Papers with contextual language model
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues (D19-50)
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| Challenge: | a blurry line between fake news and protected-speech satire has been a struggle for social media platforms . purveyors of fake news have begun to masquerade as satirical sites to avoid being demoted . |
| Approach: | They propose to automatically classify fake news versus satire based on language differences . they hypothesize that nuances could be identified using semantic and linguistic cues . |
| Outcome: | The proposed method can identify nuances between fake news and satire based on language differences . the proposed method is compared to the language-based baseline and is highly scalable . |
The Context-Dependent Additive Recurrent Neural Net (N18-1)
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| Challenge: | Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP). |
| Approach: | They propose a new family of Recurrent Neural Networks that address contextual sequence mapping . they propose to use contextual signals to control the flow of information . |
| Outcome: | The proposed architecture outperforms existing methods on dialog problem and language model . the proposed architectures are based on a novel family of recurrent neural networks . |
He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation (2021.findings-acl)
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Aparna Garimella, Akhash Amarnath, Kiran Kumar, Akash Pramod Yalla, Anandhavelu N, Niyati Chhaya, Balaji Vasan Srinivasan
| Challenge: | Existing studies have focused on mitigating social biases in context-free representations, with recent shift to contextual ones. |
| Approach: | They propose an approach to mitigate social biases in a large pre-trained contextual language model . they propose lexical co-occurrence-based bias penalization in the decoder units . |
| Outcome: | The proposed approach reduces biases in fill-in-the-blank sentences and summarizes . it also reduces the biased representations in the frameworks, the authors show . |
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases (2021.emnlp-main)
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| Challenge: | Recent advances in knowledge base construction techniques focus on the acquisition of positive (true) KB statements, but negative (false) statements are important for discriminative reasoning. |
| Approach: | They propose a framework that ranks potential negatives in commonsense KBs using a contextual language model. |
| Outcome: | The proposed framework ranks negatives in commonsense KBs using a language model . it yields positives that are more grammatical, coherent, and informative . |