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11 results on '"Breitfeller, Luke"'

1. Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction

Author

Yao, Hao-Ren, Breitfeller, Luke, Naik, Aakanksha, Zhou, Chunxiao, and Rose, Carolyn

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Event Temporal Relation Extraction (ETRE) is paramount but challenging. Within a discourse, event pairs are situated at different distances or the so-called proximity bands. The temporal ordering communicated about event pairs where at more remote (i.e., ``long'') or less remote (i.e., ``short'') proximity bands are encoded differently. SOTA models have tended to perform well on events situated at either short or long proximity bands, but not both. Nonetheless, real-world, natural texts contain all types of temporal event-pairs. In this paper, we present MulCo: Distilling Multi-Scale Knowledge via Contrastive Learning, a knowledge co-distillation approach that shares knowledge across multiple event pair proximity bands to improve performance on all types of temporal datasets. Our experimental results show that MulCo successfully integrates linguistic cues pertaining to temporal reasoning across both short and long proximity bands and achieves new state-of-the-art results on several ETRE benchmark datasets., Comment: Accepted to CIKM 2024 Full Research Track, camera ready version

Published
2022
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2. STAGE: Tool for Automated Extraction of Semantic Time Cues to Enrich Neural Temporal Ordering Models

Author

Breitfeller, Luke, Naik, Aakanksha, and Rose, Carolyn

Subjects
Computer Science - Computation and Language
Abstract

Despite achieving state-of-the-art accuracy on temporal ordering of events, neural models showcase significant gaps in performance. Our work seeks to fill one of these gaps by leveraging an under-explored dimension of textual semantics: rich semantic information provided by explicit textual time cues. We develop STAGE, a system that consists of a novel temporal framework and a parser that can automatically extract time cues and convert them into representations suitable for integration with neural models. We demonstrate the utility of extracted cues by integrating them with an event ordering model using a joint BiLSTM and ILP constraint architecture. We outline the functionality of the 3-part STAGE processing approach, and show two methods of integrating its representations with the BiLSTM-ILP model: (i) incorporating semantic cues as additional features, and (ii) generating new constraints from semantic cues to be enforced in the ILP. We demonstrate promising results on two event ordering datasets, and highlight important issues in semantic cue representation and integration for future research.

Published
2021

3. Time-Series Insights into the Process of Passing or Failing Online University Courses Using Neural-Induced Interpretable Student States

Author

Jeon, Byungsoo, Shafran, Eyal, Breitfeller, Luke, Levin, Jason, and Rosé, Carolyn P.

Abstract

This paper addresses a key challenge in Educational Data Mining, namely to model student behavioral trajectories in order to provide a means for identifying students most at risk, with the goal of providing supportive interventions. While many forms of data including clickstream data or data from sensors have been used extensively in time series models for such purposes, in this paper we explore the use of textual data, which is sometimes available in the records of students at large, online universities. We propose a time series model that constructs an evolving student state representation using both clickstream data and a signal extracted from the textual notes recorded by human mentors assigned to each student. We explore how the addition of this textual data improves both the predictive power of student states for the purpose of identifying students at risk for course failure as well as for providing interpretable insights about student course engagement processes. [For the full proceedings, see ED599096.]

Published
2019

4. Multi-Scale Contrastive Knowledge Co-Distillation for Event Temporal Relation Extraction

Author

Yao, Hao-Ren, Breitfeller, Luke, Naik, Aakanksha, Zhou, Chunxiao, Rose, Carolyn, Yao, Hao-Ren, Breitfeller, Luke, Naik, Aakanksha, Zhou, Chunxiao, and Rose, Carolyn

