Search Results for author: He He

Found 35 papers, 17 papers with code

Types of Out-of-Distribution Texts and How to Detect Them

1 code implementation14 Sep 2021 Udit Arora, William Huang, He He

Despite agreement on the importance of detecting out-of-distribution (OOD) examples, there is little consensus on the formal definition of OOD examples and how to best detect them.

Density Estimation Language Modelling +1

An Investigation of the (In)effectiveness of Counterfactually Augmented Data

1 code implementation1 Jul 2021 Nitish Joshi, He He

While pretrained language models achieve excellent performance on natural language understanding benchmarks, they tend to rely on spurious correlations and generalize poorly to out-of-distribution (OOD) data.

Natural Language Understanding

Unsupervised Extractive Summarization using Pointwise Mutual Information

1 code implementation EACL 2021 Vishakh Padmakumar, He He

Unsupervised approaches to extractive summarization usually rely on a notion of sentence importance defined by the semantic similarity between a sentence and the document.

Extractive Summarization Language Modelling +2

Text Generation by Learning from Demonstrations

1 code implementation ICLR 2021 Richard Yuanzhe Pang, He He

Current approaches to text generation largely rely on autoregressive models and maximum likelihood estimation.

Machine Translation Question Generation +2

An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models

1 code implementation14 Jul 2020 Lifu Tu, Garima Lalwani, Spandana Gella, He He

Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset.

Multi-Task Learning Natural Language Inference +1

Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach

1 code implementation3 Dec 2019 He He, Dongrui Wu

Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces.

Domain Adaptation Transfer Learning

A Dynamic Strategy Coach for Effective Negotiation

no code implementations WS 2019 Yiheng Zhou, He He, Alan W. black, Yulia Tsvetkov

We consider a bargaining scenario where a seller and a buyer negotiate the price of an item for sale through a text-based dialog.

Decision Making Text Generation

Multi-View Broad Learning System for Primate Oculomotor Decision Decoding

1 code implementation16 Aug 2019 Zhenhua Shi, Xiaomo Chen, Changming Zhao, He He, Veit Stuphorn, Dongrui Wu

Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source.

MULTI-VIEW LEARNING

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

4 code implementations9 Jul 2019 Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng, Yi Zhu

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating).

Pun Generation with Surprise

2 code implementations NAACL 2019 He He, Nanyun Peng, Percy Liang

We tackle the problem of generating a pun sentence given a pair of homophones (e. g., "died" and "dyed").

Language Modelling Text Generation

Quizbowl: The Case for Incremental Question Answering

no code implementations9 Apr 2019 Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, Jordan Boyd-Graber

Throughout this paper, we show that collaborations with the vibrant trivia community have contributed to the quality of our dataset, spawned new research directions, and doubled as an exciting way to engage the public with research in machine learning and natural language processing.

Decision Making Question Answering

QuAC: Question Answering in Context

no code implementations EMNLP 2018 Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total).

Information Seeking Question Answering +1

Decoupling Strategy and Generation in Negotiation Dialogues

2 code implementations EMNLP 2018 He He, Derek Chen, Anusha Balakrishnan, Percy Liang

We consider negotiation settings in which two agents use natural language to bargain on goods.

QuAC : Question Answering in Context

no code implementations21 Aug 2018 Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total).

Information Seeking Question Answering +1

Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach

no code implementations8 Aug 2018 He He, Dongrui Wu

Our approach has three desirable properties: 1) it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction and machine learning algorithms can then be applied to the aligned trials; 2) its computational cost is very low; and, 3) it is unsupervised and does not need any label information from the new subject.

EEG General Classification +1

Transfer Learning Enhanced Common Spatial Pattern Filtering for Brain Computer Interfaces (BCIs): Overview and a New Approach

no code implementations8 Aug 2018 He He, Dongrui Wu

The electroencephalogram (EEG) is the most widely used input for brain computer interfaces (BCIs), and common spatial pattern (CSP) is frequently used to spatially filter it to increase its signal-to-noise ratio.

EEG General Classification +1

Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context

1 code implementation ACL 2018 Urvashi Khandelwal, He He, Peng Qi, Dan Jurafsky

We know very little about how neural language models (LM) use prior linguistic context.

Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer

5 code implementations NAACL 2018 Juncen Li, Robin Jia, He He, Percy Liang

We consider the task of text attribute transfer: transforming a sentence to alter a specific attribute (e. g., sentiment) while preserving its attribute-independent content (e. g., changing "screen is just the right size" to "screen is too small").

Image Captioning Style Transfer +1

Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings

2 code implementations ACL 2017 He He, Anusha Balakrishnan, Mihail Eric, Percy Liang

To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses.

Knowledge Graph Embeddings

Opponent Modeling in Deep Reinforcement Learning

1 code implementation18 Sep 2016 He He, Jordan Boyd-Graber, Kevin Kwok, Hal Daumé III

Opponent modeling is necessary in multi-agent settings where secondary agents with competing goals also adapt their strategies, yet it remains challenging because strategies interact with each other and change.

Active Information Acquisition

no code implementations5 Feb 2016 He He, Paul Mineiro, Nikos Karampatziakis

We propose a general framework for sequential and dynamic acquisition of useful information in order to solve a particular task.

General Reinforcement Learning Sentiment Analysis

Learning to Search for Dependencies

no code implementations18 Mar 2015 Kai-Wei Chang, He He, Hal Daumé III, John Langford

We demonstrate that a dependency parser can be built using a credit assignment compiler which removes the burden of worrying about low-level machine learning details from the parser implementation.

Learning to Search in Branch and Bound Algorithms

no code implementations NeurIPS 2014 He He, Hal Daume III, Jason M. Eisner

Branch-and-bound is a widely used method in combinatorial optimization, including mixed integer programming, structured prediction and MAP inference.

Combinatorial Optimization Imitation Learning +1

A Credit Assignment Compiler for Joint Prediction

no code implementations NeurIPS 2016 Kai-Wei Chang, He He, Hal Daumé III, John Langford, Stephane Ross

Many machine learning applications involve jointly predicting multiple mutually dependent output variables.

Imitation Learning by Coaching

no code implementations NeurIPS 2012 He He, Jason Eisner, Hal Daume

However, it is important to note that these guarantees depend on how well the policy we found can imitate the oracle on the training data.

Feature Selection Imitation Learning

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