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Found 21 papers, 4 papers with code
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations
There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022).
Language Agnostic Multilingual Information Retrieval with Contrastive Learning
Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages.
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.
Contrastive Document Representation Learning with Graph Attention Networks
In this work, we propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings.
Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks
In every network layer, the capsules interact with each other and with representations of words in the sentence.
Switch-LSTMs for Multi-Criteria Chinese Word Segmentation
With these auto-switched LSTMs, our model provides a more flexible solution for multi-criteria CWS, which is also easy to transfer the learned knowledge to new criteria.
Convolutional Interaction Network for Natural Language Inference
Attention-based neural models have achieved great success in natural language inference (NLI).
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks
Designing shared neural architecture plays an important role in multi-task learning.
Toward Diverse Text Generation with Inverse Reinforcement Learning
Similar to the adversarial models, the reward and policy function in IRL are optimized alternately.
DAG-based Long Short-Term Memory for Neural Word Segmentation
In this paper, we propose a new neural model to incorporate the word-level information for Chinese word segmentation.
Adversarial Multi-Criteria Learning for Chinese Word Segmentation
Different linguistic perspectives causes many diverse segmentation criteria for Chinese word segmentation (CWS).
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering.
End-to-End Neural Sentence Ordering Using Pointer Network
However, it is nontrivial for pair-wise models to incorporate the contextual sentence information.
Neural Sentence Ordering
Sentence ordering is a general and critical task for natural language generation applications.
Gaussian Mixture Embeddings for Multiple Word Prototypes
In this paper, we propose the Gaussian mixture skip-gram (GMSG) model to learn the Gaussian mixture embeddings for words based on skip-gram framework.
A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network
In this work, we address the problem to model all the nodes (words or phrases) in a dependency tree with the dense representations.