Abstract

Event Temporal Relation Extraction (ETRE) is a crucial yet challenging problem. Event pairs are situated within a discourse at different distances, which we refer to as proximity bands. The temporal ordering communicated about event pairs situated at more remote (i.e., ``long'') or less remote (i.e., ``short'') proximity bands is encoded differently. SOTA ETRE models have tended to perform well on events situated at either short or long proximity bands, but not both. Yet, real-world, natural texts contain all types of temporal event-pairs. In this paper, we present MulCo: Multi-Scale Contrastive Knowledge Co-Distillation, a fusion approach that shares knowledge across multiple event pair proximity bands in order to improve performance on all types of temporal datasets. Our experimental results show that MulCo successfully integrates linguistic cues pertaining to temporal reasoning across both short and long proximity bands and achieves new state-of-the-art results on several ETRE benchmark datasets., Comment: update

Published
2022

5. Multi-Scale Contrastive Co-Training for Event Temporal Relation Extraction

Author

Yao, Hao-Ren, Breitfeller, Luke, Naik, Aakanksha, Zhou, Chunxiao, and Rose, Carolyn

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computation and Language (cs.CL), Machine Learning (cs.LG)
Abstract

Extracting temporal relationships between pairs of events in texts is a crucial yet challenging problem for natural language understanding. Depending on the distance between the events, models must learn to differently balance information from local and global contexts surrounding the event pair for temporal relation prediction. Learning how to fuse this information has proved challenging for transformer-based language models. Therefore, we present MulCo: Multi-Scale Contrastive Co-Training, a technique for the better fusion of local and global contextualized features. Our model uses a BERT-based language model to encode local context and a Graph Neural Network (GNN) to represent global document-level syntactic and temporal characteristics. Unlike previous state-of-the-art methods, which use simple concatenation on multi-view features or select optimal sentences using sophisticated reinforcement learning approaches, our model co-trains GNN and BERT modules using a multi-scale contrastive learning objective. The GNN and BERT modules learn a synergistic parameterization by contrasting GNN multi-layer multi-hop subgraphs (i.e., global context embeddings) and BERT outputs (i.e., local context embeddings) through end-to-end back-propagation. We empirically demonstrate that MulCo provides improved ability to fuse local and global contexts encoded using BERT and GNN compared to the current state-of-the-art. Our experimental results show that MulCo achieves new state-of-the-art results on several temporal relation extraction datasets.

Published
2022
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10. CORA_Interview_questions_online_supp – Supplemental material for Factors Affecting Health Care Engagement of Patients With End-Stage Heart Failure: An Exploratory Survey Study

Author

Lohmueller, Lisa Carey, Aakanksha Naik, Breitfeller, Luke, McIlvennan, Colleen K., Manreet Kanwar, Murali, Srinivas, Rosé, Carolyn, and Antaki, James F.

Subjects
111799 Public Health and Health Services not elsewhere classified, FOS: Health sciences, 3. Good health
Abstract

Supplemental material, CORA_Interview_questions_online_supp for Factors Affecting Health Care Engagement of Patients With End-Stage Heart Failure: An Exploratory Survey Study by Lisa Carey Lohmueller, Aakanksha Naik, Luke Breitfeller, Colleen K. McIlvennan, Manreet Kanwar, Srinivas Murali, Carolyn Rosé and James F. Antaki in MDM Policy & Practice

11. CORA_Interview_questions_online_supp – Supplemental material for Factors Affecting Health Care Engagement of Patients With End-Stage Heart Failure: An Exploratory Survey Study

Author

Lohmueller, Lisa Carey, Aakanksha Naik, Breitfeller, Luke, McIlvennan, Colleen K., Manreet Kanwar, Murali, Srinivas, Rosé, Carolyn, and Antaki, James F.

Subjects
111799 Public Health and Health Services not elsewhere classified, FOS: Health sciences, 3. Good health
Abstract

Supplemental material, CORA_Interview_questions_online_supp for Factors Affecting Health Care Engagement of Patients With End-Stage Heart Failure: An Exploratory Survey Study by Lisa Carey Lohmueller, Aakanksha Naik, Luke Breitfeller, Colleen K. McIlvennan, Manreet Kanwar, Srinivas Murali, Carolyn Rosé and James F. Antaki in MDM Policy & Practice

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