Search Results for author: Kai-Wei Chang

Found 139 papers, 71 papers with code

Robustness and Adversarial Examples in Natural Language Processing

no code implementations EMNLP (ACL) 2021 Kai-Wei Chang, He He, Robin Jia, Sameer Singh

In particular, we will review recent studies on analyzing the weakness of NLP systems when facing adversarial inputs and data with a distribution shift.

Natural Language Processing

On the Transferability of Adversarial Attacks against Neural Text Classifier

no code implementations EMNLP 2021 Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, Kai-Wei Chang

Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models.

Text Classification

Improving the Adversarial Robustness of NLP Models by Information Bottleneck

1 code implementation Findings (ACL) 2022 Cenyuan Zhang, Xiang Zhou, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh

Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models.

Adversarial Robustness

Semantic Probabilistic Layers for Neuro-Symbolic Learning

no code implementations1 Jun 2022 Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van Den Broeck, Antonio Vergari

We design a predictive layer for structured-output prediction (SOP) that can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints.

Hierarchical Multi-label Classification

Controllable Text Generation with Neurally-Decomposed Oracle

no code implementations27 May 2022 Tao Meng, Sidi Lu, Nanyun Peng, Kai-Wei Chang

We propose a general and efficient framework to control auto-regressive generation models with NeurAlly-Decomposed Oracle (NADO).

Language Modelling Machine Translation +1

GENEVA: Pushing the Limit of Generalizability for Event Argument Extraction with 100+ Event Types

no code implementations25 May 2022 Tanmay Parekh, I-Hung Hsu, Kuan-Hao Huang, Kai-Wei Chang, Nanyun Peng

In order to cater to new events and domains in a realistic low-data setting, there is a growing urgency for EAE models to be generalizable.

Event Argument Extraction

GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models

no code implementations24 May 2022 Da Yin, Hritik Bansal, Masoud Monajatipoor, Liunian Harold Li, Kai-Wei Chang

However, it is not clear up to what extent do PLMs store geo-diverse commonsense knowledge, the knowledge associated with a culture and only shared locally.

Language Modelling

Conditional Supervised Contrastive Learning for Fair Text Classification

no code implementations23 May 2022 Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian

Contrastive representation learning has gained much attention due to its superior performance in learning representations from both image and sequential data.

Classification Contrastive Learning +3

Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks

no code implementations22 Apr 2022 Zhecan Wang, Noel Codella, Yen-Chun Chen, Luowei Zhou, Xiyang Dai, Bin Xiao, Jianwei Yang, Haoxuan You, Kai-Wei Chang, Shih-Fu Chang, Lu Yuan

Experiments demonstrate that MAD leads to consistent gains in the low-shot, domain-shifted, and fully-supervised conditions on VCR, SNLI-VE, and VQA, achieving SOTA performance on VCR compared to other single models pretrained with image-text data.

Question Answering Visual Commonsense Reasoning +3

Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies

no code implementations19 Apr 2022 Md Rizwan Parvez, Jianfeng Chi, Wasi Uddin Ahmad, Yuan Tian, Kai-Wei Chang

Prior studies in privacy policies frame the question answering (QA) tasks as identifying the most relevant text segment or a list of sentences from the policy document for a user query.

Data Augmentation Question Answering

An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks

1 code implementation31 Mar 2022 Kai-Wei Chang, Wei-Cheng Tseng, Shang-Wen Li, Hung-Yi Lee

We report in this paper the first exploration of the prompt tuning paradigm for speech processing tasks based on Generative Spoken Language Model (GSLM).

Language Modelling Natural Language Processing +1

Measuring Fairness of Text Classifiers via Prediction Sensitivity

no code implementations ACL 2022 Satyapriya Krishna, Rahul Gupta, Apurv Verma, Jwala Dhamala, Yada Pruksachatkun, Kai-Wei Chang

With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions.

