14 citations · 30 across the 10 of their papers we have counts for
10 papers
Using Interpretation Methods for Model Enhancement
Zhuo Chen, Chengyue Jiang, Kewei Tu
In the age of neural natural language processing, there are plenty of works trying to derive interpretations of neural models. Intuitively, when gold rationales exist during traini…
RoT: Enhancing Large Language Models with Reflection on Search Trees
Wenyang Hui, Kewei Tu
Large language models (LLMs) have demonstrated impressive capability in reasoning and planning when integrated with tree-search-based prompting methods. However, since these method…
Do PLMs Know and Understand Ontological Knowledge?
Weiqi Wu, Chengyue Jiang, Yong Jiang +2
Ontological knowledge, which comprises classes and properties and their relationships, is integral to world knowledge. It is significant to explore whether Pretrained Language Mode…
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
Yangning Li, Shirong Ma, Xiaobin Wang +6
Recently, instruction-following Large Language Models (LLMs) , represented by ChatGPT, have exhibited exceptional performance in general Natural Language Processing (NLP) tasks. Ho…
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
Tianyu Yu, Chengyue Jiang, Chao Lou +12
Large language models (LLMs) have shown impressive ability for open-domain NLP tasks. However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which…
COMBO: A Complete Benchmark for Open KG Canonicalization
Chengyue Jiang, Yong Jiang, Weiqi Wu +3
Open knowledge graph (KG) consists of (subject, relation, object) triples extracted from millions of raw text. The subject and object noun phrases and the relation in open KG have…
Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing
Chengyue Jiang, Wenyang Hui, Yong Jiang +3
Ultra-fine entity typing (UFET) predicts extremely free-formed types (e.g., president, politician) of a given entity mention (e.g., Joe Biden) in context. State-of-the-art (SOTA) m…
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field
Chengyue Jiang, Yong Jiang, Weiqi Wu +2
Ultra-fine entity typing (UFET) aims to predict a wide range of type phrases that correctly describe the categories of a given entity mention in a sentence. Most recent works infer…
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing
Xinyu Wang, Yixian Liu, Zixia Jia +2
This paper presents the system used in our submission to the \textit{CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing}. Our system is a graph-based parser whi…
Learning Numeral Embeddings
Chengyue Jiang, Zhonglin Nian, Kaihao Guo +4
Word embedding is an essential building block for deep learning methods for natural language processing. Although word embedding has been extensively studied over the years, the pr…