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Found 14 papers, 10 papers with code
Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks
On the other hand, symbolic rules such as regular expressions are interpretable, require no training, and often achieve decent accuracy; but rules cannot benefit from labeled data when available and hence underperform neural networks in rich-resource scenarios.
Neuralizing Regular Expressions for Slot Filling
Neural models and symbolic rules such as regular expressions have their respective merits and weaknesses.
RoT: Enhancing Large Language Models with Reflection on Search Trees
It uses a strong LLM to summarize guidelines from previous tree search experiences to enhance the ability of a weak LLM.
Using Interpretation Methods for Model Enhancement
In this paper, we propose a framework of utilizing interpretation methods and gold rationales to enhance models.
Do PLMs Know and Understand Ontological Knowledge?
In this paper, we focus on probing whether PLMs store ontological knowledge and have a semantic understanding of the knowledge rather than rote memorization of the surface form.
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which always have restricted output and input format.
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
EcomInstruct scales up the data size and task diversity by constructing atomic tasks with E-commerce basic data types, such as product information, user reviews.
Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model
We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance.
COMBO: A Complete Benchmark for Open KG Canonicalization
The subject and object noun phrases and the relation in open KG have severe redundancy and ambiguity and need to be canonicalized.
Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing
We also found MCCE is very effective in fine-grained (130 types) and coarse-grained (9 types) entity typing.
Ranked #2 on
Entity Typing
on Open Entity
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field
We use mean-field variational inference for efficient type inference on very large type sets and unfold it as a neural network module to enable end-to-end training.
Ranked #3 on
Entity Typing
on Open Entity
Learning Numeral Embedding
Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training.
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing
This paper presents the system used in our submission to the \textit{CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing}.
Learning Numeral Embeddings
Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training.
