146 citations · 565 across the 30 of their papers we have counts for
6 papers · 1 filter
Dense Hierarchical Retrieval for Open-Domain Question Answering
Ye Liu, Kazuma Hashimoto, Yingbo Zhou +3
Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA), where latent representations of questions and passages are exploited for maximum…
Field Extraction from Forms with Unlabeled Data
Mingfei Gao, Zeyuan Chen, Nikhil Naik +3
We propose a novel framework to conduct field extraction from forms with unlabeled data. To bootstrap the training process, we develop a rule-based method for mining noisy pseudo-l…
RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering
Xi Ye, Semih Yavuz, Kazuma Hashimoto +2
Existing KBQA approaches, despite achieving strong performance on i.i.d. test data, often struggle in generalizing to questions involving unseen KB schema items. Prior ranking-base…
Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection
Jianguo Zhang, Kazuma Hashimoto, Yao Wan +4
Pre-trained Transformer-based models were reported to be robust in intent classification. In this work, we first point out the importance of in-domain out-of-scope detection in few…
Focused Attention Improves Document-Grounded Generation
Shrimai Prabhumoye, Kazuma Hashimoto, Yingbo Zhou +2
Document grounded generation is the task of using the information provided in a document to improve text generation. This work focuses on two different document grounded generation…
Causal-aware Safe Policy Improvement for Task-oriented dialogue
Govardana Sachithanandam Ramachandran, Kazuma Hashimoto, Caiming Xiong
The recent success of reinforcement learning's (RL) in solving complex tasks is most often attributed to its capacity to explore and exploit an environment where it has been traine…