1 code implementation • ICML 2020 • Jianshu Zhang, Jun Du, Yongxin Yang, Yi-Zhe Song, Si Wei, Li-Rong Dai
Recent encoder-decoder approaches typically employ string decoders to convert images into serialized strings for image-to-markup.
1 code implementation • SEMEVAL 2021 • Boyuan Zheng, Xiaoyu Yang, Yu-Ping Ruan, ZhenHua Ling, Quan Liu, Si Wei, Xiaodan Zhu
Given a passage and the corresponding question, a participating system is expected to choose the correct answer from five candidates of abstract concepts in a cloze-style machine reading comprehension setup.
1 code implementation • 8 Apr 2020 • Tianda Li, Jia-Chen Gu, Xiaodan Zhu, Quan Liu, Zhen-Hua Ling, Zhiming Su, Si Wei
Disentanglement is a problem in which multiple conversations occur in the same channel simultaneously, and the listener should decide which utterance is part of the conversation he will respond to.
2 code implementations • 7 Apr 2020 • Jia-Chen Gu, Tianda Li, Quan Liu, Zhen-Hua Ling, Zhiming Su, Si Wei, Xiaodan Zhu
In this paper, we study the problem of employing pre-trained language models for multi-turn response selection in retrieval-based chatbots.
1 code implementation • 27 Apr 2019 • Tianda Li, Xiaodan Zhu, Quan Liu, Qian Chen, Zhigang Chen, Si Wei
Natural language inference (NLI) is among the most challenging tasks in natural language understanding.
no code implementations • 22 Apr 2019 • Yu-Ping Ruan, Xiaodan Zhu, Zhen-Hua Ling, Zhan Shi, Quan Liu, Si Wei
Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning.
no code implementations • 1 Nov 2018 • Hao Li, Yang Wang, Xinyu Liu, Zhichao Sheng, Si Wei
We propose a nested recurrent neural network (nested RNN) model for English spelling error correction and generate pseudo data based on phonetic similarity to train it.
1 code implementation • ACL 2018 • Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, Si Wei
With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance.
Ranked #20 on Natural Language Inference on SNLI
2 code implementations • WS 2017 • Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, Diana Inkpen
The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks and the quality of the representation is tested with a natural language inference task.
Ranked #70 on Natural Language Inference on SNLI
Natural Language Inference Natural Language Understanding +1
no code implementations • 14 Mar 2017 • Junbei Zhang, Xiaodan Zhu, Qian Chen, Li-Rong Dai, Si Wei, Hui Jiang
The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA).
Ranked #39 on Question Answering on SQuAD1.1 dev
no code implementations • 13 Nov 2016 • Quan Liu, Hui Jiang, Zhen-Hua Ling, Xiaodan Zhu, Si Wei, Yu Hu
The PDP task we investigate in this paper is a complex coreference resolution task which requires the utilization of commonsense knowledge.
Ranked #63 on Coreference Resolution on Winograd Schema Challenge
no code implementations • 11 Nov 2016 • Dan Liu, Wei. Lin, Shiliang Zhang, Si Wei, Hui Jiang
This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests.
1 code implementation • 26 Oct 2016 • Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang
Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences.
11 code implementations • ACL 2017 • Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, Diana Inkpen
Reasoning and inference are central to human and artificial intelligence.
Ranked #29 on Natural Language Inference on SNLI
2 code implementations • ACL 2017 • Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, Guoping Hu
Cloze-style queries are representative problems in reading comprehension.
Ranked #3 on Question Answering on Children's Book Test
no code implementations • 24 Mar 2016 • Quan Liu, Hui Jiang, Andrew Evdokimov, Zhen-Hua Ling, Xiaodan Zhu, Si Wei, Yu Hu
We propose to use neural networks to model association between any two events in a domain.
Ranked #11 on Natural Language Understanding on PDP60
Natural Language Inference Natural Language Understanding +2
no code implementations • 28 Dec 2015 • Shiliang Zhang, Cong Liu, Hui Jiang, Si Wei, Li-Rong Dai, Yu Hu
In this paper, we propose a novel neural network structure, namely \emph{feedforward sequential memory networks (FSMN)}, to model long-term dependency in time series without using recurrent feedback.
no code implementations • 9 Oct 2015 • ShiLiang Zhang, Hui Jiang, Si Wei, Li-Rong Dai
We introduce a new structure for memory neural networks, called feedforward sequential memory networks (FSMN), which can learn long-term dependency without using recurrent feedback.