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Register a new userImproving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task
تحسين نظام الحوار المنتهي الموجهة نحو الوظيفة مع مهمة مساعدة بسيطة
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جعلت نموذج الاستفادة من النماذج اللغوية الكبيرة المدربة مسبقا تقدما ملحوظا على معايير نظم الحوار الموجهة نحو المهام (TOD).في هذه الورقة، نجمع بين هذه النموذج مع إطار تعليمي متعدد المهام لنمذجة TOD نهاية إلى الطرفية من خلال اعتماد التنبؤ SPAN كامرأة مساعدة.في الإعداد المحرز، يحقق نموذجنا نتائج أحدث نتائج جديدة مع درجات مشتركة من 108.3 و 107.5 على MultiWoz 2.0 و MultiWoz 2.1، على التوالي.علاوة على ذلك، نوضح أن التعلم متعدد المهام يحسن ليس فقط أداء النموذج ولكن قدرة تعميمه من خلال تجارب تكيف المجال في إعداد القليل من اللقطة.الرمز متاح في github.com/bepoetree/mttod.
The paradigm of leveraging large pre-trained language models has made significant progress on benchmarks on task-oriented dialogue (TOD) systems. In this paper, we combine this paradigm with multi-task learning framework for end-to-end TOD modeling by adopting span prediction as an auxiliary task. In end-to-end setting, our model achieves new state-of-the-art results with combined scores of 108.3 and 107.5 on MultiWOZ 2.0 and MultiWOZ 2.1, respectively. Furthermore, we demonstrate that multi-task learning improves not only the performance of model but its generalization capability through domain adaptation experiments in the few-shot setting. The code is available at github.com/bepoetree/MTTOD.
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Incorporating knowledge bases (KB) into end-to-end task-oriented dialogue systems is challenging, since it requires to properly represent the entity of KB, which is associated with its KB context and dialogue context. The existing works represent the
entity with only perceiving a part of its KB context, which can lead to the less effective representation due to the information loss, and adversely favor KB reasoning and response generation. To tackle this issue, we explore to fully contextualize the entity representation by dynamically perceiving all the relevant entities and dialogue history. To achieve this, we propose a COntext-aware Memory Enhanced Transformer framework (COMET), which treats the KB as a sequence and leverages a novel Memory Mask to enforce the entity to only focus on its relevant entities and dialogue history, while avoiding the distraction from the irrelevant entities. Through extensive experiments, we show that our COMET framework can achieve superior performance over the state of the arts.
We propose a novel problem within end-to-end learning of task oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting). Such dialogs are grounded in domain
-specific flowcharts, which the agent is supposed to follow during the conversation. Our task exposes novel technical challenges for neural TOD, such as grounding an utterance to the flowchart without explicit annotation, referring to additional manual pages when user asks a clarification question, and ability to follow unseen flowcharts at test time. We release a dataset (FLODIAL) consisting of 2,738 dialogs grounded on 12 different troubleshooting flowcharts. We also design a neural model, FLONET, which uses a retrieval-augmented generation architecture to train the dialog agent. Our experiments find that FLONET can do zero-shot transfer to unseen flowcharts, and sets a strong baseline for future research.
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Recent years has witnessed the remarkable success in end-to-end task-oriented dialog system, especially when incorporating external knowledge information. However, the quality of most existing models' generated response is still limited, mainly due t
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Dialogue policy optimisation via reinforcement learning requires a large number of training interactions, which makes learning with real users time consuming and expensive. Many set-ups therefore rely on a user simulator instead of humans. These user
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