Search Results for author: Jackie Chi Kit Cheung

Found 62 papers, 20 papers with code

Textual Time Travel: A Temporally Informed Approach to Theory of Mind

no code implementations Findings (EMNLP) 2021 Akshatha Arodi, Jackie Chi Kit Cheung

This capability, called theory of mind (ToM), is crucial, as it allows a model to predict and interpret the needs of users based on their mental states.

Natural Language Processing Question Answering

Post-Editing Extractive Summaries by Definiteness Prediction

no code implementations Findings (EMNLP) 2021 Jad Kabbara, Jackie Chi Kit Cheung

Moreover, based on an automatic evaluation study, we provide evidence for our system’s ability to generate linguistic decisions that lead to improved extractive summaries.

Extractive Summarization

MaskEval: Weighted MLM-Based Evaluation for Text Summarization and Simplification

no code implementations24 May 2022 Yu Lu Liu, Rachel Bawden, Thomas Scaliom, Benoît Sagot, Jackie Chi Kit Cheung

In text summarization and simplification, system outputs must be evaluated along multiple dimensions such as relevance, factual consistency, fluency, and grammaticality, and a wide range of possible outputs could be of high quality.

Language Modelling Masked Language Modeling +2

Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge

no code implementations16 Dec 2021 Ian Porada, Alessandro Sordoni, Jackie Chi Kit Cheung

Transformer models pre-trained with a masked-language-modeling objective (e. g., BERT) encode commonsense knowledge as evidenced by behavioral probes; however, the extent to which this knowledge is acquired by systematic inference over the semantics of the pre-training corpora is an open question.

Language Modelling Masked Language Modeling

Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization

1 code implementation ACL 2022 Meng Cao, Yue Dong, Jackie Chi Kit Cheung

State-of-the-art abstractive summarization systems often generate \emph{hallucinations}; i. e., content that is not directly inferable from the source text.

Abstractive Text Summarization reinforcement-learning

Modeling Event Plausibility with Consistent Conceptual Abstraction

1 code implementation NAACL 2021 Ian Porada, Kaheer Suleman, Adam Trischler, Jackie Chi Kit Cheung

Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events.

Common Sense Reasoning

The Topic Confusion Task: A Novel Scenario for Authorship Attribution

no code implementations17 Apr 2021 Malik H. Altakrori, Jackie Chi Kit Cheung, Benjamin C. M. Fung

Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors.

Pretrained Language Models

Characterizing Idioms: Conventionality and Contingency

no code implementations ACL 2022 Michaela Socolof, Jackie Chi Kit Cheung, Michael Wagner, Timothy J. O'Donnell

Second, the unconventional meaning of words in an idiom are contingent on the presence of the other words in the idiom.

TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion

1 code implementation17 Apr 2021 Jiapeng Wu, Yishi Xu, Yingxue Zhang, Chen Ma, Mark Coates, Jackie Chi Kit Cheung

The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.

Decision Making Information Retrieval +3

Discourse-Aware Unsupervised Summarization for Long Scientific Documents

1 code implementation EACL 2021 Yue Dong, Andrei Mircea, Jackie Chi Kit Cheung

We propose an unsupervised graph-based ranking model for extractive summarization of long scientific documents.

Extractive Summarization

On-the-Fly Attention Modulation for Neural Generation

no code implementations Findings (ACL) 2021 Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung, Yejin Choi

Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: the generated text is repetitive, generic, self-contradictory, and often lacks commonsense.

Language Modelling Text Generation

Optimizing Deeper Transformers on Small Datasets

1 code implementation ACL 2021 Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J. D. Prince, Yanshuai Cao

This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension.

Reading Comprehension Semantic Parsing +2

On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT

1 code implementation Joint Conference on Lexical and Computational Semantics 2020 Abhilasha Ravichander, Eduard Hovy, Kaheer Suleman, Adam Trischler, Jackie Chi Kit Cheung

In particular, we demonstrate through a simple consistency probe that the ability to correctly retrieve hypernyms in cloze tasks, as used in prior work, does not correspond to systematic knowledge in BERT.

Deconstructing word embedding algorithms

no code implementations EMNLP 2020 Kian Kenyon-Dean, Edward Newell, Jackie Chi Kit Cheung

Word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications.

Word Embeddings

An Analysis of Dataset Overlap on Winograd-Style Tasks

no code implementations COLING 2020 Ali Emami, Adam Trischler, Kaheer Suleman, Jackie Chi Kit Cheung

The Winograd Schema Challenge (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR).

Common Sense Reasoning

Learning Efficient Task-Specific Meta-Embeddings with Word Prisms

1 code implementation COLING 2020 Jingyi He, KC Tsiolis, Kian Kenyon-Dean, Jackie Chi Kit Cheung

Word embeddings are trained to predict word cooccurrence statistics, which leads them to possess different lexical properties (syntactic, semantic, etc.)

Word Embeddings

Factual Error Correction for Abstractive Summarization Models

1 code implementation EMNLP 2020 Meng Cao, Yue Dong, Jiapeng Wu, Jackie Chi Kit Cheung

Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset.

Abstractive Text Summarization

TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion

1 code implementation EMNLP 2020 Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, William L. Hamilton

Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.

Imputation Knowledge Graph Completion +1

Multi-Fact Correction in Abstractive Text Summarization

no code implementations EMNLP 2020 Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung, Jingjing Liu

Pre-trained neural abstractive summarization systems have dominated extractive strategies on news summarization performance, at least in terms of ROUGE.

