Search Results for author: Kathleen McKeown

Found 95 papers, 34 papers with code

Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies

no code implementations EMNLP 2020 Chris Kedzie, Kathleen McKeown

We study the degree to which neural sequence-to-sequence models exhibit fine-grained controllability when performing natural language generation from a meaning representation.

Data Augmentation Dialogue Generation

Towards Augmenting Lexical Resources for Slang and African American English

no code implementations VarDial (COLING) 2020 Alyssa Hwang, William R. Frey, Kathleen McKeown

Researchers in natural language processing have developed large, robust resources for understanding formal Standard American English (SAE), but we lack similar resources for variations of English, such as slang and African American English (AAE).

Clustering Word Embeddings

What Do Users Care About? Detecting Actionable Insights from User Feedback

no code implementations NAACL (ACL) 2022 Kasturi Bhattacharjee, Rashmi Gangadharaiah, Kathleen McKeown, Dan Roth

Users often leave feedback on a myriad of aspects of a product which, if leveraged successfully, can help yield useful insights that can lead to further improvements down the line.

Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport

1 code implementation EMNLP 2021 Manling Li, Tengfei Ma, Mo Yu, Lingfei Wu, Tian Gao, Heng Ji, Kathleen McKeown

Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged.

Timeline Summarization

Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers

1 code implementation2 Mar 2024 Melanie Subbiah, Sean Zhang, Lydia B. Chilton, Kathleen McKeown

We evaluate recent Large language Models (LLMs) on the challenging task of summarizing short stories, which can be lengthy, and include nuanced subtext or scrambled timelines.

Social Orientation: A New Feature for Dialogue Analysis

no code implementations26 Feb 2024 Todd Morrill, Zhaoyuan Deng, Yanda Chen, Amith Ananthram, Colin Wayne Leach, Kathleen McKeown

Based on these results showing the utility of social orientation tags for dialogue outcome prediction tasks, we release our data sets, code, and models that are fine-tuned to predict social orientation tags on dialogue utterances.

TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization

1 code implementation20 Feb 2024 Liyan Tang, Igor Shalyminov, Amy Wing-mei Wong, Jon Burnsky, Jake W. Vincent, Yu'an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, Kathleen McKeown

We find that there are diverse errors and error distributions in model-generated summaries and that non-LLM based metrics can capture all error types better than LLM-based evaluators.

Hallucination News Summarization +2

Parallel Structures in Pre-training Data Yield In-Context Learning

no code implementations19 Feb 2024 Yanda Chen, Chen Zhao, Zhou Yu, Kathleen McKeown, He He

Pre-trained language models (LMs) are capable of in-context learning (ICL): they can adapt to a task with only a few examples given in the prompt without any parameter update.

In-Context Learning

Fair Abstractive Summarization of Diverse Perspectives

1 code implementation14 Nov 2023 Yusen Zhang, Nan Zhang, Yixin Liu, Alexander Fabbri, Junru Liu, Ryo Kamoi, Xiaoxin Lu, Caiming Xiong, Jieyu Zhao, Dragomir Radev, Kathleen McKeown, Rui Zhang

However, current work in summarization metrics and Large Language Models (LLMs) evaluation has not explored fair abstractive summarization.

Abstractive Text Summarization Fairness

ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style Transfer

1 code implementation29 Aug 2023 Zachary Horvitz, Ajay Patel, Chris Callison-Burch, Zhou Yu, Kathleen McKeown

Our parameter-efficient approach, ParaGuide, leverages paraphrase-conditioned diffusion models alongside gradient-based guidance from both off-the-shelf classifiers and strong existing style embedders to transform the style of text while preserving semantic information.

Style Transfer

Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning

no code implementations10 Aug 2023 Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang

We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data.

Data-to-Text Generation

Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations

no code implementations17 Jul 2023 Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, Kathleen McKeown

To answer these questions, we propose to evaluate $\textbf{counterfactual simulatability}$ of natural language explanations: whether an explanation can enable humans to precisely infer the model's outputs on diverse counterfactuals of the explained input.

counterfactual

Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence

1 code implementation29 May 2023 Gengyu Wang, Kate Harwood, Lawrence Chillrud, Amith Ananthram, Melanie Subbiah, Kathleen McKeown

We present a new fact-checking benchmark, Check-COVID, that requires systems to verify claims about COVID-19 from news using evidence from scientific articles.

