يصف هذا العمل نهجنا للمهام الفرعية للمهمة Semeval-2021 8: MeasessVal: التهم والقياسات التي أخذت المركز الأول الرسمي في المسابقة.لحل جميع المهام الفرعية، نستخدم التعلم متعدد المهام بطريقة تشبه الإجابة على الأسئلة.نحن نستخدم أيضا الأوزان العددية في الوزن للمساهمة في الخسارة النهائية في التدريب المتعدد المهام.نحن نغتنم Luke لاستخراج الكميات، ونحن نغلق روبرتا لاستخراج كل ما يتعلق بكميات وجدت، بما في ذلك الكميات نفسها.
This work describes our approach for subtasks of SemEval-2021 Task 8: MeasEval: Counts and Measurements which took the official first place in the competition. To solve all subtasks we use multi-task learning in a question-answering-like manner. We also use learnable scalar weights to weight subtasks' contribution to the final loss in multi-task training. We fine-tune LUKE to extract quantity spans and we fine-tune RoBERTa to extract everything related to found quantities, including quantities themselves.
References used
https://aclanthology.org/
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
This paper explains the design of a heterogeneous system that ranked eighth in competition in SemEval2021 Task 8. We analyze ablation experiments and demonstrate how the system components, namely tokenizer, unit identifier, modifier classifier, and l
This paper presents our system for the Quantity span identification, Unit of measurement identification and Value modifier classification subtasks of the MeasEval 2021 task. The purpose of the Quantity span identification task was to locate spans of
We describe MeasEval, a SemEval task of extracting counts, measurements, and related context from scientific documents, which is of significant importance to the creation of Knowledge Graphs that distill information from the scientific literature. Th
In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. In response, social media platforms have worked on developing automatic detection methods and employing h