Search Results for author: Yiming Yang

Found 77 papers, 44 papers with code

Think about it! Improving defeasible reasoning by first modeling the question scenario.

1 code implementation EMNLP 2021 Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter Clark, Yiming Yang, Eduard Hovy

Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.

不同类型噪声环境下言语理解的脑机制研究(Brain Mechanism of Speech Comprehension in Different Noise Conditions)

no code implementations CCL 2021 Libo Geng, Zixuan Xue, Yiming Yang

“文章使用ERP技术, 对比分析了安静、白噪声、汉语噪声、英语噪声四种听觉条件下, 听力正常的汉语母语者加工汉语句子的情况, 以探究信息掩蔽条件下语义加工的神经机制。研究发现不同噪声条件下诱发的N100、N400、LPC等ERPs成分具有不同的波形表现, 据此本文得出以下结论:首先, 在语音掩蔽条件下, 对于难度较大的语义加工, 目标语音与掩蔽噪声在知觉层面的相似性并非主要影响因素, 而掩蔽噪声语义内容上的可懂度发挥着更关键的作用。其次, 当言语噪声为听者极其熟悉或完全陌生的语言, 其对语义加工的掩蔽干扰较小, 而当掩蔽噪声为听者接触过的语言但不是母语或主要语言, 其掩蔽效应可能更强。最后, 不熟悉的言语噪声中所包含的出现频率较小但能够被听者理解的语义内容, 与听者的预期相冲突, 引发听者的注意转移, 这些语义信息被传输至听觉中枢神经, 占用了原本用于目标刺激的认知资源, 从而增强了信息掩蔽的效果。”

Long-tailed Extreme Multi-label Text Classification with Generated Pseudo Label Descriptions

no code implementations2 Apr 2022 Ruohong Zhang, Yau-Shian Wang, Yiming Yang, Donghan Yu, Tom Vu, Likun Lei

Extreme Multi-label Text Classification (XMTC) has been a tough challenge in machine learning research and applications due to the sheer sizes of the label spaces and the severe data scarce problem associated with the long tail of rare labels in highly skewed distributions.

Multi Label Text Classification Multi-Label Text Classification

Exploiting Local and Global Features in Transformer-based Extreme Multi-label Text Classification

no code implementations2 Apr 2022 Ruohong Zhang, Yau-Shian Wang, Yiming Yang, Tom Vu, Likun Lei

Extreme multi-label text classification (XMTC) is the task of tagging each document with the relevant labels from a very large space of predefined categories.

Multi Label Text Classification Multi-Label Text Classification

Memory-assisted prompt editing to improve GPT-3 after deployment

1 code implementation16 Jan 2022 Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang

Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans.

Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback

1 code implementation16 Dec 2021 Niket Tandon, Aman Madaan, Peter Clark, Yiming Yang

Our goal is for an LM to continue to improve after deployment, without retraining, using feedback from the user.

Structured Prediction

Interscript: A dataset for interactive learning of scripts through error feedback

1 code implementation15 Dec 2021 Niket Tandon, Aman Madaan, Peter Clark, Keisuke Sakaguchi, Yiming Yang

We present a new dataset, Interscript, containing user feedback on a deployed model that generates complex everyday tasks.

Structured Prediction

Think about it! Improving defeasible reasoning by first modeling the question scenario

1 code implementation24 Oct 2021 Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter Clark, Yiming Yang, Eduard Hovy

Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.

Dual Encoding U-Net for Spatio-Temporal Domain Shift Frame Prediction

1 code implementation21 Oct 2021 Jay Santokhi, Dylan Hillier, Yiming Yang, Joned Sarwar, Anna Jordan, Emil Hewage

The landscape of city-wide mobility behaviour has altered significantly over the past 18 months.

Frame

KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering

no code implementations ACL 2022 Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng

The recent proposed Fusion-in-Decoder (FiD), which is built on top of the pretrained generative model T5, achieves the state-of-the-art performance in the reading module.

Answer Generation Open-Domain Question Answering +1

Sparse Attention with Learning to Hash

no code implementations ICLR 2022 Zhiqing Sun, Yiming Yang, Shinjae Yoo

To overcome these issues, this paper proposes a new strategy for sparse attention, namely LHA (Learning-to-Hash Attention), which directly learns separate parameterized hash functions for queries and keys, respectively.

Image Classification Language Modelling +1

Improving Neural Model Performance through Natural Language Feedback on Their Explanations

no code implementations18 Apr 2021 Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Yiming Yang, Peter Clark, Keisuke Sakaguchi, Ed Hovy

A class of explainable NLP models for reasoning tasks support their decisions by generating free-form or structured explanations, but what happens when these supporting structures contain errors?

