Search Results for author: Jiawei Zhou

Found 18 papers, 8 papers with code

Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue

no code implementations ACL 2022 Jiawei Zhou, Jason Eisner, Michael Newman, Emmanouil Antonios Platanios, Sam Thomson

Standard conversational semantic parsing maps a complete user utterance into an executable program, after which the program is executed to respond to the user.

Machine Translation Semantic Parsing +1

CLIPTrans: Transferring Visual Knowledge with Pre-trained Models for Multimodal Machine Translation

1 code implementation ICCV 2023 Devaansh Gupta, Siddhant Kharbanda, Jiawei Zhou, Wanhua Li, Hanspeter Pfister, Donglai Wei

Simultaneously, there has been an influx of multilingual pre-trained models for NMT and multimodal pre-trained models for vision-language tasks, primarily in English, which have shown exceptional generalisation ability.

Image Captioning Multimodal Machine Translation +2

Retrieval-based Disentanglement with Distant Supervision

no code implementations15 Dec 2022 Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Xin Jiang, Qun Liu, Lei Chen

Disentangled representation learning remains challenging as ground truth factors of variation do not naturally exist.

Cross-Modal Retrieval Disentanglement +2

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Benchmarking Text Generation

Inducing and Using Alignments for Transition-based AMR Parsing

1 code implementation NAACL 2022 Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramon Fernandez Astudillo

These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints.

AMR Parsing

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering

1 code implementation ACL 2022 Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen

To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR).

Open-Domain Question Answering Passage Retrieval +1

Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing

1 code implementation EMNLP 2021 Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo, Young-suk Lee, Radu Florian, Salim Roukos

We provide a detailed comparison with recent progress in AMR parsing and show that the proposed parser retains the desirable properties of previous transition-based approaches, while being simpler and reaching the new parsing state of the art for AMR 2. 0, without the need for graph re-categorization.

Ranked #9 on AMR Parsing on LDC2017T10 (using extra training data)

AMR Parsing

Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning

no code implementations6 Sep 2021 Yuxiang Sun, Bo Yuan, Yufan Xue, Jiawei Zhou, XiaoYu Zhang, Xianzhong Zhou

Researchers are increasingly focusing on intelligent games as a hot research area. The article proposes an algorithm that combines the multi-attribute management and reinforcement learning methods, and that combined their effect on wargaming, it solves the problem of the agent's low rate of winning against specific rules and its inability to quickly converge during intelligent wargame training. At the same time, this paper studied a multi-attribute decision making and reinforcement learning algorithm in a wargame simulation environment, and obtained data on red and blue conflict. Calculate the weight of each attribute based on the intuitionistic fuzzy number weight calculations.

Decision Making Management +2

AMR Parsing with Action-Pointer Transformer

1 code implementation NAACL 2021 Jiawei Zhou, Tahira Naseem, Ramón Fernandez Astudillo, Radu Florian

In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments.

AMR Parsing Hard Attention +1

On Statistical Efficiency in Learning

1 code implementation24 Dec 2020 Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh

We propose a generalized notion of Takeuchi's information criterion and prove that the proposed method can asymptotically achieve the optimal out-sample prediction loss under reasonable assumptions.

Model Selection

Improving Non-autoregressive Neural Machine Translation with Monolingual Data

no code implementations ACL 2020 Jiawei Zhou, Phillip Keung

Non-autoregressive (NAR) neural machine translation is usually done via knowledge distillation from an autoregressive (AR) model.

Data Augmentation Knowledge Distillation +2

Automating Botnet Detection with Graph Neural Networks

1 code implementation13 Mar 2020 Jiawei Zhou, Zhiying Xu, Alexander M. Rush, Minlan Yu

Botnets are now a major source for many network attacks, such as DDoS attacks and spam.

Graph Learning

Local Structure Matching Driven by Joint-Saliency-Structure Adaptive Kernel Regression

no code implementations3 Feb 2013 Binjie Qin, Zhuangming Shen, Zien Zhou, Jiawei Zhou, Jiuai Sun, HUI ZHANG, Mingxing Hu, Yisong Lv

For nonrigid image registration, matching the particular structures (or the outliers) that have missing correspondence and/or local large deformations, can be more difficult than matching the common structures with small deformations in the two images.

Image Registration regression

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