Search Results for author: William Yang Wang

Found 149 papers, 69 papers with code

Counterfactual Off-Policy Training for Neural Dialogue Generation

no code implementations EMNLP 2020 Qingfu Zhu, Wei-Nan Zhang, Ting Liu, William Yang Wang

Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses.

Dialogue Generation

Counterfactual Vision-and-Language Navigation via Adversarial Path Sampler

no code implementations ECCV 2020 Tsu-Jui Fu, Xin Eric Wang, Matthew F. Peterson,Scott T. Grafton, Miguel P. Eckstein, William Yang Wang

In particular, we present a model-agnostic adversarial path sampler (APS) that learns to sample challenging paths that force the navigator to improve based on the navigation performance.

Data Augmentation Vision and Language Navigation

FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue

1 code implementation12 May 2022 Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang

Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.

Dialogue Understanding Domain Adaptation +1

HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data

no code implementations Findings (ACL) 2022 Kai Nakamura, Sharon Levy, Yi-Lin Tuan, Wenhu Chen, William Yang Wang

A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities.

Response Generation

Imagination-Augmented Natural Language Understanding

1 code implementation18 Apr 2022 Yujie Lu, Wanrong Zhu, Xin Eric Wang, Miguel Eckstein, William Yang Wang

Human brains integrate linguistic and perceptual information simultaneously to understand natural language, and hold the critical ability to render imaginations.

Natural Language Understanding

End-to-end Dense Video Captioning as Sequence Generation

no code implementations18 Apr 2022 Wanrong Zhu, Bo Pang, Ashish Thapliyal, William Yang Wang, Radu Soricut

Dense video captioning aims to identify the events of interest in an input video, and generate descriptive captions for each event.

Dense Video Captioning

Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains

1 code implementation26 Jan 2022 Alon Albalak, Sharon Levy, William Yang Wang

Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains.

Question Answering Translation

Relation Leakage in Elicited Natural Language Inference Datasets

no code implementations16 Dec 2021 Michael Saxon, Xinyi Wang, Wenda Xu, William Yang Wang

Natural language inference (NLI) is an important task for producing useful models of human language.

Natural Language Inference

Relational Graph Learning for Grounded Video Description Generation

no code implementations2 Dec 2021 Wenqiao Zhang, Xin Eric Wang, Siliang Tang, Haizhou Shi, Haocheng Shi, Jun Xiao, Yueting Zhuang, William Yang Wang

Such a setting can help explain the decisions of captioning models and prevents the model from hallucinating object words in its description.

Graph Learning Video Description

VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling

1 code implementation24 Nov 2021 Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, Zicheng Liu

Further, unlike previous studies that found pre-training tasks on video inputs (e. g., masked frame modeling) not very effective, we design a new pre-training task, Masked Visual-token Modeling (MVM), for better video modeling.

Frame Question Answering +4

MIC: Model-agnostic Integrated Cross-channel Recommenders

no code implementations22 Oct 2021 Yujie Lu, Ping Nie, Shengyu Zhang, Ming Zhao, Ruobing Xie, William Yang Wang, Yi Ren

However, existing work are primarily built upon pre-defined retrieval channels, including User-CF (U2U), Item-CF (I2I), and Embedding-based Retrieval (U2I), thus access to the limited correlation between users and items which solely entail from partial information of latent interactions.

Recommendation Systems Semantic Similarity +1

ContraQA: Question Answering under Contradicting Contexts

no code implementations15 Oct 2021 Liangming Pan, Wenhu Chen, Min-Yen Kan, William Yang Wang

With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over contradicting information to derive correct answers.

Misinformation Question Answering

Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer

1 code implementation6 Oct 2021 Wenda Xu, Michael Saxon, Misha Sra, William Yang Wang

This is a particularly notable issue in the medical domain, where layman are often confused by medical text online.

Language Modelling Self-Supervised Learning +2

A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space

1 code implementation EMNLP 2021 Alex Jones, William Yang Wang, Kyle Mahowald

We verify some of our linguistic findings by looking at the effect of morphological segmentation on English-Inuktitut alignment, in addition to examining the effect of word order agreement on isomorphism for 66 zero-shot language pairs from a different corpus.

