1 code implementation • NAACL 2022 • Tingting Ma, Qianhui Wu, Zhiwei Yu, Tiejun Zhao, Chin-Yew Lin
Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem, where a meta-learning model is trained with a certain capability to quickly adapt to newly specified few-shot tasks with potentially unseen intent categories.
no code implementations • 23 Apr 2024 • Jieru Lin, Danqing Huang, Tiejun Zhao, Dechen Zhan, Chin-Yew Lin
This complexity makes the comprehension of graphic design challenging, for it needs the capability to both recognize the design elements and understand the design.
1 code implementation • 19 Mar 2024 • Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, QIngwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, Dongmei Zhang
The challenge is that information entropy may be a suboptimal compression metric: (i) it only leverages unidirectional context and may fail to capture all essential information needed for prompt compression; (ii) it is not aligned with the prompt compression objective.
no code implementations • 14 Mar 2024 • Haohan Weng, Danqing Huang, Yu Qiao, Zheng Hu, Chin-Yew Lin, Tong Zhang, C. L. Philip Chen
In this paper, we present Desigen, an automatic template creation pipeline which generates background images as well as harmonious layout elements over the background.
1 code implementation • 29 Jan 2024 • Jieru Lin, Danqing Huang, Tiejun Zhao, Dechen Zhan, Chin-Yew Lin
Furthermore, based on our observation that pixel space is more sensitive in capturing spatial patterns of graphic layouts (e. g., overlap, alignment), we propose a learning-based locator to detect erroneous tokens which takes the wireframe image rendered from the generated layout sequence as input.
no code implementations • 14 Nov 2023 • Yuhan Li, Jian Wu, Zhiwei Yu, Börje F. Karlsson, Wei Shen, Manabu Okumura, Chin-Yew Lin
To close this gap in data availability and enable cross-modality IE, while alleviating labeling costs, we propose a semi-supervised pipeline for annotating entities in text, as well as entities and relations in tables, in an iterative procedure.
1 code implementation • 10 Oct 2023 • Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu
Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs' perception of the key information to simultaneously address the three challenges.
1 code implementation • 9 Oct 2023 • Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu
Large language models (LLMs) have been applied in various applications due to their astonishing capabilities.
1 code implementation • 24 May 2023 • Tingting Ma, Qianhui Wu, Huiqiang Jiang, Börje F. Karlsson, Tiejun Zhao, Chin-Yew Lin
Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language.
1 code implementation • 21 Nov 2022 • Qianhui Wu, Huiqiang Jiang, Haonan Yin, Börje F. Karlsson, Chin-Yew Lin
Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-distribution (ID) examples.
2 code implementations • 24 Oct 2022 • Yiheng Shu, Zhiwei Yu, Yuhan Li, Börje F. Karlsson, Tingting Ma, Yuzhong Qu, Chin-Yew Lin
Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios.
no code implementations • 20 Oct 2022 • Wanjun Zhong, Tingting Ma, Jiahai Wang, Jian Yin, Tiejun Zhao, Chin-Yew Lin, Nan Duan
This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making.
no code implementations • 14 Apr 2022 • Carina Negreanu, Alperen Karaoglu, Jack Williams, Shuang Chen, Daniel Fabian, Andrew Gordon, Chin-Yew Lin
The task divides into two steps: subject suggestion, the task of populating the main column; and gap filling, the task of populating the remaining columns.
1 code implementation • Findings (ACL) 2022 • Tingting Ma, Huiqiang Jiang, Qianhui Wu, Tiejun Zhao, Chin-Yew Lin
Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples.
Ranked #5 on Few-shot NER on Few-NERD (INTRA)
1 code implementation • ACL 2021 • Tingting Ma, Jin-Ge Yao, Chin-Yew Lin, Tiejun Zhao
The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be entailed by the input, aiming at generic classification applicable on any specified label space.
1 code implementation • ACL 2021 • Shuang Chen, Qian Liu, Zhiwei Yu, Chin-Yew Lin, Jian-Guang Lou, Feng Jiang
We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA).
Ranked #1 on Knowledge Base Question Answering on GrailQA
no code implementations • 1 Jan 2021 • Yuxi Xie, Danqing Huang, Jinpeng Wang, Chin-Yew Lin
Layout representation, which models visual elements in a canvas and their inter-relations, plays a crucial role in graphic design intelligence.
no code implementations • COLING 2020 • Feng Nie, Jinpeng Wang, Chin-Yew Lin
Large-scale datasets recently proposed for generation contain loosely corresponding data text pairs, where part of spans in text cannot be aligned to its incomplete paired input.
no code implementations • 6 Jan 2020 • Shuang Chen, Jinpeng Wang, Feng Jiang, Chin-Yew Lin
Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility.
