Dialogue Generation is a fundamental component for real-world virtual assistants such as Siri and Alexa. It is the text generation task that automatically generate a response given a post by the user.
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To enhance the generalization ability of PanGu-$\alpha$, we collect 1. 1TB high-quality Chinese data from a wide range of domains to pretrain the model.
Ranked #1 on Reading Comprehension (Zero-Shot) on CMRC 2018
CLOZE (MULTI-CHOICES) (FEW-SHOT) CLOZE (MULTI-CHOICES) (ONE-SHOT) CLOZE (MULTI-CHOICES) (ZERO-SHOT) COMMON SENSE REASONING (FEW-SHOT) COMMON SENSE REASONING (ONE-SHOT) COMMON SENSE REASONING (ZERO-SHOT) DIALOGUE GENERATION FEW-SHOT IMAGE CLASSIFICATION NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE INFERENCE (FEW-SHOT) NATURAL LANGUAGE INFERENCE (ONE-SHOT) NATURAL LANGUAGE INFERENCE (ZERO-SHOT) NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING READING COMPREHENSION READING COMPREHENSION (FEW-SHOT) READING COMPREHENSION (ONE-SHOT) READING COMPREHENSION (ZERO-SHOT) TEXT CLASSIFICATION TEXT SUMMARIZATION
The system includes modules such as dialogue topic prediction, knowledge matching and dialogue generation.
The finding of general knowledge is further hindered by the unidirectional distillation, as the student should obey the teacher and may discard some knowledge that is truly general but refuted by the teacher.
Open-domain multi-turn conversations mainly have three features, which are hierarchical semantic structure, redundant information, and long-term dependency.
In this paper, we transform each view into a set of subviews and then decompose the original MI bound into a sum of bounds involving conditional MI between the subviews.
Despite the recent success of large-scale language models on various downstream NLP tasks, the repetition and inconsistency problems still persist in dialogue response generation.
AR and NAR generation can be uniformly regarded as to what extent previous tokens can be attended, and BANG bridges AR and NAR generation by designing a novel model structure for large-scale pretraining.
We study knowledge-grounded dialogue generation with pre-trained language models.
These results show that discrepancies is an important factor to consider when we use a pre-trained model, and a reduction in discrepancies can lead to improved performance.