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Register a new userLearning compositional structures for semantic graph parsing
تعلم الهياكل التركيبية لتحليل الرسم البياني الدلالي
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AM تحليل التبعية هي طريقة لتحليل الرسم البياني الدلالي العصبي الذي يستغل مبدأ التركيبية.على الرغم من أن محلل التبعية، فقد تبين أن محلل التبعية سريعة ودقيقة عبر العديد من الرسوم البيانية، فإنها تتطلب عبائيات صريحة لهياكل الأشجار التركيبية للتدريب.في الماضي، تم الحصول على هؤلاء استخدام الاستدلال المعقدة من الرسوم المشتركة من قبل الخبراء.هنا نظهر كيف يمكن تدريبهم بدلا من ذلك مباشرة على الرسوم البيانية مع نموذج متغير كامنة عصبي، مما يقلل بشكل كبير من كمية وتعقيد الاستدلال اليدوي.نوضح أن نموذجنا يلتقط العديد من الظواهر اللغوية بمفرده وتحقق دقة مماثلة للتدريب الخاضع للإشراف، مما يسهل بشكل كبير استخدام تحليل التبعية لشبانس جديدة.
AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of the compositional tree structures for training. In the past, these were obtained using complex graphbank-specific heuristics written by experts. Here we show how they can instead be trained directly on the graphs with a neural latent-variable model, drastically reducing the amount and complexity of manual heuristics. We demonstrate that our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training, greatly facilitating the use of AM dependency parsing for new sembanks.
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The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In this work, we explore an alternative paradigm. We formulate semantic
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