Improving Hyper-Relational Knowledge Graph Completion

1 code implementation16 Apr 2021 Donghan Yu, Yiming Yang

Different from traditional knowledge graphs (KGs) where facts are represented as entity-relation-entity triplets, hyper-relational KGs (HKGs) allow triplets to be associated with additional relation-entity pairs (a. k. a qualifiers) to convey more complex information.

Knowledge Graph Completion

Meta Back-translation

1 code implementation ICLR 2021 Hieu Pham, Xinyi Wang, Yiming Yang, Graham Neubig

Back-translation is an effective strategy to improve the performance of Neural Machine Translation~(NMT) by generating pseudo-parallel data.

14 Machine Translation +2

Handling Noisy Labels via One-Step Abductive Multi-Target Learning: An Application to Helicobacter Pylori Segmentation

no code implementations25 Nov 2020 Yongquan Yang, Yiming Yang, Jie Chen, Jiayi Zheng, Zhongxi Zheng

This situation raises two more difficult problems: 1) the methodology of approaches making corrections corresponding to potentially noisy-labeled instances has limitations due to the complex noise existing in labels; and 2) the appropriate evaluation strategy for validation/testing is unclear because of the great difficulty in collecting the noisy-free ground-truth labels.

Rethinking Transformer-based Set Prediction for Object Detection

1 code implementation ICCV 2021 Zhiqing Sun, Shengcao Cao, Yiming Yang, Kris Kitani

DETR is a recently proposed Transformer-based method which views object detection as a set prediction problem and achieves state-of-the-art performance but demands extra-long training time to converge.

Object Detection

Neural Language Modeling for Contextualized Temporal Graph Generation

1 code implementation NAACL 2021 Aman Madaan, Yiming Yang

We address this challenge by using existing IE/NLP tools to automatically generate a large quantity (89, 000) of system-produced document-graph pairs, and propose a novel formulation of the contextualized graph generation problem as a sequence-to-sequence mapping task.

Graph Generation Language Modelling

Unsupervised Parallel Corpus Mining on Web Data

no code implementations18 Sep 2020 Guokun Lai, Zihang Dai, Yiming Yang

In contrast, there is a large-scale of parallel corpus created by humans on the Internet.

14 Machine Translation +2

Kernel Stein Generative Modeling

no code implementations6 Jul 2020 Wei-Cheng Chang, Chun-Liang Li, Youssef Mroueh, Yiming Yang

NCK is crucial for successful inference with SVGD in high dimension, as it adapts the kernel to the noise level of the score estimate.

Bayesian Inference

An EM Approach to Non-autoregressive Conditional Sequence Generation

1 code implementation ICML 2020 Zhiqing Sun, Yiming Yang

Autoregressive (AR) models have been the dominating approach to conditional sequence generation, but are suffering from the issue of high inference latency.

Machine Translation Translation

Knowledge Embedding Based Graph Convolutional Network

1 code implementation12 Jun 2020 Donghan Yu, Yiming Yang, Ruohong Zhang, Yuexin Wu

Recently, a considerable literature has grown up around the theme of Graph Convolutional Network (GCN).

Knowledge Graph Embedding Knowledge Graphs

Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing

3 code implementations NeurIPS 2020 Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le

With the success of language pretraining, it is highly desirable to develop more efficient architectures of good scalability that can exploit the abundant unlabeled data at a lower cost.

Reading Comprehension Text Classification

Predicting Performance for Natural Language Processing Tasks

1 code implementation ACL 2020 Mengzhou Xia, Antonios Anastasopoulos, Ruochen Xu, Yiming Yang, Graham Neubig

Given the complexity of combinations of tasks, languages, and domains in natural language processing (NLP) research, it is computationally prohibitive to exhaustively test newly proposed models on each possible experimental setting.

Politeness Transfer: A Tag and Generate Approach

1 code implementation ACL 2020 Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. black, Shrimai Prabhumoye

This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning.

Style Transfer TAG

Practical Comparable Data Collection for Low-Resource Languages via Images

1 code implementation24 Apr 2020 Aman Madaan, Shruti Rijhwani, Antonios Anastasopoulos, Yiming Yang, Graham Neubig

We propose a method of curating high-quality comparable training data for low-resource languages with monolingual annotators.

Machine Translation Translation

VIOLIN: A Large-Scale Dataset for Video-and-Language Inference

1 code implementation CVPR 2020 Jingzhou Liu, Wenhu Chen, Yu Cheng, Zhe Gan, Licheng Yu, Yiming Yang, Jingjing Liu

We introduce a new task, Video-and-Language Inference, for joint multimodal understanding of video and text.