Fairness Text Classification

Representation Learning for Resource-Constrained Keyphrase Generation

1 code implementation15 Mar 2022 Di wu, Wasi Uddin Ahmad, Sunipa Dev, Kai-Wei Chang

State-of-the-art keyphrase generation methods generally depend on large annotated datasets, limiting their performance in domains with limited annotated data.

Denoising Keyphrase Generation +2

A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models

no code implementations17 Feb 2022 Da Yin, Li Dong, Hao Cheng, Xiaodong Liu, Kai-Wei Chang, Furu Wei, Jianfeng Gao

With the increasing of model capacity brought by pre-trained language models, there emerges boosting needs for more knowledgeable natural language processing (NLP) models with advanced functionalities including providing and making flexible use of encyclopedic and commonsense knowledge.

Language Modelling Natural Language Processing

Neuro-Symbolic Entropy Regularization

no code implementations25 Jan 2022 Kareem Ahmed, Eric Wang, Kai-Wei Chang, Guy Van Den Broeck

We propose a loss, neuro-symbolic entropy regularization, that encourages the model to confidently predict a valid object.

Structured Prediction

SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning

no code implementations16 Dec 2021 Zhecan Wang, Haoxuan You, Liunian Harold Li, Alireza Zareian, Suji Park, Yiqing Liang, Kai-Wei Chang, Shih-Fu Chang

As for pre-training, a scene-graph-aware pre-training method is proposed to leverage structure knowledge extracted in the visual scene graph.

Visual Commonsense Reasoning

Grounded Language-Image Pre-training

1 code implementation CVPR 2022 Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, Jianfeng Gao

The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representation semantic-rich.

Ranked #2 on Phrase Grounding on Flickr30k Entities Test (using extra training data)

object-detection Object Detection +1

Socially Aware Bias Measurements for Hindi Language Representations

1 code implementation15 Oct 2021 Vijit Malik, Sunipa Dev, Akihiro Nishi, Nanyun Peng, Kai-Wei Chang

Language representations are efficient tools used across NLP applications, but they are strife with encoded societal biases.

Toward Degradation-Robust Voice Conversion

no code implementations14 Oct 2021 Chien-yu Huang, Kai-Wei Chang, Hung-Yi Lee

However, in real-world scenarios, it is difficult to collect clean utterances of a speaker, and they are usually degraded by noises or reverberations.

Denoising Speech Enhancement +1

Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution

1 code implementation EMNLP 2021 Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh

Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models.

DEGREE: A Data-Efficient Generation-Based Event Extraction Model

1 code implementation29 Aug 2021 I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng

Given a passage and a manually designed prompt, DEGREE learns to summarize the events mentioned in the passage into a natural sentence that follows a predefined pattern.

Event Extraction Structured Prediction +1

AVATAR: A Parallel Corpus for Java-Python Program Translation

1 code implementation26 Aug 2021 Wasi Uddin Ahmad, Md Golam Rahman Tushar, Saikat Chakraborty, Kai-Wei Chang

Program translation refers to migrating source code from one programming language to another.


Retrieval Augmented Code Generation and Summarization

1 code implementation Findings (EMNLP) 2021 Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang

To mimic developers' code or summary generation behavior, we propose a retrieval augmented framework, REDCODER, that retrieves relevant code or summaries from a retrieval database and provides them as a supplement to code generation or summarization models.

 Ranked #1 on Code Generation on CodeXGLUE - CodeSearchNet (using extra training data)

Code Generation Code Summarization

What do Bias Measures Measure?

no code implementations7 Aug 2021 Sunipa Dev, Emily Sheng, Jieyu Zhao, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Nanyun Peng, Kai-Wei Chang

To address this gap, this work presents a comprehensive survey of existing bias measures in NLP as a function of the associated NLP tasks, metrics, datasets, and social biases and corresponding harms.

