Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Jiang Chengyue"'
Publikováno v:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 424-438
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 training, one can additionally train the model to match its interpretation w
Externí odkaz:
http://arxiv.org/abs/2404.02068
Publikováno v:
SHS Web of Conferences, Vol 148, p 03004 (2022)
With the development of multi-polarization in the world, the UN Security Council plays an increasingly important role in maintaining international peace and promoting international cooperation, which has attracted much attention from the academic and
Externí odkaz:
https://doaj.org/article/2eb319a8d1e94668b986739e811c3fb5
Ontological knowledge, which comprises classes and properties and their relationships, is integral to world knowledge. It is significant to explore whether Pretrained Language Models (PLMs) know and understand such knowledge. However, existing PLM-pr
Externí odkaz:
http://arxiv.org/abs/2309.05936
Autor:
Yu, Tianyu, Jiang, Chengyue, Lou, Chao, Huang, Shen, Wang, Xiaobin, Liu, Wei, Cai, Jiong, Li, Yangning, Li, Yinghui, Tu, Kewei, Zheng, Hai-Tao, Zhang, Ningyu, Xie, Pengjun, Huang, Fei, Jiang, Yong
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 always have restricted output and input format. Their performances on
Externí odkaz:
http://arxiv.org/abs/2308.10529
Autor:
Li, Yangning, Ma, Shirong, Wang, Xiaobin, Huang, Shen, Jiang, Chengyue, Zheng, Hai-Tao, Xie, Pengjun, Huang, Fei, Jiang, Yong
Recently, instruction-following Large Language Models (LLMs) , represented by ChatGPT, have exhibited exceptional performance in general Natural Language Processing (NLP) tasks. However, the unique characteristics of E-commerce data pose significant
Externí odkaz:
http://arxiv.org/abs/2308.06966
Autor:
Cai, Jiong, Jiang, Yong, Zhang, Yue, Jiang, Chengyue, Yu, Ke, Ji, Jianhui, Xiao, Rong, Tang, Haihong, Wang, Tao, Huang, Zhongqiang, Xie, Pengjun, Huang, Fei, Tu, Kewei
Discovering the intended items of user queries from a massive repository of items is one of the main goals of an e-commerce search system. Relevance prediction is essential to the search system since it helps improve performance. When online serving
Externí odkaz:
http://arxiv.org/abs/2307.00370
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 severe redundancy and ambiguity and need to be canonicalized. Existing
Externí odkaz:
http://arxiv.org/abs/2302.03905
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) methods use the cross-encoder (CE) based architecture. CE concatenates
Externí odkaz:
http://arxiv.org/abs/2212.09125
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 each entity type independently, ignoring the correlations between typ
Externí odkaz:
http://arxiv.org/abs/2212.01581
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 which combines an extended pointer-generator network that generates nodes
Externí odkaz:
http://arxiv.org/abs/2004.03849