Fact Checking Sentence

Generating EDU Extracts for Plan-Guided Summary Re-Ranking

1 code implementation28 May 2023 Griffin Adams, Alexander R. Fabbri, Faisal Ladhak, Kathleen McKeown, Noémie Elhadad

Similarly, on 1k samples from CNN / DM, we show that prompting GPT-3 to follow EDU plans outperforms sampling-based methods by 1. 05 ROUGE-2 F1 points.

Language Modelling Re-Ranking

Unsupervised Selective Rationalization with Noise Injection

1 code implementation27 May 2023 Adam Storek, Melanie Subbiah, Kathleen McKeown

To address this problem, unsupervised selective rationalization produces rationales alongside predictions by chaining two jointly-trained components, a rationale generator and a predictor.

Evaluation of African American Language Bias in Natural Language Generation

no code implementations23 May 2023 Nicholas Deas, Jessi Grieser, Shana Kleiner, Desmond Patton, Elsbeth Turcan, Kathleen McKeown

We evaluate how well LLMs understand African American Language (AAL) in comparison to their performance on White Mainstream English (WME), the encouraged "standard" form of English taught in American classrooms.

Text Generation

ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media

no code implementations23 May 2023 Kung-Hsiang Huang, Hou Pong Chan, Kathleen McKeown, Heng Ji

We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information.

Fact Checking

Faithfulness-Aware Decoding Strategies for Abstractive Summarization

1 code implementation6 Mar 2023 David Wan, Mengwen Liu, Kathleen McKeown, Markus Dreyer, Mohit Bansal

We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive summarization.

Abstractive Text Summarization

Towards Detecting Harmful Agendas in News Articles

1 code implementation31 Jan 2023 Melanie Subbiah, Amrita Bhattacharjee, Yilun Hua, Tharindu Kumarage, Huan Liu, Kathleen McKeown

Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread.

Misinformation

Benchmarking Large Language Models for News Summarization

1 code implementation31 Jan 2023 Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, Tatsunori B. Hashimoto

Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood.

Benchmarking News Summarization

SWING: Balancing Coverage and Faithfulness for Dialogue Summarization

1 code implementation25 Jan 2023 Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, Kathleen McKeown

Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries.

Natural Language Inference

In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

no code implementations20 Dec 2022 Yukun Huang, Yanda Chen, Zhou Yu, Kathleen McKeown

We propose to combine in-context learning objectives with language modeling objectives to distill both the ability to read in-context examples and task knowledge to the smaller models.

Few-Shot Learning In-Context Learning +1

Legal and Political Stance Detection of SCOTUS Language

1 code implementation21 Nov 2022 Noah Bergam, Emily Allaway, Kathleen McKeown

As a natural extension of this political stance detection, we propose the more specialized task of legal stance detection with our new dataset SC-stance, which matches written opinions to legal questions.

Stance Detection

Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection

no code implementations9 Nov 2022 Hardy Hardy, Miguel Ballesteros, Faisal Ladhak, Muhammad Khalifa, Vittorio Castelli, Kathleen McKeown

Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout the chapter.

Abstractive Text Summarization Extractive Summarization

SafeText: A Benchmark for Exploring Physical Safety in Language Models

no code implementations18 Oct 2022 Sharon Levy, Emily Allaway, Melanie Subbiah, Lydia Chilton, Desmond Patton, Kathleen McKeown, William Yang Wang

Understanding what constitutes safe text is an important issue in natural language processing and can often prevent the deployment of models deemed harmful and unsafe.

Text Generation

Mitigating Covertly Unsafe Text within Natural Language Systems

no code implementations17 Oct 2022 Alex Mei, Anisha Kabir, Sharon Levy, Melanie Subbiah, Emily Allaway, John Judge, Desmond Patton, Bruce Bimber, Kathleen McKeown, William Yang Wang

An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences.

On the Relation between Sensitivity and Accuracy in In-context Learning

1 code implementation16 Sep 2022 Yanda Chen, Chen Zhao, Zhou Yu, Kathleen McKeown, He He

In-context learning (ICL) suffers from oversensitivity to the prompt, making it unreliable in real-world scenarios.

In-Context Learning Relation

Seeded Hierarchical Clustering for Expert-Crafted Taxonomies

no code implementations23 May 2022 Anish Saha, Amith Ananthram, Emily Allaway, Heng Ji, Kathleen McKeown

Practitioners from many disciplines (e. g., political science) use expert-crafted taxonomies to make sense of large, unlabeled corpora.

