activity
20162022
most citedBLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

871 citations · 2.9k across the 42 of their papers we have counts for

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14 papers · 1 filter

cs.CL20217 cited

Cascaded Fast and Slow Models for Efficient Semantic Code Search

Akhilesh Deepak Gotmare, Junnan Li, Shafiq Joty +1

The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query. Existing approa…

cs.CL202125 cited

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Yue Wang, Weishi Wang, Shafiq Joty +1

Pre-trained models for Natural Languages (NL) like BERT and GPT have been recently shown to transfer well to Programming Languages (PL) and largely benefit a broad set of code-rela…

cs.CL20217 cited

Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning

Amrita Saha, Shafiq Joty, Steven C. H. Hoi

Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic fo…

cs.CL2020

Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition

Genta Indra Winata, Guangsen Wang, Caiming Xiong +1

One crucial challenge of real-world multilingual speech recognition is the long-tailed distribution problem, where some resource-rich languages like English have abundant training…

cs.CL2020

Improving Limited Labeled Dialogue State Tracking with Self-Supervision

Chien-Sheng Wu, Steven Hoi, Caiming Xiong

Existing dialogue state tracking (DST) models require plenty of labeled data. However, collecting high-quality labels is costly, especially when the number of domains increases. In…

cs.CL2020

Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading

Yifan Gao, Chien-Sheng Wu, Jingjing Li +5

Document interpretation and dialog understanding are the two major challenges for conversational machine reading. In this work, we propose Discern, a discourse-aware entailment rea…