for IWPT 2021 Shared Task of Enhanced Dependency (ED) parsing in multiple languages. For this shared task, we propose and evaluate an End-to-end Seq2seq mBERT-based ED parser which predicts the ED-parse tree of a given i
nput sentence as a relative head-position tag-sequence. Our proposed model is a multitasking neural-network which performs five key tasks simultaneously namely UPOS tagging, UFeat tagging, Lemmatization, Dependency-parsing and ED-parsing. Furthermore we utilise the linguistic typology available in the WALS database to improve the ability of our proposed end-to-end parser to transfer across languages. Results show that our proposed Seq2seq ED-parser performs on par with state-of-the-art ED-parser despite having a much simpler de- sign.
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