Pre-training Tasks for Embedding-based Large-scale Retrieval

no code implementations ICLR 2020 Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, Sanjiv Kumar

We consider the large-scale query-document retrieval problem: given a query (e. g., a question), return the set of relevant documents (e. g., paragraphs containing the answer) from a large document corpus.

Information Retrieval Link Prediction

Graph-Revised Convolutional Network

2 code implementations17 Nov 2019 Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, Yiming Yang

Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications.

XL-Editor: Post-editing Sentences with XLNet

no code implementations19 Oct 2019 Yong-Siang Shih, Wei-Cheng Chang, Yiming Yang

While neural sequence generation models achieve initial success for many NLP applications, the canonical decoding procedure with left-to-right generation order (i. e., autoregressive) in one-pass can not reflect the true nature of human revising a sentence to obtain a refined result.

Style Transfer Text Style Transfer

Active Learning for Graph Neural Networks via Node Feature Propagation

no code implementations16 Oct 2019 Yuexin Wu, Yichong Xu, Aarti Singh, Yiming Yang, Artur Dubrawski

Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.

Active Learning General Classification +1

Active Learning Graph Neural Networks via Node Feature Propagation

no code implementations25 Sep 2019 Yuexin Wu, Yichong Xu, Aarti Singh, Artur Dubrawski, Yiming Yang

Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.

Active Learning Node Classification

Bridging the domain gap in cross-lingual document classification

1 code implementation16 Sep 2019 Guokun Lai, Barlas Oguz, Yiming Yang, Veselin Stoyanov

We consider the setting of semi-supervised cross-lingual understanding, where labeled data is available in a source language (English), but only unlabeled data is available in the target language.

Classification Cross-Domain Document Classification +6

DEFT: A corpus for definition extraction in free- and semi-structured text

no code implementations WS 2019 Sasha Spala, Nicholas A. Miller, Yiming Yang, Franck Dernoncourt, Carl Dockhorn

Definition extraction has been a popular topic in NLP research for well more than a decade, but has been historically limited to well-defined, structured, and narrow conditions.

Definition Extraction

XLNet: Generalized Autoregressive Pretraining for Language Understanding

23 code implementations NeurIPS 2019 Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le

With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.

Audio Question Answering Chinese Reading Comprehension +9

Taming Pretrained Transformers for Extreme Multi-label Text Classification

1 code implementation7 May 2019 Wei-Cheng Chang, Hsiang-Fu Yu, Kai Zhong, Yiming Yang, Inderjit Dhillon

However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue.

Classification Extreme Multi-Label Classification +6

Implicit Kernel Learning

no code implementations26 Feb 2019 Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos

While learning the kernel in a data driven way has been investigated, in this paper we explore learning the spectral distribution of kernel via implicit generative models parametrized by deep neural networks.

Text Generation

The ARIEL-CMU Systems for LoReHLT18

no code implementations24 Feb 2019 Aditi Chaudhary, Siddharth Dalmia, Junjie Hu, Xinjian Li, Austin Matthews, Aldrian Obaja Muis, Naoki Otani, Shruti Rijhwani, Zaid Sheikh, Nidhi Vyas, Xinyi Wang, Jiateng Xie, Ruochen Xu, Chunting Zhou, Peter J. Jansen, Yiming Yang, Lori Levin, Florian Metze, Teruko Mitamura, David R. Mortensen, Graham Neubig, Eduard Hovy, Alan W. black, Jaime Carbonell, Graham V. Horwood, Shabnam Tafreshi, Mona Diab, Efsun S. Kayi, Noura Farra, Kathleen McKeown

This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).

Machine Translation Translation

Re-examination of the Role of Latent Variables in Sequence Modeling

1 code implementation NeurIPS 2019 Zihang Dai, Guokun Lai, Yiming Yang, Shinjae Yoo

With latent variables, stochastic recurrent models have achieved state-of-the-art performance in modeling sound-wave sequence.

Density Estimation Frame

An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation

no code implementations22 Jan 2019 Xiang Kong, Bohan Li, Graham Neubig, Eduard Hovy, Yiming Yang

In this work, we propose a method for neural dialogue response generation that allows not only generating semantically reasonable responses according to the dialogue history, but also explicitly controlling the sentiment of the response via sentiment labels.

Dialogue Generation Response Generation

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

27 code implementations ACL 2019 Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov

Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling.

Language Modelling

Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning

1 code implementation19 Nov 2018 Yuexin Wu, Xiujun Li, Jingjing Liu, Jianfeng Gao, Yiming Yang

Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences.

