Papers by Douglas Oard

5 papers
Cross-language Sentence Selection via Data Augmentation and Rationale Training (2021.acl-long)

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Challenge: a new approach to cross-language sentence selection is proposed for low-resource contexts . a cross-lingual embedding-based model is proposed that avoids translation entirely .
Approach: They propose a cross-lingual embedding-based query relevance model that uses data augmentation and negative sampling techniques to directly learn a query-sentence pair.
Outcome: The proposed approach performs better than state-of-the-art models on noisy parallel data . consistent improvements are seen across three language pairs over state- of-the art models .
Syntopical Graphs for Computational Argumentation Tasks (2021.acl-long)

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Challenge: adler and van Doren (1940) proposed a formalized manual process for understanding a topic based on multiple viewpoints.
Approach: They propose a syntopical reading process that emphasizes comparing and contrasting viewpoints to improve topic understanding.
Outcome: The proposed method outperforms approaches that do not use collection-level information.
A Joint Model for Document Segmentation and Segment Labeling (2020.acl-main)

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Challenge: Existing approaches to text segmentation focus on document segmentation and segment labeling separately.
Approach: They propose a method for jointly segmenting a document and labeling segments . they show that S-LSTM reduces segmentation error by 30% on average .
Outcome: The proposed method reduces segmentation error by 30% while improving segment labeling.
A Prioritization Model for Suicidality Risk Assessment (2020.acl-main)

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Challenge: Existing methods for predicting suicide have failed for fifty years . however, with the advent of machine learning, the problem is gaining momentum .
Approach: They propose a method that jointly ranks individuals and their social media posts to improve suicide risk assessment.
Outcome: The proposed approach outperforms existing methods in a case study using expert-annotated test collection.
Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization (2022.coling-1)

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Challenge: Query-focused summarization of foreign-language documents can help a user understand whether a document is relevant to a query term.
Approach: They propose to use machine translation and post-editing to improve human relevance judgments . they include a query term in a summary when its translation appears in the source document .
Outcome: The proposed approach improves human relevance judgments by including a query term in a summary when its translation appears in the source document.

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