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20152023
most citedOff the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search

24 citations · 118 across the 25 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

cs.RO2022★ 1 cited

Knowledge-driven Scene Priors for Semantic Audio-Visual Embodied Navigation

Gyan Tatiya, Jonathan Francis, Luca Bondi +4

Generalisation to unseen contexts remains a challenge for embodied navigation agents. In the context of semantic audio-visual navigation (SAVi) tasks, the notion of generalisation…

cs.RO2022

Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving

Jonathan Francis, Bingqing Chen, Weiran Yao +2

The feasibility of collecting a large amount of expert demonstrations has inspired growing research interests in learning-to-drive settings, where models learn by imitating the dri…

cs.CL2022★ 1 cited

Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge

Kaixin Ma, Hao Cheng, Xiaodong Liu +2

We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources. The key novelty of our method is…

cs.CL2022★ 7 cited

Coalescing Global and Local Information for Procedural Text Understanding

Kaixin Ma, Filip Ilievski, Jonathan Francis +2

Procedural text understanding is a challenging language reasoning task that requires models to track entity states across the development of a narrative. A complete procedural unde…

cs.IR2022★ 5 cited

Positional Bias in Long-Document Ranking: Impact, Assessment, and Mitigation

Leonid Boytsov, David Akinpelu, Nipun Katyal +5

We tested over 20 Transformer models for ranking long documents (including recent LongP models trained with FlashAttention and RankGPT models "powered" by OpenAI and Anthropic clou…

cs.CL2022★ 1 cited

Table Retrieval May Not Necessitate Table-specific Model Design

Zhiruo Wang, Zhengbao Jiang, Eric Nyberg +1

Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent wo…