Papers with response selection model

3 papers
BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)

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Challenge: Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios.
Approach: They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation.
Outcome: The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection.
Training Neural Response Selection for Task-Oriented Dialogue Systems (P19-1)

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Challenge: Despite their popularity, retrieval-based models have had modest impact on task-oriented dialogue systems . main obstacle to their application is the low-data regime of most task-orientated dialogue tasks . e-commerce, banking, and other domains are applications of retrieval models .
Approach: They propose a method which pretrains a retrieval-based model on large general-domain conversational corpora and fine-tunes it for the target dialogue domain.
Outcome: The proposed method is evaluated on five diverse domains, ranging from e-commerce to banking.
Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection Task (2022.emnlp-main)

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Challenge: Existing methods for synthesizing adversarial negative responses are limited by their scalability and cost.
Approach: They propose a method for generating adversarial negative responses using a large-scale language model.
Outcome: The proposed method outperforms other methods on dialogue selection tasks.

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