Natural Language Processing

Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble

no code implementations ACL 2021 Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang

Although deep neural networks have achieved prominent performance on many NLP tasks, they are vulnerable to adversarial examples.

How Much Can CLIP Benefit Vision-and-Language Tasks?

2 code implementations13 Jul 2021 Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, Kurt Keutzer

Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world.

Ranked #5 on Visual Entailment on SNLI-VE val (using extra training data)

Question Answering Visual Entailment +1

Clinical Named Entity Recognition using Contextualized Token Representations

no code implementations23 Jun 2021 Yichao Zhou, Chelsea Ju, J. Harry Caufield, Kevin Shih, Calvin Chen, Yizhou Sun, Kai-Wei Chang, Peipei Ping, Wei Wang

To facilitate various downstream applications using clinical case reports (CCRs), we pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) using the clinical-related corpus from the PubMed Central.

Language Modelling named-entity-recognition +3

Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification

no code implementations Findings (ACL) 2021 Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, Kai-Wei Chang

Existing bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training.

Data Augmentation Fairness +1

Syntax-augmented Multilingual BERT for Cross-lingual Transfer

1 code implementation ACL 2021 Wasi Uddin Ahmad, Haoran Li, Kai-Wei Chang, Yashar Mehdad

In recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transfer learning.

Cross-Lingual Transfer named-entity-recognition +5

Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?

1 code implementation Findings (ACL) 2021 Jieyu Zhao, Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Kai-Wei Chang

We investigate the effectiveness of natural language interventions for reading-comprehension systems, studying this in the context of social stereotypes.

Few-Shot Learning Question Answering +1

``Nice Try, Kiddo'': Investigating Ad Hominems in Dialogue Responses

no code implementations NAACL 2021 Emily Sheng, Kai-Wei Chang, Prem Natarajan, Nanyun Peng

Ad hominem attacks are those that target some feature of a person{'}s character instead of the position the person is maintaining.

Abusive Language

Societal Biases in Language Generation: Progress and Challenges

1 code implementation ACL 2021 Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng

Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner.

Fairness Text Generation

Evaluating the Values of Sources in Transfer Learning

1 code implementation NAACL 2021 Md Rizwan Parvez, Kai-Wei Chang

Transfer learning that adapts a model trained on data-rich sources to low-resource targets has been widely applied in natural language processing (NLP).

Cross-Lingual POS Tagging Natural Language Processing +1

Revealing Persona Biases in Dialogue Systems

1 code implementation18 Apr 2021 Emily Sheng, Josh Arnold, Zhou Yu, Kai-Wei Chang, Nanyun Peng

Dialogue systems in the form of chatbots and personal assistants are being increasingly integrated into people's lives.

On the Sensitivity and Stability of Model Interpretations in NLP

1 code implementation ACL 2022 Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang

We propose two new criteria, sensitivity and stability, that provide complementary notions of faithfulness to the existed removal-based criteria.

Adversarial Robustness Dependency Parsing +2

Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training

1 code implementation EMNLP 2021 Kuan-Hao Huang, Wasi Uddin Ahmad, Nanyun Peng, Kai-Wei Chang

Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer.

Text Classification Transfer Learning +1

Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models

1 code implementation NAACL 2021 James Y. Huang, Kuan-Hao Huang, Kai-Wei Chang

In this work, we present ParaBART, a semantic sentence embedding model that learns to disentangle semantics and syntax in sentence embeddings obtained by pre-trained language models.

Semantic Similarity Semantic Textual Similarity +2

Unified Pre-training for Program Understanding and Generation

1 code implementation NAACL 2021 Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang

Experiments on code summarization in the English language, code generation, and code translation in seven programming languages show that PLBART outperforms or rivals state-of-the-art models.

Clone Detection Code Generation +7

CREATe: Clinical Report Extraction and Annotation Technology

no code implementations28 Feb 2021 Yichao Zhou, Wei-Ting Chen, BoWen Zhang, David Lee, J. Harry Caufield, Kai-Wei Chang, Yizhou Sun, Peipei Ping, Wei Wang

Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies.

BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation

1 code implementation27 Jan 2021 Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta

To systematically study and benchmark social biases in open-ended language generation, we introduce the Bias in Open-Ended Language Generation Dataset (BOLD), a large-scale dataset that consists of 23, 679 English text generation prompts for bias benchmarking across five domains: profession, gender, race, religion, and political ideology.

Text Generation

Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs

1 code implementation EACL 2021 Kuan-Hao Huang, Kai-Wei Chang

We also demonstrate that the performance of SynPG is competitive or even better than supervised models when the unannotated data is large.

Data Augmentation Disentanglement +1

Intent Classification and Slot Filling for Privacy Policies

1 code implementation ACL 2021 Wasi Uddin Ahmad, Jianfeng Chi, Tu Le, Thomas Norton, Yuan Tian, Kai-Wei Chang

We refer to predicting the privacy practice explained in a sentence as intent classification and identifying the text spans sharing specific information as slot filling.

Classification General Classification +3

Generating Sports News from Live Commentary: A Chinese Dataset for Sports Game Summarization

1 code implementation Asian Chapter of the Association for Computational Linguistics 2020 Kuan-Hao Huang, Chen Li, Kai-Wei Chang

To deeply study this task, we present SportsSum, a Chinese sports game summarization dataset which contains 5, 428 soccer games of live commentaries and the corresponding news articles.

On the Transferability of Adversarial Attacksagainst Neural Text Classifier

no code implementations17 Nov 2020 Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, Kai-Wei Chang

Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models.

Text Classification

Cross-Lingual Dependency Parsing by POS-Guided Word Reordering

no code implementations Findings of the Association for Computational Linguistics 2020 Lu Liu, Yi Zhou, Jianhan Xu, Xiaoqing Zheng, Kai-Wei Chang, Xuanjing Huang

The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM).

Dependency Parsing Language Modelling +1

"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue Responses

1 code implementation24 Oct 2020 Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng

Ad hominem attacks are those that target some feature of a person's character instead of the position the person is maintaining.

Abusive Language

GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction

1 code implementation6 Oct 2020 Wasi Uddin Ahmad, Nanyun Peng, Kai-Wei Chang

Recent progress in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations such that models trained on one language can be applied to other languages.

Event Extraction Graph Attention

PolicyQA: A Reading Comprehension Dataset for Privacy Policies

1 code implementation Findings of the Association for Computational Linguistics 2020 Wasi Uddin Ahmad, Jianfeng Chi, Yuan Tian, Kai-Wei Chang

Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question.

Question Answering Reading Comprehension

What Does BERT with Vision Look At?

no code implementations ACL 2020 Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang

Pre-trained visually grounded language models such as ViLBERT, LXMERT, and UNITER have achieved significant performance improvement on vision-and-language tasks but what they learn during pre-training remains unclear.

Language Modelling

``The Boating Store Had Its Best Sail Ever'': Pronunciation-attentive Contextualized Pun Recognition

no code implementations ACL 2020 Yichao Zhou, Jyun-Yu Jiang, Jieyu Zhao, Kai-Wei Chang, Wei Wang

In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence.

GPT-GNN: Generative Pre-Training of Graph Neural Networks

2 code implementations27 Jun 2020 Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun

Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.

Graph Generation

Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble

1 code implementation20 Jun 2020 Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang

Despite neural networks have achieved prominent performance on many natural language processing (NLP) tasks, they are vulnerable to adversarial examples.

Natural Language Processing

An Integer Linear Programming Framework for Mining Constraints from Data

1 code implementation18 Jun 2020 Tao Meng, Kai-Wei Chang

This raises a question -- \emph{can we mine constraints and rules from data based on a learning algorithm?}

Multi-class Classification Multi-Label Classification

On the Robustness of Language Encoders against Grammatical Errors

1 code implementation ACL 2020 Fan Yin, Quanyu Long, Tao Meng, Kai-Wei Chang

We conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors.

