1 code implementation • 26 Mar 2024 • Davide Baldelli, Junfeng Jiang, Akiko Aizawa, Paolo Torroni
In this paper, we present TWOLAR: a two-stage pipeline for passage reranking based on the distillation of knowledge from Large Language Models (LLM).
1 code implementation • 31 Jan 2024 • Xanh Ho, Anh Khoa Duong Nguyen, An Tuan Dao, Junfeng Jiang, Yuki Chida, Kaito Sugimoto, Huy Quoc To, Florian Boudin, Akiko Aizawa
The number of Language Models (LMs) dedicated to processing scientific text is on the rise.
1 code implementation • starsem 2023 • An Wang, Junfeng Jiang, Youmi Ma, Ao Liu, Naoaki Okazaki
Aspect sentiment quad prediction (ASQP) analyzes the aspect terms, opinion terms, sentiment polarity, and aspect categories in a text.
Ranked #3 on Aspect-Based Sentiment Analysis (ABSA) on ASQP
1 code implementation • 15 May 2023 • Junfeng Jiang, Chengzhang Dong, Sadao Kurohashi, Akiko Aizawa
In this paper, we provide a feasible definition of dialogue segmentation points with the help of document-grounded dialogues and release a large-scale supervised dataset called SuperDialseg, containing 9, 478 dialogues based on two prevalent document-grounded dialogue corpora, and also inherit their useful dialogue-related annotations.
1 code implementation • 27 Oct 2022 • Che Liu, Rui Wang, Junfeng Jiang, Yongbin Li, Fei Huang
In this paper, we introduce the task of learning unsupervised dialogue embeddings.
1 code implementation • ICCV 2021 • QiXing Huang, Xiangru Huang, Bo Sun, Zaiwei Zhang, Junfeng Jiang, Chandrajit Bajaj
Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy.
1 code implementation • EACL 2021 • Junfeng Jiang, An Wang, Akiko Aizawa
It aims to extract the corresponding opinion words for a given opinion target in a review sentence.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
no code implementations • 16 May 2020 • Chao Xiong, Che Liu, Zijun Xu, Junfeng Jiang, Jieping Ye
In this work, we propose a matching network, called sequential sentence matching network (S2M), to use the sentence-level semantic information to address the problem.
2 code implementations • 22 Sep 2018 • Junfeng Jiang, Jiahao Li
Especially during the Chinese market crash in 2015, the Pearson correlation coefficient of adjusted sentimental factor with SSE is 0. 5844, which suggests that our model can provide a solid guidance, especially in the special period when the market is influenced greatly by public sentiment.