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Register a new userAM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples
AM2ICO: تقييم معنى الكلمات في السياق عبر لغات الموارد المنخفضة مع أمثلة الخصومة
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إن التقاط معنى كلمة في السياق والتمييز بين المراسلات والاختلافات عبر اللغات هو مفتاح بناء نماذج تمثيل نصية متعددة اللغات والنجاح. ومع ذلك، فإن مجموعات بيانات التقييم المتعددة اللغات الحالية التي تقيم الدلالات المعجمية في السياق "لها قيود مختلفة. على وجه الخصوص، 1) تقتصر تغطيتها اللغوية على لغات الموارد العالية والانحساج لصالح سوى عدد قليل من الأسر والمناطق اللغوية، 2) تصميم يجعل المهمة قابلة للحل عبر الإشارات السطحية، والتي تؤدي إلى تضخم بشكل مصطنع (وأحيانا سوبر -Human) عروض التشفير المحددين، و 3) لا يوجد دعم للتقييم المتبادل اللغوي. من أجل معالجة هذه الثغرات، نقدم AM2ICO (المعنى الخصومة والتعدد اللغات في السياق)، مجموعة التقييم الواسع التغطية واللغات متعددة اللغات؛ يهدف إلى تقييم إدراج قدرة نماذج التمثيل الحديثة (SOTA) لفهم هوية معنى الكلمات في السياقات المتبادلة لمدة 14 أزواج لغوية. نقوم بإجراء سلسلة من التجارب في مجموعة واسعة من الإعدادات وإظهار الطبيعة الصعبة ل AM2ICO. تكشف النتائج أن تشفير سوتا الحالية التي يتعامل معها يتخلف بشكل كبير وراء الأداء البشري، ويتم ملاحظة أكبر فجوات لغات الموارد المنخفضة ولغات مختلفة عن اللغة الإنجليزية.
Capturing word meaning in context and distinguishing between correspondences and variations across languages is key to building successful multilingual and cross-lingual text representation models. However, existing multilingual evaluation datasets that evaluate lexical semantics in-context'' have various limitations. In particular, 1) their language coverage is restricted to high-resource languages and skewed in favor of only a few language families and areas, 2) a design that makes the task solvable via superficial cues, which results in artificially inflated (and sometimes super-human) performances of pretrained encoders, and 3) no support for cross-lingual evaluation. In order to address these gaps, we present AM2iCo (Adversarial and Multilingual Meaning in Context), a wide-coverage cross-lingual and multilingual evaluation set; it aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts for 14 language pairs. We conduct a series of experiments in a wide range of setups and demonstrate the challenging nature of AM2iCo. The results reveal that current SotA pretrained encoders substantially lag behind human performance, and the largest gaps are observed for low-resource languages and languages dissimilar to English.
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