Papers by Carolyn Rosé
Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network (2021.acl-long)
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| Challenge: | Knowledge graphs (KGs) are incomplete because of the large number of benchmark datasets that are not representative of real KGs. |
| Approach: | They develop a deep convolutional network that utilizes textual entity representations to distill the knowledge from the convolution into a student network that re-ranks promising candidate entities. |
| Outcome: | The proposed model outperforms recent methods in a realistic setting where dense connectivity is not guaranteed. |
A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)
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| Challenge: | Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs. |
| Approach: | They propose unsupervised and supervised methods to extract more informative representations from pre-trained language models to develop knowledge graph completion models. |
| Outcome: | The proposed model outperforms recent neural models in terms of performance and unsupervised processing methods. |
Linguistic representations for fewer-shot relation extraction across domains (2023.acl-long)
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| Challenge: | Recent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolds on performance in several NLP tasks. |
| Approach: | They extend previous work to examine whether linguistic representations enhance generalizability . they incorporate syntactic and semantic graphs from off-the-shelf tools into a transformer-based architecture . |
| Outcome: | The proposed approach enhances generalization by providing cross-domain pivots . it also shows that syntactic and semantic graphs exhibit roughly equivalent utility . |
Attentive Interaction Model: Modeling Changes in View in Argumentation (N18-1)
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| Challenge: | Prior work on argumentation in the NLP community has focused mainly on the first goal and has missed more nuanced and complex details of viewpoints. |
| Approach: | They propose a neural architecture that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument to predict if the argument succeeded in altering the OH' s view. |
| Outcome: | The proposed model outperforms several baselines on discussions on the Change My View forum on Reddit. |
Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research (2021.naacl-main)
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| Challenge: | Natural language processing research is often assumed to emerge naturally . many innovations go unapplied and important questions remain unstudied . |
| Approach: | They propose a new paradigm to structure and facilitate the processes by which basic and applied NLP research inform one another. |
| Outcome: | The proposed framework provides a roadmap for developing Translational NLP as a dedicated research area. |
Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance (2021.naacl-main)
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| Challenge: | Recent work on entity coreference resolution (CR) follows current trends in Deep Learning . traditional approaches do not make use of hierarchical representations of discourse structure . |
| Approach: | They propose to leverage automatically constructed discourse parse trees within a neural approach to generate anaphoric mentions. |
| Outcome: | The proposed model improves on two benchmark entity coreference-resolution datasets. |
Improving compositional generalization for multi-step quantitative reasoning in question answering (2022.emnlp-main)
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| Challenge: | Quantitative reasoning is an important aspect of question answering when numeric and verbal cues interact to indicate sophisticated, multi-step programs. |
| Approach: | They propose a method that encourages QA models to adjust attention patterns and capture input/output alignments that are meaningful to the reasoning task. |
| Outcome: | The proposed approach improves program accuracy and renders models more robust against overfitting as the number of reasoning steps grows. |
Using counterfactual contrast to improve compositional generalization for multi-step quantitative reasoning (2023.acl-long)
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| Challenge: | In quantitative question answering, compositional generalization is one of the main challenges of state of the art models. |
| Approach: | They propose a method that uses counterfactual scenarios to generate samples with compositional contrast. |
| Outcome: | The proposed method improves the performance of three state of the art models on four recently released datasets and also improves OOD performance on unseen domains and unsealed compositions. |
Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks (2022.tacl-1)
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| Challenge: | Natural language understanding (NLU) has made massive progress driven by large benchmarks, but a long tail of infrequent phenomena is underrepresented. |
| Approach: | They conceptualize the long tail using macro-level dimensions and perform a meta-analysis of 100 representative papers on transfer learning for NLU. |
| Outcome: | The results highlight avenues for future research in transfer learning for the long tail . authors suggest that the results may be useful for future studies . |