Papers by Justus-Jonas Erker
Triple-Encoders: Representations That Fire Together, Wire Together (2024.acl-long)
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| Challenge: | Existing search-based dialog models re-encode the dialog history at every turn, causing high cost. |
| Approach: | They propose a method that encodes relative distances between utterances via a bi-encoder . they use a hebbian-inspired co-occurrence learning objective to compute distributed utterrance mixtures . |
| Outcome: | The proposed method improves on bi-encoded utterances and even better generalization . it can encode 2020 and Erker et al. (2023) with the same results. |
GRITHopper: Decomposition-Free Multi-Hop Dense Retrieval (2026.eacl-long)
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| Challenge: | Decomposition-based multi-hop retrieval methods rely on autoregressive steps to break down complex queries, which breaks end-to-end differentiability and is computationally expensive. |
| Approach: | They propose a multi-hop dense retrieval model that integrates causal language modeling with dense retrievals. |
| Outcome: | The proposed model outperforms existing methods on in-distribution and out-of-difference benchmarks. |
Imagination is All You Need! Curved Contrastive Learning for Abstract Sequence Modeling Utilized on Long Short-Term Dialogue Planning (2023.findings-acl)
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| Challenge: | Curved Contrastive Learning is a new approach for learning the relative turn distance between utterance pairs in multi-turn dialogues. |
| Approach: | They propose a technique for learning the relative turn distance between utterance pairs in multi-turn dialogues by projecting goal utterence and reply candidates into a latent space. |
| Outcome: | The proposed technique can guide transformers towards a goal in a zero-shot fashion . it can be used to estimate the likelihood of sequences by the entailment strength . |