Clustering

Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions

no code implementations23 May 2022 Emily Allaway, Jena D. Hwang, Chandra Bhagavatula, Kathleen McKeown, Doug Downey, Yejin Choi

Generics express generalizations about the world (e. g., birds can fly) that are not universally true (e. g., newborn birds and penguins cannot fly).

Natural Language Inference

Learning to Revise References for Faithful Summarization

1 code implementation13 Apr 2022 Griffin Adams, Han-Chin Shing, Qing Sun, Christopher Winestock, Kathleen McKeown, Noémie Elhadad

In real-world scenarios with naturally occurring datasets, reference summaries are noisy and may contain information that cannot be inferred from the source text.

Attribute Clinical Knowledge +2

Read Top News First: A Document Reordering Approach for Multi-Document News Summarization

1 code implementation Findings (ACL) 2022 Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi

A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document.

Document Summarization News Summarization

Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation

1 code implementation10 Mar 2022 Kung-Hsiang Huang, Kathleen McKeown, Preslav Nakov, Yejin Choi, Heng Ji

Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation.

Fake News Detection Natural Language Inference +1

An analysis of document graph construction methods for AMR summarization

no code implementations27 Nov 2021 Fei-Tzin Lee, Chris Kedzie, Nakul Verma, Kathleen McKeown

Prior work in AMR-based summarization has automatically merged the individual sentence graphs into a document graph, but the method of merging and its effects on summary content selection have not been independently evaluated.

graph construction Sentence

A Bag of Tricks for Dialogue Summarization

no code implementations EMNLP 2021 Muhammad Khalifa, Miguel Ballesteros, Kathleen McKeown

Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization.

Language Modelling Multi-Task Learning +1

Semantic Categorization of Social Knowledge for Commonsense Question Answering

1 code implementation EMNLP (sustainlp) 2021 Gengyu Wang, Xiaochen Hou, Diyi Yang, Kathleen McKeown, Jing Huang

Large pre-trained language models (PLMs) have led to great success on various commonsense question answering (QA) tasks in an end-to-end fashion.

Question Answering

Event-Centric Natural Language Processing

no code implementations ACL 2021 Muhao Chen, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown, Dan Roth

This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text.

Emotion-Infused Models for Explainable Psychological Stress Detection

1 code implementation NAACL 2021 Elsbeth Turcan, Smaranda Muresan, Kathleen McKeown

The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital.

Language Modelling Multi-Task Learning

Adversarial Learning for Zero-Shot Stance Detection on Social Media

1 code implementation NAACL 2021 Emily Allaway, Malavika Srikanth, Kathleen McKeown

Stance detection on social media can help to identify and understand slanted news or commentary in everyday life.

Zero-Shot Stance Detection

Segmenting Subtitles for Correcting ASR Segmentation Errors

no code implementations EACL 2021 David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuščáková, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown

Typical ASR systems segment the input audio into utterances using purely acoustic information, which may not resemble the sentence-like units that are expected by conventional machine translation (MT) systems for Spoken Language Translation.

Information Retrieval Machine Translation +4

Event Guided Denoising for Multilingual Relation Learning

no code implementations4 Dec 2020 Amith Ananthram, Emily Allaway, Kathleen McKeown

General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks.

Denoising Relation +1

Event-Guided Denoising for Multilingual Relation Learning

no code implementations COLING 2020 Amith Ananthram, Emily Allaway, Kathleen McKeown

General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks.

Denoising Relation +1

Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages

1 code implementation COLING 2020 Efsun Sarioglu Kayi, Linyong Nan, Bohan Qu, Mona Diab, Kathleen McKeown

We adopt cross-lingual embeddings constructed using different methods to extract features of the tweets, including a few state-of-the-art contextual embeddings such as BERT, RoBERTa and XLM-R. We train classifiers of different architectures on the extracted features.

Transfer Learning XLM-R

Incorporating Terminology Constraints in Automatic Post-Editing

1 code implementation WMT (EMNLP) 2020 David Wan, Chris Kedzie, Faisal Ladhak, Marine Carpuat, Kathleen McKeown

In this paper, we present both autoregressive and non-autoregressive models for lexically constrained APE, demonstrating that our approach enables preservation of 95% of the terminologies and also improves translation quality on English-German benchmarks.

Automatic Post-Editing Data Augmentation +1

A Unified Feature Representation for Lexical Connotations

no code implementations EACL 2021 Emily Allaway, Kathleen McKeown

Ideological attitudes and stance are often expressed through subtle meanings of words and phrases.