Ranked #2 on Entity Disambiguation on AIDA-CoNLL (Micro-F1 metric)
1 code implementation • 14 Nov 2019 • Qianhui Wu, Zijia Lin, Guoxin Wang, Hui Chen, Börje F. Karlsson, Biqing Huang, Chin-Yew Lin
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER).
Ranked #1 on Cross-Lingual NER on MSRA
no code implementations • IJCNLP 2019 • Shuang Chen, Jinpeng Wang, Xiaocheng Feng, Feng Jiang, Bing Qin, Chin-Yew Lin
Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge.
no code implementations • WS 2019 • Feng Nie, Jinpeng Wang, Rong pan, Chin-Yew Lin
Data-to-text generation aims to generate descriptions given a structured input data (i. e., a table with multiple records).
no code implementations • 25 Sep 2019 • Hiroaki Yamane, Chin-Yew Lin, Tatsuya Harada
To this end, we first used a crowdsourcing service to obtain sufficient data for a subjective agreement on numerical common sense.
no code implementations • ACL 2019 • Feng Nie, Jin-Ge Yao, Jinpeng Wang, Rong pan, Chin-Yew Lin
Recent neural language generation systems often \textit{hallucinate} contents (i. e., producing irrelevant or contradicted facts), especially when trained on loosely corresponding pairs of the input structure and text.
1 code implementation • ACL 2019 • Tianyu Liu, Jin-Ge Yao, Chin-Yew Lin
Most of the recently proposed neural models for named entity recognition have been purely data-driven, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features.
Ranked #14 on Named Entity Recognition (NER) on Ontonotes v5 (English) (using extra training data)
no code implementations • 8 Mar 2019 • Tianwen Jiang, Sendong Zhao, Jing Liu, Jin-Ge Yao, Ming Liu, Bing Qin, Ting Liu, Chin-Yew Lin
Time-DS is composed of a time series instance-popularity and two strategies.
no code implementations • EMNLP 2018 • Longxu Dou, Guanghui Qin, Jinpeng Wang, Jin-Ge Yao, Chin-Yew Lin
Data2Text Studio is a platform for automated text generation from structured data.
1 code implementation • EMNLP 2018 • Guanghui Qin, Jin-Ge Yao, Xuening Wang, Jinpeng Wang, Chin-Yew Lin
Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms.
no code implementations • CONLL 2018 • Feng Nie, Shuyan Zhou, Jing Liu, Jinpeng Wang, Chin-Yew Lin, Rong pan
The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base.
no code implementations • EMNLP 2018 • Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong pan, Chin-Yew Lin
Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data.
1 code implementation • 8 Sep 2018 • Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong pan, Chin-Yew Lin
Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data.
no code implementations • 15 Aug 2018 • Feng Nie, Hailin Chen, Jinpeng Wang, Jin-Ge Yao, Chin-Yew Lin, Rong pan
Recent neural models for data-to-document generation have achieved remarkable progress in producing fluent and informative texts.
no code implementations • COLING 2018 • Danqing Huang, Jing Liu, Chin-Yew Lin, Jian Yin
Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.
no code implementations • ACL 2018 • Danqing Huang, Jin-Ge Yao, Chin-Yew Lin, Qingyu Zhou, Jian Yin
To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system.
no code implementations • IJCNLP 2017 • Jinpeng Wang, Yutai Hou, Jing Liu, Yunbo Cao, Chin-Yew Lin
We present in this paper a statistical framework that generates accurate and fluent product description from product attributes.
no code implementations • EMNLP 2017 • Danqing Huang, Shuming Shi, Chin-Yew Lin, Jian Yin
This method learns the mappings between math concept phrases in math word problems and their math expressions from training data.
no code implementations • ACL 2017 • Ying Lin, Chin-Yew Lin, Heng Ji
Traditional Entity Linking (EL) technologies rely on rich structures and properties in the target knowledge base (KB).
no code implementations • EACL 2017 • Benjamin Heinzerling, Michael Strube, Chin-Yew Lin
We introduce automatic verification as a post-processing step for entity linking (EL).