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Found 11 papers, 6 papers with code
DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation
Finally, for improving the efficiency, we design a migrator to transfer the extracted information to the latest target domain model, which only need the target domain model for inference.
Unsupervised Contrast-Consistent Ranking with Language Models
We compare pairwise, pointwise and listwise prompting techniques to elicit a language model's ranking knowledge.
Overcoming Catastrophic Forgetting in Massively Multilingual Continual Learning
Real-life multilingual systems should be able to efficiently incorporate new languages as data distributions fed to the system evolve and shift over time.
Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset
To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to the system-induced biases.
Dataless Knowledge Fusion by Merging Weights of Language Models
In this paper, we study the problem of merging individual models built on different training data sets to obtain a single model that performs well both across all data set domains and can generalize on out-of-domain data.
DIWIFT: Discovering Instance-wise Influential Features for Tabular Data
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce.
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user.
Attending to Entities for Better Text Understanding
Recent progress in NLP witnessed the development of large-scale pre-trained language models (GPT, BERT, XLNet, etc.)
Implicit Argument Prediction as Reading Comprehension
Implicit arguments, which cannot be detected solely through syntactic cues, make it harder to extract predicate-argument tuples.
Implicit Argument Prediction with Event Knowledge
Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract.
Representing Meaning with a Combination of Logical and Distributional Models
In this paper, we focus on the three components of a practical system integrating logical and distributional models: 1) Parsing and task representation is the logic-based part where input problems are represented in probabilistic logic.
