Papers with attention-based
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos (N18-1)
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Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, Erik Cambria, Louis-Philippe Morency, Roger Zimmermann
| Challenge: | Existing methods for recognizing emotions in conversations ignore inter-speaker dependency relations . dyadic conversations are a form of dialogue between two entities . |
| Approach: | They propose a deep neural framework which leverages contextual information from the conversation history to model past utterances of each speaker into memories. |
| Outcome: | The proposed framework improves by 3 4% over the state-of-the-art in recognizing emotions in dyadic conversational videos. |
Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data (2020.acl-main)
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| Challenge: | Recent studies have shown that simpler, properly tuned models are at least competitive across NLP tasks. |
| Approach: | They propose to use a table-to-text and neural question generation tasks to generate text from structured and unstructured data. |
| Outcome: | The proposed task generates biographies based on Wikipedia infoboxes . the proposed model can achieve the state of the art in both tasks . |
Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation (2023.acl-long)
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| Challenge: | Neural machine translation models can unpredictably produce severely pathological translations, known as hallucinations, that seriously undermine user trust. |
| Approach: | They propose a fully unsupervised, plug-in detector that can be used with any attention-based NMT model. |
| Outcome: | The proposed detector outperforms existing models and is competitive with detectors that employ external models trained on millions of samples. |