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Found 9 papers, 8 papers with code
An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding Tasks
Though linguistic knowledge emerges during large-scale language model pretraining, recent work attempt to explicitly incorporate human-defined linguistic priors into task-specific fine-tuning.
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering
Representations of events described in text are important for various tasks.
CoCoLM: COmplex COmmonsense Enhanced Language Model with Discourse Relations
Large-scale pre-trained language models have demonstrated strong knowledge representation ability.
XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System
The offline experimental results based on real-world E-commerce data demonstrate the effectiveness and verify the importance of unclicked items in sequential recommendation.
When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models
We also demonstrate that distributional methods are ideal to make up for pattern-based ones in such cases.
Hypernymy Detection for Low-Resource Languages via Meta Learning
Hypernymy detection, a. k. a, lexical entailment, is a fundamental sub-task of many natural language understanding tasks.
Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations
Computational and cognitive studies suggest that the abstraction of eventualities (activities, states, and events) is crucial for humans to understand daily eventualities.
Multiplex Word Embeddings for Selectional Preference Acquisition
Therefore, in this paper, we propose a multiplex word embedding model, which can be easily extended according to various relations among words.
SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors.