Active Learning Q-Learning +1

Unsupervised Cross-lingual Transfer of Word Embedding Spaces

1 code implementation EMNLP 2018 Ruochen Xu, Yiming Yang, Naoki Otani, Yuexin Wu

Supervised methods for this problem rely on the availability of cross-lingual supervision, either using parallel corpora or bilingual lexicons as the labeled data for training, which may not be available for many low resource languages.

Bilingual Lexicon Induction Cross-Lingual Transfer +3

Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data

1 code implementation15 Jun 2018 Guokun Lai, Bohan Li, Guoqing Zheng, Yiming Yang

In this paper, we combine the ideas from both stochastic latent variables and dilated convolutions, and propose a new architecture to model sequential data, termed as Stochastic WaveNet, where stochastic latent variables are injected into the WaveNet structure.

Learning Graph Convolution Filters from Data Manifold

no code implementations ICLR 2018 Guokun Lai, Hanxiao Liu, Yiming Yang

Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features.

Asymmetric Variational Autoencoders

1 code implementation20 Nov 2017 Guoqing Zheng, Yiming Yang, Jaime Carbonell

However, freely enriching the family of variational distribution is challenging since the ELBO requires variational likelihood evaluations of the latent variables.

Density Estimation Variational Inference

Convolutional Normalizing Flows

1 code implementation ICLR 2018 Guoqing Zheng, Yiming Yang, Jaime Carbonell

Variational inference provides one way to approximate the posterior distribution, however its expressive power is limited and so is the accuracy of resulting approximation.

Variational Inference

Learning Depthwise Separable Graph Convolution from Data Manifold

no code implementations31 Oct 2017 Guokun Lai, Hanxiao Liu, Yiming Yang

Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features.

MMD GAN: Towards Deeper Understanding of Moment Matching Network

2 code implementations NeurIPS 2017 Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos

In this paper, we propose to improve both the model expressiveness of GMMN and its computational efficiency by introducing adversarial kernel learning techniques, as the replacement of a fixed Gaussian kernel in the original GMMN.

Data-driven Random Fourier Features using Stein Effect

no code implementations23 May 2017 Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabas Poczos

Large-scale kernel approximation is an important problem in machine learning research.

Analogical Inference for Multi-Relational Embeddings

1 code implementation ICML 2017 Hanxiao Liu, Yuexin Wu, Yiming Yang

Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs.

Knowledge Graphs Link Prediction

Cross-lingual Distillation for Text Classification

1 code implementation ACL 2017 Ruochen Xu, Yiming Yang

Using soft probabilistic predictions for the documents in a label-rich language as the (induced) supervisory labels in a parallel corpus of documents, we train classifiers successfully for new languages in which labeled training data are not available.

Classification General Classification +2

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

19 code implementations21 Mar 2017 Guokun Lai, Wei-Cheng Chang, Yiming Yang, Hanxiao Liu

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.

Multivariate Time Series Forecasting Time Series +1

Co-Clustering for Multitask Learning

no code implementations3 Mar 2017 Keerthiram Murugesan, Jaime Carbonell, Yiming Yang

This paper presents a new multitask learning framework that learns a shared representation among the tasks, incorporating both task and feature clusters.

Leveraging Multilingual Training for Limited Resource Event Extraction

no code implementations COLING 2016 Andrew Hsi, Yiming Yang, Jaime Carbonell, Ruochen Xu

Event extraction has become one of the most important topics in information extraction, but to date, there is very limited work on leveraging cross-lingual training to boost performance.

Dependency Parsing Event Extraction +3

Adaptive Smoothed Online Multi-Task Learning

no code implementations NeurIPS 2016 Keerthiram Murugesan, Hanxiao Liu, Jaime Carbonell, Yiming Yang

This paper addresses the challenge of jointly learning both the per-task model parameters and the inter-task relationships in a multi-task online learning setting.

Multi-Task Learning online learning

Cross-Graph Learning of Multi-Relational Associations

no code implementations6 May 2016 Hanxiao Liu, Yiming Yang

Cross-graph Relational Learning (CGRL) refers to the problem of predicting the strengths or labels of multi-relational tuples of heterogeneous object types, through the joint inference over multiple graphs which specify the internal connections among each type of objects.

Graph Learning Relational Reasoning

Bayesian models for Large-scale Hierarchical Classification

no code implementations NeurIPS 2012 Siddharth Gopal, Yiming Yang, Bing Bai, Alexandru Niculescu-Mizil

A challenging problem in hierarchical classification is to leverage the hierarchical relations among classes for improving classification performance.

Classification General Classification

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