Pretrained Language Models

D-REX: Dialogue Relation Extraction with Explanations

1 code implementation NLP4ConvAI (ACL) 2022 Alon Albalak, Varun Embar, Yi-Lin Tuan, Lise Getoor, William Yang Wang

Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods.

Dialog Relation Extraction Frame +2

FinQA: A Dataset of Numerical Reasoning over Financial Data

1 code implementation EMNLP 2021 Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, William Yang Wang

In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations.

Question Answering

Neural Stylistic Response Generation with Disentangled Latent Variables

no code implementations ACL 2021 Qingfu Zhu, Wei-Nan Zhang, Ting Liu, William Yang Wang

Generating open-domain conversational responses in the desired style usually suffers from the lack of parallel data in the style.

Response Generation

Local Explanation of Dialogue Response Generation

1 code implementation NeurIPS 2021 Yi-Lin Tuan, Connor Pryor, Wenhu Chen, Lise Getoor, William Yang Wang

To gain insights into the reasoning process of a generation model, we propose a new method, local explanation of response generation (LERG) that regards the explanations as the mutual interaction of segments in input and output sentences.

Response Generation Text Generation

ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation

no code implementations10 Jun 2021 Wanrong Zhu, Xin Eric Wang, An Yan, Miguel Eckstein, William Yang Wang

Automatic evaluations for natural language generation (NLG) conventionally rely on token-level or embedding-level comparisons with the text references.

Text Generation

Counterfactual Maximum Likelihood Estimation for Training Deep Networks

1 code implementation NeurIPS 2021 Xinyi Wang, Wenhu Chen, Michael Saxon, William Yang Wang

Although deep learning models have driven state-of-the-art performance on a wide array of tasks, they are prone to spurious correlations that should not be learned as predictive clues.

Domain Generalization Image Captioning +1

Semi-Supervised Policy Initialization for Playing Games with Language Hints

1 code implementation NAACL 2021 Tsu-Jui Fu, William Yang Wang

Using natural language as a hint can supply an additional reward for playing sparse-reward games.

Language-Driven Image Style Transfer

no code implementations1 Jun 2021 Tsu-Jui Fu, Xin Eric Wang, William Yang Wang

We propose contrastive language visual artist (CLVA) that learns to extract visual semantics from style instructions and accomplish LDAST by the patch-wise style discriminator.

Style Transfer

Zero-shot Fact Verification by Claim Generation

1 code implementation ACL 2021 Liangming Pan, Wenhu Chen, Wenhan Xiong, Min-Yen Kan, William Yang Wang

However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive.

Fact Verification

Comparing Visual Reasoning in Humans and AI

no code implementations29 Apr 2021 Shravan Murlidaran, William Yang Wang, Miguel P. Eckstein

Results show that the machine/human agreement scene descriptions are much lower than human/human agreement for our complex scenes.

Visual Reasoning

Gaze Perception in Humans and CNN-Based Model

no code implementations17 Apr 2021 Nicole X. Han, William Yang Wang, Miguel P. Eckstein

Making accurate inferences about other individuals' locus of attention is essential for human social interactions and will be important for AI to effectively interact with humans.

M3L: Language-based Video Editing via Multi-Modal Multi-Level Transformers

no code implementations2 Apr 2021 Tsu-Jui Fu, Xin Eric Wang, Scott T. Grafton, Miguel P. Eckstein, William Yang Wang

LBVE contains two features: 1) the scenario of the source video is preserved instead of generating a completely different video; 2) the semantic is presented differently in the target video, and all changes are controlled by the given instruction.

Frame Video Understanding

On Hallucination and Predictive Uncertainty in Conditional Language Generation

no code implementations EACL 2021 Yijun Xiao, William Yang Wang

Despite improvements in performances on different natural language generation tasks, deep neural models are prone to hallucinating facts that are incorrect or nonexistent.

Data-to-Text Generation Image Captioning

They, Them, Theirs: Rewriting with Gender-Neutral English

no code implementations12 Feb 2021 Tony Sun, Kellie Webster, Apu Shah, William Yang Wang, Melvin Johnson

Responsible development of technology involves applications being inclusive of the diverse set of users they hope to support.