Abstractive Text Summarization News Summarization +1

Deconstructing and reconstructing word embedding algorithms

no code implementations29 Nov 2019 Edward Newell, Kian Kenyon-Dean, Jackie Chi Kit Cheung

Uncontextualized word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications.

Word Embeddings

Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text

no code implementations WS 2019 Ian Porada, Kaheer Suleman, Jackie Chi Kit Cheung

Previous work has focused specifically on modeling physical plausibility and shown that distributional methods fail when tested in a supervised setting.

Natural Language Understanding Pretrained Language Models

On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs

no code implementations10 Nov 2019 Teng Long, Yanshuai Cao, Jackie Chi Kit Cheung

Variational autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic properties from local regularities of natural language.

Language Modelling

Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses

no code implementations IJCNLP 2019 Matt Grenander, Yue Dong, Jackie Chi Kit Cheung, Annie Louis

Sentence position is a strong feature for news summarization, since the lead often (but not always) summarizes the key points of the article.

News Summarization

EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing

1 code implementation ACL 2019 Yue Dong, Zichao Li, Mehdi Rezagholizadeh, Jackie Chi Kit Cheung

We present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-interpreter approach.

Machine Translation Text Simplification +1

Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples

no code implementations NAACL 2019 Krtin Kumar, Jackie Chi Kit Cheung

Neural abstractive summarizers generate summary texts using a language model conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarization datasets.

Language Modelling

On Variational Learning of Controllable Representations for Text without Supervision

1 code implementation ICML 2020 Peng Xu, Jackie Chi Kit Cheung, Yanshuai Cao

The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent space.

Style Transfer Text Style Transfer

Clustering-Oriented Representation Learning with Attractive-Repulsive Loss

1 code implementation18 Dec 2018 Kian Kenyon-Dean, Andre Cianflone, Lucas Page-Caccia, Guillaume Rabusseau, Jackie Chi Kit Cheung, Doina Precup

The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective.

General Classification Representation Learning

Multi-task Learning over Graph Structures

no code implementations26 Nov 2018 Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit Cheung

We present two architectures for multi-task learning with neural sequence models.

General Classification Multi-Task Learning +1

Contextualized Non-local Neural Networks for Sequence Learning

no code implementations21 Nov 2018 Pengfei Liu, Shuaichen Chang, Xuanjing Huang, Jian Tang, Jackie Chi Kit Cheung

Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which self-attention, as exemplified by the Transformer model, and graph neural networks (GNNs) have attracted much attention.

General Classification Text Classification

How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG

1 code implementation IJCNLP 2019 Paul Trichelair, Ali Emami, Adam Trischler, Kaheer Suleman, Jackie Chi Kit Cheung

Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG.

Common Sense Reasoning

The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution

1 code implementation ACL 2019 Ali Emami, Paul Trichelair, Adam Trischler, Kaheer Suleman, Hannes Schulz, Jackie Chi Kit Cheung

To explain this performance gap, we show empirically that state-of-the art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision.

Common Sense Reasoning Coreference Resolution +1

A Knowledge Hunting Framework for Common Sense Reasoning

no code implementations EMNLP 2018 Ali Emami, Noelia De La Cruz, Adam Trischler, Kaheer Suleman, Jackie Chi Kit Cheung

We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge.

Common Sense Reasoning

A Hierarchical Neural Attention-based Text Classifier

1 code implementation EMNLP 2018 Koustuv Sinha, Yue Dong, Jackie Chi Kit Cheung, Derek Ruths

Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification.

Classification General Classification +1

BanditSum: Extractive Summarization as a Contextual Bandit

1 code implementation EMNLP 2018 Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, Jackie Chi Kit Cheung

In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels.

Extractive Summarization Extractive Text Summarization +1

Let's do it ``again'': A First Computational Approach to Detecting Adverbial Presupposition Triggers

no code implementations ACL 2018 Andre Cianflone, Yulan Feng, Jad Kabbara, Jackie Chi Kit Cheung

We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as summarization and dialogue systems.

Language Modelling Text Generation

A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge

no code implementations NAACL 2018 Ali Emami, Adam Trischler, Kaheer Suleman, Jackie Chi Kit Cheung

We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA).

Common Sense Reasoning Coreference Resolution +1

World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions

no code implementations EMNLP 2017 Teng Long, Emmanuel Bengio, Ryan Lowe, Jackie Chi Kit Cheung, Doina Precup

Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading comprehension systems that can do the same.

Language Modelling Reading Comprehension

Predicting Success in Goal-Driven Human-Human Dialogues

no code implementations WS 2017 Michael Noseworthy, Jackie Chi Kit Cheung, Joelle Pineau

We then propose a turn-based hierarchical neural network model that can be used to predict success without requiring a structured goal definition.

Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs

no code implementations COLING 2016 Jad Kabbara, Yulan Feng, Jackie Chi Kit Cheung

We examine the potential of recurrent neural networks for handling pragmatic inferences involving complex contextual cues for the task of article usage prediction.

Grammatical Error Detection Machine Translation +1

Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data

no code implementations ACL 2016 Teng Long, Ryan Lowe, Jackie Chi Kit Cheung, Doina Precup

Recent work in learning vector-space embeddings for multi-relational data has focused on combining relational information derived from knowledge bases with distributional information derived from large text corpora.

Entity Embeddings

Cannot find the paper you are looking for? You can Submit a new open access paper.