Search Results for author: Chris Kedzie

Found 16 papers, 5 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

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

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 +1

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

Subtitles to Segmentation: Improving Low-Resource Speech-to-Text Translation Pipelines

no code implementations19 Oct 2020 David Wan, Zhengping Jiang, Chris Kedzie, Elsbeth Turcan, Peter Bell, Kathleen McKeown

In this work, we focus on improving ASR output segmentation in the context of low-resource language speech-to-text translation.

Information Retrieval POS +3

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

Low-Level Linguistic Controls for Style Transfer and Content Preservation

1 code implementation8 Nov 2019 Katy Gero, Chris Kedzie, Jonathan Reeve, Lydia Chilton

Despite the success of style transfer in image processing, it has seen limited progress in natural language generation.

Style Transfer Text Generation

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.

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

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

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