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Found 9 papers, 2 papers with code
Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing
Although most existing methods can fulfil this requirement, they can only model single-source dialog data and cannot effectively capture the underlying knowledge of relations among data and subtasks.
Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning
Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive to obtain and would be small-scale.
OS-MSL: One Stage Multimodal Sequential Link Framework for Scene Segmentation and Classification
In this paper, from an alternate perspective to overcome the above challenges, we unite these two tasks into one task by a new form of predicting shots link: a link connects two adjacent shots, indicating that they belong to the same scene or category.
RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction
To further leverage relation information, we introduce a separate event relation prediction task and adopt multi-task learning method to explicitly enhance event extraction performance.
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Document-level Event Extraction
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Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
Hence, the key is to make full use of rich interaction information among streamers, users, and products.
When and Who? Conversation Transition Based on Bot-Agent Symbiosis Learning Network
In online customer service applications, multiple chatbots that are specialized in various topics are typically developed separately and are then merged with other human agents to a single platform, presenting to the users with a unified interface.
DialogAct2Vec: Towards End-to-End Dialogue Agent by Multi-Task Representation Learning
In this paper, we propose a novel joint end-to-end model by multi-task representation learning, which can capture the knowledge from heterogeneous information through automatically learning knowledgeable low-dimensional embeddings from data, named with DialogAct2Vec.
Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability
Learning an efficient manager of dialogue agent from data with little manual intervention is important, especially for goal-oriented dialogues.
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC
Recent years have witnessed the proliferation of Massive Open Online Courses (MOOCs).
