Papers with Recall@1
Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting (2021.naacl-industry)
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| Challenge: | a challenge in building task-oriented dialogue systems is the limited amount of supervised training data available. |
| Approach: | They propose a method for training retrieval-based dialogue systems using annotated data and a larger, unlabeled dataset. |
| Outcome: | The proposed method improves model performance offline and online compared with no pretraining . the model is deployed in an agent-support application and evaluated on live customer service contacts . |
MERLIN: Multimodal Embedding Refinement via LLM-based Iterative Navigation for Text-Video Retrieval-Rerank Pipeline (2024.emnlp-industry)
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| Challenge: | Recent advances in text-video retrieval neglect the crucial user perspective, leading to discrepancies between user queries and content retrieved. |
| Approach: | They propose a novel, training-free pipeline that leverages Large Language Models for iterative feedback learning. |
| Outcome: | Experimental results show that MERLIN significantly outperforms existing systems in video retrieval. |
Hybrid-Vector Retrieval for Visually Rich Documents: Combining Single-Vector Efficiency and Multi-Vector Accuracy (2026.findings-acl)
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| Challenge: | Existing approaches to document retrieval are coarse and efficient, but expensive. |
| Approach: | a plug-and-play two-stage hybrid-vector framework is proposed to retrieve visually rich documents . HEAVEN efficiently retrieves candidate pages using a single-vektor method over VS-Pages . it also reranks candidates with a multi-vecctor method while filtering query tokens by linguistic importance . |
| Outcome: | HEAVEN achieves 99.87% of the Recall@1 performance of multi-vector models on average . it reduces per-query computation by 99.8%, achieving efficiency and accuracy . |
External Knowledge Acquisition for End-to-End Document-Oriented Dialog Systems (2023.eacl-main)
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| Challenge: | End-to-end neural models for conversational AI often assume that a response can be generated by considering only the knowledge acquired during training. |
| Approach: | They propose an architecture for document-oriented conversations with access to external knowledge sources. |
| Outcome: | The proposed architecture outperforms baseline models on the Wizard of Wikipedia dataset by 10.3% and 7.4%. |
Joint Semantic and Strategy Matching for Persuasive Dialogue (2023.findings-emnlp)
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Chuhao Jin, Yutao Zhu, Lingzhen Kong, Shijie Li, Xiao Zhang, Ruihua Song, Xu Chen, Huan Chen, Yuchong Sun, Yu Chen, Jun Xu
| Challenge: | Persuasive dialogue models rely on utterance semantic matching and a key aspect has been ignored . compared with utterrance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuation. |
| Approach: | They propose to model conversation semantics and strategies to match them using a BERT-like module and an auto-regressive predictor. |
| Outcome: | The proposed model improves state-of-the-art by 5% on a small and 37% on 'large' datasets. |
SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization (2025.findings-emnlp)
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| Challenge: | a recent study shows that code retrievers exhibit a strong bias towards well-documented code . |
| Approach: | They propose a framework that augments textual information with semantic information to mask specific features while preserving code functionality. |
| Outcome: | The proposed framework enhances textual information and reduces bias by augmenting code or structural knowledge with semantic information. |
Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval (2024.lrec-main)
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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
| Challenge: | Existing methods for video-text retrieval capture fine-grained semantic concepts . however, they lack the ability to capture finer-grain concepts such as objects and actions. |
| Approach: | They propose a dual-encoder architecture for fast video-text retrieval that learns lexicon representations to capture fine-grained semantics. |
| Outcome: | The proposed framework outperforms existing methods with 4.8% and 8.2% improvement on MSR-VTT and DiDeMo respectively. |