تقدم هذه الورقة نظام GX لمهمة الغموض المتعددة اللغات واللغة اللغوية في السياق (MCL-WIC).الغرض من المهمة MCL-WIC هو معالجة التحدي المتمثل في التقاط الطبيعة Polysemous للكلمات دون الاعتماد على مخزون ثابت ثابت في بيئة متعددة اللغات واللغة اللغوية.لحل المشكلات، نستخدم Adgeddings Word الخاص بالسياق من بيرت للقضاء على الغموض بين الكلمات في سياقات مختلفة.ولغات دون وجود كائن تدريب متاح، مثل الصينية، نستخدم نموذج الترجمة الآلي للخلايا العصبية لترجمة البيانات الإنجليزية الصادرة عن المنظمين للحصول على البيانات الزائفة المتاحة.في هذه الورقة، نطبق نظامنا على الإعداد الإنجليزي والصيني متعدد اللغات وإظهار النتائج التجريبية أن طريقتنا لها مزايا معينة.
This paper presents the GX system for the Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) task. The purpose of the MCL-WiC task is to tackle the challenge of capturing the polysemous nature of words without relying on a fixed sense inventory in a multilingual and cross-lingual setting. To solve the problems, we use context-specific word embeddings from BERT to eliminate the ambiguity between words in different contexts. For languages without an available training corpus, such as Chinese, we use neuron machine translation model to translate the English data released by the organizers to obtain available pseudo-data. In this paper, we apply our system to the English and Chinese multilingual setting and the experimental results show that our method has certain advantages.
References used
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