Papers by Kathy McKeown
A Robust Abstractive System for Cross-Lingual Summarization (N19-1)
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| Challenge: | We present a novel system for cross-lingual summarization that can be applied to low-resource languages. |
| Approach: | They propose a neural abstractive summarization system that can be applied to low-resource languages . they use machine translation and the New York Times summarizing corpus to create a corpus . |
| Outcome: | The proposed system achieves higher fluency than standard summarizers on translated documents . the proposed system can be easily applied to new low-resource languages . |
Neural Network Alignment for Sentential Paraphrases (P19-1)
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| Challenge: | a monolingual alignment system is ill-suited for word- or short phrase-based alignments. |
| Approach: | They propose a monolingual alignment system for long, sentence- or clause-level alignments . they show that systems designed for word- or short phrase-based alignment are ill-suited for longer alignments. |
| Outcome: | The proposed system outperforms state-of-the-art systems on long alignments . it achieves significantly higher recall on aligning phrases of four or more words . |
Detecting Gang-Involved Escalation on Social Media Using Context (D18-1)
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Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William Frey, Chris Kedzie, Kathy McKeown
| Challenge: | In cities such as Chicago, gang-involved youth have increasingly turned to social media to post about their experiences and intents online. |
| Approach: | They propose a system that uses domain-specific resources and contextual representations of the emotional and semantic content of the user’s recent tweets and their interactions with other users to detect Aggression and Loss in social media posts. |
| Outcome: | The proposed system improves on a large unlabeled dataset and incorporates contextual representations of the emotional and semantic content of the user’s recent tweets as well as their interactions with other users. |
Detecting and Reducing Bias in a High Stakes Domain (D19-1)
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| Challenge: | Existing research shows that a deep learning model can predict aggression and loss in posts by focusing on stop words such as “a” or “on”. |
| Approach: | They developed an approach to interpret a deep learning model that often bases its predictions on stop words such as "a" or "on" to tackle bias, they annotated the rationales and built models that drastically reduce bias. |
| Outcome: | The proposed model can predict aggression and loss in posts by using stop words such as "a" or "on" the new annotations enable us to quantitatively measure how justified the model predictions are, and build models that drastically reduce bias. |
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions (D19-1)
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| Challenge: | Argument mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text. |
| Approach: | They propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level models of argumentation. |
| Outcome: | The proposed model improves on existing models using pointer networks and a pre-trained language model. |
IMHO Fine-Tuning Improves Claim Detection (N19-1)
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| Challenge: | Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. |
| Approach: | They propose to fine-tune a language model using a Reddit corpus of opinionated claims and use the internet acronyms IMO/IMHO to identify claims. |
| Outcome: | The proposed approach improves state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points. |
Fixed That for You: Generating Contrastive Claims with Semantic Edits (N19-1)
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| Challenge: | Understanding contrastive opinions is a key component of argument generation. |
| Approach: | They create a corpus of Reddit comment pairs and train neural models to edit the original claim and produce a new claim with a different view. |
| Outcome: | The proposed model improves on a sequence-to-sequence baseline and compared to a human evaluation for fluency, coherence, and contrast. |
Dreaddit: A Reddit Dataset for Stress Analysis in Social Media (D19-62)
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| Challenge: | Existing computational studies on stress only focus on domains such as speech or Twitter . a corpus of social media text is used to identify stress . |
| Approach: | They propose a text corpus of lengthy social media data for detecting stress . they use 190K posts from five different categories of Reddit communities . |
| Outcome: | The proposed corpus of social media data can be used to identify stress . it includes 190K posts from five different categories of Reddit communities . |
Automatically Inferring Gender Associations from Language (D19-1)
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| Challenge: | In this paper, we demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. |
| Approach: | They propose to integrate two datasets and a novel approach to automatically infer gender associations from language and find coherent word clusters and label clusters for the semantic concepts they represent. |
| Outcome: | The proposed methods outperform strong baselines in large-scale studies of how people talk about women and men in two different settings. |