Cloze Test Linguistic Acceptability

"The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition

1 code implementation29 Apr 2020 Yichao Zhou, Jyun-Yu Jiang, Jieyu Zhao, Kai-Wei Chang, Wei Wang

In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence.

Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond

5 code implementations NeurIPS 2020 Kaidi Xu, Zhouxing Shi, huan zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh

Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense.


Robustness Verification for Transformers

1 code implementation ICLR 2020 Zhouxing Shi, huan zhang, Kai-Wei Chang, Minlie Huang, Cho-Jui Hsieh

Robustness verification that aims to formally certify the prediction behavior of neural networks has become an important tool for understanding model behavior and obtaining safety guarantees.

Sentiment Analysis

Towards Understanding Gender Bias in Relation Extraction

1 code implementation ACL 2020 Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang

We use WikiGenderBias to evaluate systems for bias and find that NRE systems exhibit gender biased predictions and lay groundwork for future evaluation of bias in NRE.

Data Augmentation Relation Extraction +1

Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages

1 code implementation CONLL 2019 Wasi Uddin Ahmad, Zhisong Zhang, Xuezhe Ma, Kai-Wei Chang, Nanyun Peng

We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages.

Cross-Lingual Transfer Dependency Parsing +2

Visualizing Trends of Key Roles in News Articles

1 code implementation IJCNLP 2019 Chen Xia, Haoxiang Zhang, Jacob Moghtader, Allen Wu, Kai-Wei Chang

There are tons of news articles generated every day reflecting the activities of key roles such as people, organizations and political parties.

Natural Language Processing

BOSH: An Efficient Meta Algorithm for Decision-based Attacks

no code implementations10 Sep 2019 Zhenxin Xiao, Puyudi Yang, Yuchen Jiang, Kai-Wei Chang, Cho-Jui Hsieh

Adversarial example generation becomes a viable method for evaluating the robustness of a machine learning model.

Adversarial Attack

Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification

1 code implementation IJCNLP 2019 Yichao Zhou, Jyun-Yu Jiang, Kai-Wei Chang, Wei Wang

To identify adversarial attacks, a perturbation discriminator validates how likely a token in the text is perturbed and provides a set of potential perturbations.

General Classification Sentiment Analysis +1

Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing

1 code implementation IJCNLP 2019 Tao Meng, Nanyun Peng, Kai-Wei Chang

Experiments show that the Lagrangian relaxation and posterior regularization inference improve the performances on 15 and 17 out of 19 target languages, respectively.

Dependency Parsing

The Woman Worked as a Babysitter: On Biases in Language Generation

1 code implementation IJCNLP 2019 Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng

We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups.

Language Modelling Text Generation +1

Few-Shot Representation Learning for Out-Of-Vocabulary Words

1 code implementation ACL 2019 Ziniu Hu, Ting Chen, Kai-Wei Chang, Yizhou Sun

Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts.

Learning Word Embeddings Meta-Learning

Learning Bilingual Word Embeddings Using Lexical Definitions

no code implementations WS 2019 Weijia Shi, Muhao Chen, Yingtao Tian, Kai-Wei Chang

Bilingual word embeddings, which representlexicons of different languages in a shared em-bedding space, are essential for supporting se-mantic and knowledge transfers in a variety ofcross-lingual NLP tasks.

Translation Word Alignment +1

Context Attentive Document Ranking and Query Suggestion

3 code implementations5 Jun 2019 Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang

We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance.

Document Ranking

Pre-Training Graph Neural Networks for Generic Structural Feature Extraction

no code implementations31 May 2019 Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, Yizhou Sun

With the proposed pre-training procedure, the generic structural information is learned and preserved, thus the pre-trained GNN requires less amount of labeled data and fewer domain-specific features to achieve high performance on different downstream tasks.


