Papers with Incremental Transformer

3 papers
Incremental Transformer with Deliberation Decoder for Document Grounded Conversations (P19-1)

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Challenge: Existing dialogue systems do not exploit document knowledge effectively enough.
Approach: They propose a Transformer-based architecture for document grounded conversations that incorporates document knowledge into a two-pass decoder to improve context coherence and knowledge correctness.
Outcome: The proposed model outperforms baselines on context coherence and knowledge relevance on a real-world document grounded dataset.
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020.coling-main)

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Challenge: Existing models for ERTC use a few non-neutral categories to identify the emotion of each utterance.
Approach: They propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning to address these challenges by leveraging commonsense knowledge to leverage context.
Outcome: The proposed model outperforms state-of-the-art models across five benchmark datasets.
Incremental Transformer: Efficient Encoder for Incremented Text Over MRC and Conversation Tasks (2025.coling-main)

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Challenge: Existing encoders that encode incremented inputs have to re-encode the whole text to obtain the encoding of the extended input.
Approach: They propose an efficient encoder dedicated for faster encoding of incremented input . it takes only added input as input but attends to cached representations of original input a lower layer .
Outcome: The proposed encoder achieves 6.2x speedup over current encoders . it takes only added input as input but attends to cached representations of original input .

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