Papers with MRF
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning (N18-2)
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| Challenge: | Existing methods to recognize textual entailment use a CCG parser to process sentences . failing to recognize the similar syntactic structure results in inconsistent argument structures . |
| Approach: | They propose to extend existing CCG parsers to parse sentences consistently . they use an inter-sentence modeling with Markov Random Fields to achieve this . |
| Outcome: | The proposed method improves on English and Japanese languages. |
Fake News Detection using Deep Markov Random Fields (N19-1)
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| Challenge: | Existing deep-learning-based methods ignore the correlations among news articles and only consider each article individually. |
| Approach: | They propose a graph-theoretic method that inherits the power of deep learning while utilizing the correlations among the articles. |
| Outcome: | The proposed model improves on state-of-the-art models on well-known datasets. |
Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines (2022.acl-long)
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Saadia Gabriel, Skyler Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi
| Challenge: | Empirical results confirm that it is indeed possible for neural models to predict the prominent patterns of readers’ reactions to previously unseen news headlines. |
| Approach: | They propose a pragmatic formalism for modeling how readers might react to a news headline . they propose 'misinfo' frames, which can be used to model reader perceptions of news reliability . |
| Outcome: | The proposed model can predict readers' reactions to previously unseen headlines. |
MRF-Chat: Improving Dialogue with Markov Random Fields (2021.emnlp-main)
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| Challenge: | Existing approaches to deep learning for open-domain dialogue include training end-to-end models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts and persona of the agent and the user, among others. |
| Approach: | They propose a probabilistic approach using Markov Random Fields to augment existing deep-learning methods for improved next utterance prediction. |
| Outcome: | The proposed approach significantly improves the performance of existing state-of-the-art retrieval models for open-domain conversational agents. |