L2C: Describing Visual Differences Needs Semantic Understanding of Individuals

no code implementations EACL 2021 An Yan, Xin Eric Wang, Tsu-Jui Fu, William Yang Wang

Recent advances in language and vision push forward the research of captioning a single image to describing visual differences between image pairs.

Image Captioning

DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents

no code implementations28 Jan 2021 Tsu-Jui Fu, William Yang Wang, Daniel McDuff, Yale Song

Creating presentation materials requires complex multimodal reasoning skills to summarize key concepts and arrange them in a logical and visually pleasing manner.

Document Summarization

Modeling Disclosive Transparency in NLP Application Descriptions

1 code implementation EMNLP 2021 Michael Saxon, Sharon Levy, Xinyi Wang, Alon Albalak, William Yang Wang

Broader disclosive transparency$-$truth and clarity in communication regarding the function of AI systems$-$is widely considered desirable.

Fairness Language Modelling

Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations

no code implementations EMNLP 2020 Wanrong Zhu, Xin Eric Wang, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang

A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings.

Text Generation

Investigating African-American Vernacular English in Transformer-Based Text Generation

1 code implementation EMNLP 2020 Sophie Groenwold, Lily Ou, Aesha Parekh, Samhita Honnavalli, Sharon Levy, Diba Mirza, William Yang Wang

The growth of social media has encouraged the written use of African American Vernacular English (AAVE), which has traditionally been used only in oral contexts.

Text Generation

KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation

1 code implementation EMNLP 2020 Wenhu Chen, Yu Su, Xifeng Yan, William Yang Wang

We propose a knowledge-grounded pre-training (KGPT), which consists of two parts, 1) a general knowledge-grounded generation model to generate knowledge-enriched text.

KG-to-Text Generation Transfer Learning

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

1 code implementation ICLR 2021 Wenhan Xiong, Xiang Lorraine Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Wen-tau Yih, Sebastian Riedel, Douwe Kiela, Barlas Oğuz

We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER.

Question Answering

SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning

1 code implementation EMNLP 2020 Tsu-Jui Fu, Xin Eric Wang, Scott Grafton, Miguel Eckstein, William Yang Wang

In this paper, we introduce a Self-Supervised Counterfactual Reasoning (SSCR) framework that incorporates counterfactual thinking to overcome data scarcity.

Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation

1 code implementation EACL 2021 Wanrong Zhu, Xin Eric Wang, Tsu-Jui Fu, An Yan, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang

Outdoor vision-and-language navigation (VLN) is such a task where an agent follows natural language instructions and navigates a real-life urban environment.

Ranked #4 on Vision and Language Navigation on Touchdown Dataset (using extra training data)

Style Transfer Text Style Transfer +1

Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection

no code implementations LREC 2020 Kai Nakamura, Sharon Levy, William Yang Wang

We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.

Classification Fake News Detection +1

Evaluating Transformer-Based Multilingual Text Classification

no code implementations29 Apr 2020 Sophie Groenwold, Samhita Honnavalli, Lily Ou, Aesha Parekh, Sharon Levy, Diba Mirza, William Yang Wang

As NLP tools become ubiquitous in today's technological landscape, they are increasingly applied to languages with a variety of typological structures.

Classification General Classification +4

Counterfactual Off-Policy Training for Neural Response Generation

no code implementations29 Apr 2020 Qingfu Zhu, Wei-Nan Zhang, Ting Liu, William Yang Wang

Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses.

Dialogue Generation Response Generation

Logical Natural Language Generation from Open-Domain Tables

1 code implementation ACL 2020 Wenhu Chen, Jianshu Chen, Yu Su, Zhiyu Chen, William Yang Wang

To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset \cite{chen2019tabfact} featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w. r. t.\ logical inference.

Text Generation

On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond

1 code implementation ACL 2020 Chen Wu, Prince Zizhuang Wang, William Yang Wang

To this end, we propose Coupled-VAE, which couples a VAE model with a deterministic autoencoder with the same structure and improves the encoder and decoder parameterizations via encoder weight sharing and decoder signal matching.

