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Register a new userGreat Service! Fine-grained Parsing of Implicit Arguments
خدمة رائعة!تحليل غرامة الحبيبات من الحجج الضمنية
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التغطية الواسعة التي تعني تعني التمثيلات في NLP التركيز في الغالب على المحتوى المعبر عنه صراحة. والأهم من ذلك أن ندرة مجموعات البيانات التلقائية للأدوار الضمنية المتنوعة يحد من الدراسات التجريبية في الدقوق اللغوي. على سبيل المثال، في مراجعة الويب خدمة رائعة! ''، المزود والمستهلك حجج ضمنية لأنواع مختلفة. نحن ندرس وجعة مشروحة من الحجج الضمنية الضمنية الجميلة (CUI و Hershcovich، 2020) من خلال إعادة النظر في ذلك بعناية، وحل العديد من التناقضات. في وقت لاحق، نقدم أول محلل عصبي يستند إلى الانتقال يمكن أن يتعامل مع الحجج الضمنية بشكل حيوي، وتجربة نظاميين انتقالين مختلفين على مجموعة بيانات محسنة. نجد أن أنواع معينة من الوسائط الضمنية أكثر صعوبة من غيرها من غيرها وأن النظام الأكثر دقة أكثر دقة في استرداد الوسائط الضمنية، على الرغم من وجود نقاط تحليل إجمالية أقل، مما يشهد قيود التفكير الحالية لنماذج NLP. سيسهل هذا العمل فهم أفضل للغة الضمنية والكشفية، من خلال دمجها بشكل كلي في تمثيلات المعنى.
Broad-coverage meaning representations in NLP mostly focus on explicitly expressed content. More importantly, the scarcity of datasets annotating diverse implicit roles limits empirical studies into their linguistic nuances. For example, in the web review Great service!'', the provider and consumer are implicit arguments of different types. We examine an annotated corpus of fine-grained implicit arguments (Cui and Hershcovich, 2020) by carefully re-annotating it, resolving several inconsistencies. Subsequently, we present the first transition-based neural parser that can handle implicit arguments dynamically, and experiment with two different transition systems on the improved dataset. We find that certain types of implicit arguments are more difficult to parse than others and that the simpler system is more accurate in recovering implicit arguments, despite having a lower overall parsing score, attesting current reasoning limitations of NLP models. This work will facilitate a better understanding of implicit and underspecified language, by incorporating it holistically into meaning representations.
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