Dynamically Expanded CNN Array for Video Coding

no code implementations10 May 2019 Everett Fall, Kai-Wei Chang, Liang-Gee Chen

Marked progress has been made in video quality, compression, and computational efficiency.

Gender Bias in Contextualized Word Embeddings

1 code implementation NAACL 2019 Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, Kai-Wei Chang

In this paper, we quantify, analyze and mitigate gender bias exhibited in ELMo's contextualized word vectors.

Word Embeddings

Efficient Contextual Representation Learning Without Softmax Layer

no code implementations28 Feb 2019 Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh, Kai-Wei Chang

Our framework reduces the time spent on the output layer to a negligible level, eliminates almost all the trainable parameters of the softmax layer and performs language modeling without truncating the vocabulary.

Dimensionality Reduction Language Modelling +1

Quantification and Analysis of Scientific Language Variation Across Research Fields

no code implementations4 Dec 2018 Pei Zhou, Muhao Chen, Kai-Wei Chang, Carlo Zaniolo

Quantifying differences in terminologies from various academic domains has been a longstanding problem yet to be solved.

Language Modelling

Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations

2 code implementations ICCV 2019 Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, Vicente Ordonez

In this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables --such as gender-- in visual recognition tasks.

Temporal Action Localization

Learning Gender-Neutral Word Embeddings

1 code implementation EMNLP 2018 Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, Kai-Wei Chang

Word embedding models have become a fundamental component in a wide range of Natural Language Processing (NLP) applications.

Natural Language Processing Word Embeddings

Robust Text Classifier on Test-Time Budgets

1 code implementation IJCNLP 2019 Md. Rizwan Parvez, Tolga Bolukbasi, Kai-Wei Chang, Venkatesh Saligrama

We propose a generic and interpretable learning framework for building robust text classification model that achieves accuracy comparable to full models under test-time budget constraints.

General Classification Text Classification

Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

no code implementations18 Jun 2018 Muhao Chen, Yingtao Tian, Kai-Wei Chang, Steven Skiena, Carlo Zaniolo

Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions.

Entity Alignment Knowledge Graphs

LearningWord Embeddings for Low-resource Languages by PU Learning

1 code implementation9 May 2018 Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang

In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved.

Generating Natural Language Adversarial Examples

5 code implementations EMNLP 2018 Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, Kai-Wei Chang

Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify.

Natural Language Inference Sentiment Analysis

Counterexamples for Robotic Planning Explained in Structured Language

no code implementations23 Mar 2018 Lu Feng, Mahsa Ghasemi, Kai-Wei Chang, Ufuk Topcu

Automated techniques such as model checking have been used to verify models of robotic mission plans based on Markov decision processes (MDPs) and generate counterexamples that may help diagnose requirement violations.

Multi-Task Learning for Document Ranking and Query Suggestion

1 code implementation ICLR 2018 Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang

We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search.

Document Ranking Multi-Task Learning

Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context

no code implementations WS 2017 Shyam Upadhyay, Kai-Wei Chang, Matt Taddy, Adam Kalai, James Zou

We present a multi-view Bayesian non-parametric algorithm which improves multi-sense word embeddings by (a) using multilingual (i. e., more than two languages) corpora to significantly improve sense embeddings beyond what one achieves with bilingual information, and (b) uses a principled approach to learn a variable number of senses per word, in a data-driven manner.

Word Embeddings

Quantifying and Reducing Stereotypes in Word Embeddings

no code implementations20 Jun 2016 Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai

Machine learning algorithms are optimized to model statistical properties of the training data.

Word Embeddings

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 Better Than Your Teacher

no code implementations8 Feb 2015 Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford

Methods for learning to search for structured prediction typically imitate a reference policy, with existing theoretical guarantees demonstrating low regret compared to that reference.

Multi-Armed Bandits Structured Prediction

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.

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