Dialogue Generation Language Modelling +1

Environment-agnostic Multitask Learning for Natural Language Grounded Navigation

1 code implementation ECCV 2020 Xin Eric Wang, Vihan Jain, Eugene Ie, William Yang Wang, Zornitsa Kozareva, Sujith Ravi

Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.

Vision-Language Navigation

HULK: An Energy Efficiency Benchmark Platform for Responsible Natural Language Processing

no code implementations EACL 2021 Xiyou Zhou, Zhiyu Chen, Xiaoyong Jin, William Yang Wang

We introduce HULK, a multi-task energy efficiency benchmarking platform for responsible natural language processing.

Disentangled Representation Learning with Wasserstein Total Correlation

no code implementations30 Dec 2019 Yijun Xiao, William Yang Wang

However, Kullback-Leibler (KL) divergence-based total correlation is metric-agnostic and sensitive to data samples.

Disentanglement

Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling

no code implementations17 Nov 2019 Tsu-Jui Fu, Xin Eric Wang, Matthew Peterson, Scott Grafton, Miguel Eckstein, William Yang Wang

In particular, we present a model-agnostic adversarial path sampler (APS) that learns to sample challenging paths that force the navigator to improve based on the navigation performance.

Data Augmentation Vision and Language Navigation

r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection

3 code implementations10 Nov 2019 Kai Nakamura, Sharon Levy, William Yang Wang

We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.

Classification Fake News Detection +1

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

Table-to-Text Natural Language Generation with Unseen Schemas

no code implementations9 Nov 2019 Tianyu Liu, Wei Wei, William Yang Wang

In this paper, we propose the new task of table-to-text NLG with unseen schemas, which specifically aims to test the generalization of NLG for input tables with attribute types that never appear during training.

Text Generation

Cross-Lingual Vision-Language Navigation

2 code implementations24 Oct 2019 An Yan, Xin Eric Wang, Jiangtao Feng, Lei LI, William Yang Wang

Commanding a robot to navigate with natural language instructions is a long-term goal for grounded language understanding and robotics.

Domain Adaptation Vision-Language Navigation +1

Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering

no code implementations WS 2019 Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Hong Wang, Shiyu Chang, Murray Campbell, William Yang Wang

To resolve this issue, we introduce a new sub-problem of open-domain multi-hop QA, which aims to recognize the bridge (\emph{i. e.}, the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model.

Information Retrieval Multi-hop Question Answering +2

Neural Correction Model for Open-Domain Named Entity Recognition

1 code implementation13 Sep 2019 Mengdi Zhu, Zheye Deng, Wenhan Xiong, Mo Yu, Ming Zhang, William Yang Wang

In this work, to address the low precision and recall problems, we first utilize DBpedia as the source of distant supervision to annotate abstracts from Wikipedia and design a neural correction model trained with a human-annotated NER dataset, DocRED, to correct the false entity labels.

Multi-Task Learning Named Entity Recognition +3

A Benchmark Dataset for Learning to Intervene in Online Hate Speech

1 code implementation IJCNLP 2019 Jing Qian, Anna Bethke, Yinyin Liu, Elizabeth Belding, William Yang Wang

In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.

Response Generation

Neural Gaussian Copula for Variational Autoencoder

no code implementations IJCNLP 2019 Prince Zizhuang Wang, William Yang Wang

We argue that this would cause a typical training problem called posterior collapse observed in all other variational language models.

TabFact: A Large-scale Dataset for Table-based Fact Verification

1 code implementation ICLR 2020 Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, William Yang Wang

To this end, we construct a large-scale dataset called TabFact with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED.

Fact Checking Fact Verification +3

Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization

no code implementations IJCNLP 2019 Siyao Li, Deren Lei, Pengda Qin, William Yang Wang

Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues.

Abstractive Text Summarization reinforcement-learning

Text Modeling with Syntax-Aware Variational Autoencoders

no code implementations27 Aug 2019 Yijun Xiao, William Yang Wang

We propose syntax-aware variational autoencoders (SAVAEs) that dedicate a subspace in the latent dimensions dubbed syntactic latent to represent syntactic structures of sentences.