Stance Detection

Exploring Content Selection in Summarization of Novel Chapters

1 code implementation ACL 2020 Faisal Ladhak, Bryan Li, Yaser Al-Onaizan, Kathleen McKeown

We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides.

Extractive Summarization News Summarization

A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models

1 code implementation WS 2019 Chris Kedzie, Kathleen McKeown

Deep neural networks (DNN) are quickly becoming the de facto standard modeling method for many natural language generation (NLG) tasks.

Text Generation

Dreaddit: A Reddit Dataset for Stress Analysis in Social Media

1 code implementation WS 2019 Elsbeth Turcan, Kathleen McKeown

Stress is a nigh-universal human experience, particularly in the online world.

Automatically Inferring Gender Associations from Language

no code implementations IJCNLP 2019 Serina Chang, Kathleen McKeown

In this paper, we pose the question: do people talk about women and men in different ways?

Fine-grained Sentiment Analysis with Faithful Attention

no code implementations19 Aug 2019 Ruiqi Zhong, Steven Shao, Kathleen McKeown

While the general task of textual sentiment classification has been widely studied, much less research looks specifically at sentiment between a specified source and target.

Relation Extraction Sentiment Analysis +1

The ARIEL-CMU Systems for LoReHLT18

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

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

Machine Translation Translation

Content Selection in Deep Learning Models of Summarization

2 code implementations EMNLP 2018 Chris Kedzie, Kathleen McKeown, Hal Daume III

We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed.

Sentence

Predictive Embeddings for Hate Speech Detection on Twitter

no code implementations WS 2018 Rohan Kshirsagar, Tyus Cukuvac, Kathleen McKeown, Susan McGregor

We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular.

Hate Speech Detection Word Embeddings

Detecting Gang-Involved Escalation on Social Media Using Context

1 code implementation EMNLP 2018 Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William Frey, Chris Kedzie, Kathleen McKeown

Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online.

Multimodal Social Media Analysis for Gang Violence Prevention

no code implementations23 Jul 2018 Philipp Blandfort, Desmond Patton, William R. Frey, Svebor Karaman, Surabhi Bhargava, Fei-Tzin Lee, Siddharth Varia, Chris Kedzie, Michael B. Gaskell, Rossano Schifanella, Kathleen McKeown, Shih-Fu Chang

In this paper we partnered computer scientists with social work researchers, who have domain expertise in gang violence, to analyze how public tweets with images posted by youth who mention gang associations on Twitter can be leveraged to automatically detect psychosocial factors and conditions that could potentially assist social workers and violence outreach workers in prevention and early intervention programs.

General Classification

Domain-Adaptable Hybrid Generation of RDF Entity Descriptions

no code implementations IJCNLP 2017 Or Biran, Kathleen McKeown

RDF ontologies provide structured data on entities in many domains and continue to grow in size and diversity.

Domain Adaptation

Leveraging Sparse and Dense Feature Combinations for Sentiment Classification

no code implementations13 Aug 2017 Tao Yu, Christopher Hidey, Owen Rambow, Kathleen McKeown

This model outperforms many deep learning models and achieves comparable results to other deep learning models with complex architectures on sentiment analysis datasets.

BIG-bench Machine Learning Classification +3

SMARTies: Sentiment Models for Arabic Target Entities

no code implementations EACL 2017 Noura Farra, Kathleen McKeown

We consider entity-level sentiment analysis in Arabic, a morphologically rich language with increasing resources.

Sentiment Analysis

Automatically Processing Tweets from Gang-Involved Youth: Towards Detecting Loss and Aggression

no code implementations COLING 2016 Terra Blevins, Robert Kwiatkowski, Jamie MacBeth, Kathleen McKeown, Desmond Patton, Owen Rambow

Violence is a serious problems for cities like Chicago and has been exacerbated by the use of social media by gang-involved youths for taunting rival gangs.

Real-Time Web Scale Event Summarization Using Sequential Decision Making

no code implementations12 May 2016 Chris Kedzie, Fernando Diaz, Kathleen McKeown

We present a system based on sequential decision making for the online summarization of massive document streams, such as those found on the web.

Decision Making

Annotating Agreement and Disagreement in Threaded Discussion

no code implementations LREC 2012 Jacob Andreas, Sara Rosenthal, Kathleen McKeown

We introduce a new corpus of sentence-level agreement and disagreement annotations over LiveJournal and Wikipedia threads.

Sentence

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