Representation Learning

Meta Reasoning over Knowledge Graphs

no code implementations13 Aug 2019 Hong Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang

The ability to reason over learned knowledge is an innate ability for humans and humans can easily master new reasoning rules with only a few demonstrations.

Few-Shot Learning Knowledge Base Completion +1

What Should I Ask? Using Conversationally Informative Rewards for Goal-Oriented Visual Dialog

no code implementations28 Jul 2019 Pushkar Shukla, Carlos Elmadjian, Richika Sharan, Vivek Kulkarni, Matthew Turk, William Yang Wang

In this work, we focus on the task of goal-oriented visual dialogue, aiming to automatically generate a series of questions about an image with a single objective.

Visual Dialog

TWEETQA: A Social Media Focused Question Answering Dataset

no code implementations ACL 2019 Wenhan Xiong, Jiawei Wu, Hong Wang, Vivek Kulkarni, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge.

Question Answering

What Should I Ask? Using Conversationally Informative Rewards for Goal-oriented Visual Dialog.

no code implementations ACL 2019 Pushkar Shukla, Carlos Elmadjian, Richika Sharan, Vivek Kulkarni, Matthew Turk, William Yang Wang

In this work, we focus on the task of goal-oriented visual dialogue, aiming to automatically generate a series of questions about an image with a single objective.

Visual Dialog

Self-Supervised Dialogue Learning

no code implementations ACL 2019 Jiawei Wu, Xin Wang, William Yang Wang

The sequential order of utterances is often meaningful in coherent dialogues, and the order changes of utterances could lead to low-quality and incoherent conversations.

Self-Supervised Learning

Self-Supervised Learning for Contextualized Extractive Summarization

1 code implementation ACL 2019 Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang

Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.

Extractive Summarization Self-Supervised Learning

Deep Adversarial Learning for NLP

no code implementations NAACL 2019 William Yang Wang, Sameer Singh, Jiwei Li

Adversarial learning is a game-theoretic learning paradigm, which has achieved huge successes in the field of Computer Vision recently.

Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader

2 code implementations ACL 2019 Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets.

Question Answering

REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments

1 code implementation CVPR 2020 Yuankai Qi, Qi Wu, Peter Anderson, Xin Wang, William Yang Wang, Chunhua Shen, Anton Van Den Hengel

One of the long-term challenges of robotics is to enable robots to interact with humans in the visual world via natural language, as humans are visual animals that communicate through language.

Referring Expression Vision and Language Navigation

Few-Shot NLG with Pre-Trained Language Model

1 code implementation ACL 2020 Zhiyu Chen, Harini Eavani, Wenhu Chen, Yinyin Liu, William Yang Wang

Neural-based end-to-end approaches to natural language generation (NLG) from structured data or knowledge are data-hungry, making their adoption for real-world applications difficult with limited data.

Few-Shot Learning Language Modelling +1

VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research

2 code implementations ICCV 2019 Xin Wang, Jiawei Wu, Junkun Chen, Lei LI, Yuan-Fang Wang, William Yang Wang

We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.

Machine Translation Translation +3

Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling

1 code implementation NAACL 2019 Prince Zizhuang Wang, William Yang Wang

The RNF transforms a latent variable into a space that respects the geometric characteristics of input space, which makes posterior impossible to collapse to the non-informative prior.

Language Modelling Text Generation

Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation

no code implementations NAACL 2019 Jiawei Wu, Xin Wang, William Yang Wang

The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs.

Translation Unsupervised Machine Translation

Learning to Decipher Hate Symbols

no code implementations NAACL 2019 Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang

Furthermore, we propose a novel Variational Decipher and show how it can generalize better to unseen hate symbols in a more challenging testing setting.

Frame General Classification

Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing

1 code implementation NAACL 2019 Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance.

Entity Typing

Sentence Embedding Alignment for Lifelong Relation Extraction

2 code implementations NAACL 2019 Hong Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang

We formulate such a challenging problem as lifelong relation extraction and investigate memory-efficient incremental learning methods without catastrophically forgetting knowledge learned from previous tasks.

Incremental Learning Relation Extraction +2

Quantifying Uncertainties in Natural Language Processing Tasks

no code implementations18 Nov 2018 Yijun Xiao, William Yang Wang

Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems.

Language Modelling Named Entity Recognition +1

Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning

1 code implementation7 Nov 2018 Xin Wang, Jiawei Wu, Da Zhang, Yu Su, William Yang Wang

Although promising results have been achieved in video captioning, existing models are limited to the fixed inventory of activities in the training corpus, and do not generalize to open vocabulary scenarios.

Video Captioning

SafeRoute: Learning to Navigate Streets Safely in an Urban Environment

1 code implementation3 Nov 2018 Sharon Levy, Wenhan Xiong, Elizabeth Belding, William Yang Wang

We propose SafeRoute, a novel solution to the problem of navigating cities and avoiding street harassment and crime.

Representation Learning

A Survey on Natural Language Processing for Fake News Detection

1 code implementation LREC 2020 Ray Oshikawa, Jing Qian, William Yang Wang

We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.

Fake News Detection

Towards Explainable NLP: A Generative Explanation Framework for Text Classification

no code implementations ACL 2019 Hui Liu, Qingyu Yin, William Yang Wang

Building explainable systems is a critical problem in the field of Natural Language Processing (NLP), since most machine learning models provide no explanations for the predictions.

Classification General Classification +1

DOLORES: Deep Contextualized Knowledge Graph Embeddings

no code implementations AKBC 2020 Haoyu Wang, Vivek Kulkarni, William Yang Wang

We introduce a new method DOLORES for learning knowledge graph embeddings that effectively captures contextual cues and dependencies among entities and relations.

Knowledge Graph Embeddings Knowledge Graphs +2

Dirichlet Variational Autoencoder for Text Modeling

no code implementations31 Oct 2018 Yijun Xiao, Tiancheng Zhao, William Yang Wang

We introduce an improved variational autoencoder (VAE) for text modeling with topic information explicitly modeled as a Dirichlet latent variable.

Analyzing and Interpreting Convolutional Neural Networks in NLP

no code implementations18 Oct 2018 Mahnaz Koupaee, William Yang Wang

Convolutional neural networks have been successfully applied to various NLP tasks.

Decision Making

WikiHow: A Large Scale Text Summarization Dataset

9 code implementations18 Oct 2018 Mahnaz Koupaee, William Yang Wang

Sequence-to-sequence models have recently gained the state of the art performance in summarization.

Text Summarization

Hierarchical CVAE for Fine-Grained Hate Speech Classification

no code implementations EMNLP 2018 Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang

Existing work on automated hate speech detection typically focuses on binary classification or on differentiating among a small set of categories.

Classification General Classification +1

XL-NBT: A Cross-lingual Neural Belief Tracking Framework

1 code implementation EMNLP 2018 Wenhu Chen, Jianshu Chen, Yu Su, Xin Wang, Dong Yu, Xifeng Yan, William Yang Wang

Then, we pre-train a state tracker for the source language as a teacher, which is able to exploit easy-to-access parallel data.

Transfer Learning

Zero Pronoun Resolution with Attention-based Neural Network

1 code implementation COLING 2018 Qingyu Yin, Yu Zhang, Wei-Nan Zhang, Ting Liu, William Yang Wang

Recent neural network methods for zero pronoun resolution explore multiple models for generating representation vectors for zero pronouns and their candidate antecedents.

Chinese Zero Pronoun Resolution

Deep Reinforcement Learning for NLP

no code implementations ACL 2018 William Yang Wang, Jiwei Li, Xiaodong He

Many Natural Language Processing (NLP) tasks (including generation, language grounding, reasoning, information extraction, coreference resolution, and dialog) can be formulated as deep reinforcement learning (DRL) problems.

Atari Games Coreference Resolution +6

Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

3 code implementations16 Jun 2018 Wenhan Xiong, Xiaoxiao Guo, Mo Yu, Shiyu Chang, Bo-Wen Zhou, William Yang Wang

We investigate the task of learning to follow natural language instructions by jointly reasoning with visual observations and language inputs.

Efficient Exploration reinforcement-learning

Scalable Construction and Reasoning of Massive Knowledge Bases

no code implementations NAACL 2018 Xiang Ren, Nanyun Peng, William Yang Wang

In today{'}s information-based society, there is abundant knowledge out there carried in the form of natural language texts (e. g., news articles, social media posts, scientific publications), which spans across various domains (e. g., corporate documents, advertisements, legal acts, medical reports), which grows at an astonishing rate.

Simple Models for Word Formation in Slang

1 code implementation NAACL 2018 Vivek Kulkarni, William Yang Wang

We propose the first generative models for three types of extra-grammatical word formation phenomena abounding in slang: Blends, Clippings, and Reduplicatives.

DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction

no code implementations ACL 2018 Pengda Qin, Weiran Xu, William Yang Wang

Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem.

Relation Classification

Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

2 code implementations ACL 2018 Pengda Qin, Weiran Xu, William Yang Wang

The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.

reinforcement-learning Relation Extraction

No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling

2 code implementations ACL 2018 Xin Wang, Wenhu Chen, Yuan-Fang Wang, William Yang Wang

Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem.

Image Captioning Visual Storytelling

Reinforced Co-Training

no code implementations NAACL 2018 Jiawei Wu, Lei LI, William Yang Wang

However, the selection of samples in existing co-training methods is based on a predetermined policy, which ignores the sampling bias between the unlabeled and the labeled subsets, and fails to explore the data space.

Clickbait Detection General Classification +2

Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media

2 code implementations11 Apr 2018 Mai ElSherief, Vivek Kulkarni, Dana Nguyen, William Yang Wang, Elizabeth Belding

While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech.

Simple Models for Word Formation in English Slang

1 code implementation7 Apr 2018 Vivek Kulkarni, William Yang Wang

We propose generative models for three types of extra-grammatical word formation phenomena abounding in English slang: Blends, Clippings, and Reduplicatives.

Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

1 code implementation ECCV 2018 Xin Wang, Wenhan Xiong, Hongmin Wang, William Yang Wang

In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices---We propose a novel, planned-ahead hybrid reinforcement learning model that combines model-free and model-based reinforcement learning to solve a real-world vision-language navigation task.

Model-based Reinforcement Learning reinforcement-learning +3

TFW, DamnGina, Juvie, and Hotsie-Totsie: On the Linguistic and Social Aspects of Internet Slang

no code implementations22 Dec 2017 Vivek Kulkarni, William Yang Wang

In this work, we use UrbanDictionary to conduct the first large-scale linguistic analysis of slang and its social aspects on the Internet to yield insights into this variety of language that is increasingly used all over the world online.

Question Answering

MojiTalk: Generating Emotional Responses at Scale

2 code implementations ACL 2018 Xianda Zhou, William Yang Wang

In this paper, we take a more radical approach: we exploit the idea of leveraging Twitter data that are naturally labeled with emojis.

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

3 code implementations NAACL 2018 Liwei Cai, William Yang Wang

This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks.

Knowledge Base Completion Knowledge Graph Embedding +3

Learning to Explain Non-Standard English Words and Phrases

1 code implementation IJCNLP 2017 Ke Ni, William Yang Wang

We describe a data-driven approach for automatically explaining new, non-standard English expressions in a given sentence, building on a large dataset that includes 15 years of crowdsourced examples from UrbanDictionary. com.

Deep Residual Learning for Weakly-Supervised Relation Extraction

1 code implementation EMNLP 2017 Yi Yao Huang, William Yang Wang

Deep residual learning (ResNet) is a new method for training very deep neural networks using identity map-ping for shortcut connections.

General Classification Relation Extraction

Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic

no code implementations12 Apr 2014 William Yang Wang, Kathryn Mazaitis, Ni Lao, Tom Mitchell, William W. Cohen

We show that the problem of constructing proofs for this logic is related to computation of personalized PageRank (PPR) on a linearized version of the proof space, and using on this connection, we develop a proveably-correct approximate grounding scheme, based on the PageRank-Nibble algorithm.

Relational Reasoning

Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic

no code implementations10 May 2013 William Yang Wang, Kathryn Mazaitis, William W. Cohen

In many probabilistic first-order representation systems, inference is performed by "grounding"---i. e., mapping it to a propositional representation, and then performing propositional inference.

Entity Resolution

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